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Publications

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== Books ==

Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., Norman, M. K. (2010). How Learning Works: 7 Research-Based Principles for Smart Teaching. Jossey-Bass: John Wiley & Sons, Inc.: San Francisco.

Juffs, A. & Rodríguez, G.A. (2014). Second Language Sentence Processing. Cognitive Science and Second Language Acqusition Series. Routledge/Taylor-Francis, Inc.

Juffs, A., Davis, B., McCormick, D., Mizera, G., O’Neill, C., Ranson, S., Slaathaug, M, & Smith, D. (2012). Vocabulary Building in English. University of Michigan Press. (2 volumes). ISBN 13: 9780472034215

Smith, D. & Brown, J. (2007). Active Listening, Second edition, Levels 1, 2 and 3. A listening comprehension textbook series with CD, for beginning to intermediate students of ESL. Cambridge University Press.

== Edited Books, Edited Journals, Edited Conference Proceedings ==

Aleven, V., Beal, C.R. & Graesser, A. (Eds.). (in press). Editors of Special Issue of the Journal of Educational Psychology: Advanced Learning Technologies.

Aleven, V., Kay, J. & Mostow, J. (Eds.). (2010). Proceedings of the 10th Intelligent Tutoring Systems Conference (ITS), Pittsburgh, PA.

Azvedo, R. & Aleven, V. (Eds.). (2013). Metacognition and Learning Technologies: An Overview of Current Interdisciplinary Research. International handbook of metacognition and learning technologies. Springer International Handbooks of Education: Vol. 26, 1-16. New York: Springer. doi:10.1007/978-1-4419-5546-3.

Baker, R.S.J.d. & Winne, P.H. (Eds.). (in press). Guest editors of special issue of the Journal of Educational Data Mining (JEDM): Educational Data Mining on Motivation, Meta-Cognition, and Self-Regulated Learning

Baker, R.S.J.d., Barnes, T. & Beck, J. (Eds.). (2008). Educational Data Mining 2008: 1st International Conference on Educational Data Mining, Proceedings. Montreal, Quebec, Canada. June 2008.

Carver, S.M. & Shrager, J. (Eds.). (2012). Journey from Child to Scientist: Integrating Cognitive Development and the Education Sciences. American Psychological Association (APA); 1st edition (March 15, 2012).

Gordon, G., Dunson, D. & Dudik, M. (Eds.).(2011). JMLR Workshop and Conference Proceedings Volume 15: AISTATS 2011. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, April 11-13, 2011, Fort Lauderdale, FL.

Kim, J. & Kumar, R. (Eds.).(in press). IJAIED Special issue on Intelligent Support for Learning in Groups (ISLG), Associate Editors.

Klatzky, R., MacWhinney, B. & Behrmann (Eds.). (2008). Embodiment, ego-space, and action. R. Klatzky, B. MacWhinney, & M. Behrmann, (Eds). Carnegie Mellon Symposia on Cognition. Psychology Press: Taylor & Francis Group.

Lane, H.C., Yacef, K., Mostow, J. & Pavlik, P. (Eds.). (2013). Proceedings of AIED 2013, LNAI 7926, 2013. Springer-Verlag Berlin Heidelberg.

McLaren, B. & Sosnovsky, S. (Eds.).(in press). IJAIED Special Issue on Landmark Learning Systmes and New Ideas and Developments in Mathematics and Science Learning. Special issue associate editors.

N. De Jong, K. Juffermans, M. Keijzer, & L. Rasier (Eds.). (2012). Proceedings of the Anéla Applied Linguistics Conference, May 2012, Lunteren. Proceedings Editors.

Pechenizkiy, M., Calders, T., Conati, C., Ventura, S. Romero, C., & Stamper, J. (Eds.).(2011). Proceedings of the 4th International Conference on Educational Data Mining, EDM 2011.

Pinkwart, N. & McLaren, B. (Eds.). (2012). Educational Technologies for Teaching Argumentation Skills, Bentham Science.

Resnick, L.B., Asterhan, C.A. & Clarke, S.N. (Eds.). (in press). Socializing Intelligence through Academic Talk and Dialogue. Washington, D.C.: American Educational Research Association.

Ryan, R.S.J.d., Merceron, A., & Pavlik, P. (Eds.). (2010). Proceedings of the 3rd International Conference on Educational Data Mining (EDM 2010).

Schmalhofer, F. & Perfetti, C. (Eds.). (2007). Higher level language processes in the brain: Inference and comprehension processes. Routledge: Psychology Press.

Schunn, C.D., Ashley, K.D. & Goldin, I.M. (Eds.). (2012). Redesigning educational peer review interactions using computer tools. Special issue of the Journal of Writing Research (JoWR), special issue guest editors.

Stamper, J, Gordon, G., et al (Eds.).(2010). 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010): KDD Cup 2010 Workshop Proceedings.

Suthers, D., Lund, K., Rosé, C. P., Teplovs, C., Law, N. & Dyke, G. (Eds.). (2013). Productive Multivocality in the Analysis of Group Interactions. Edited volume, Computer-Supported Collaborative Learning Series, Springer.

Yacef, K., Baker, R.S.J.D. & Beck, J.E. (Eds.). (2010). JEDM - Journal of Educational Data Mining (ISSN 2157-2100), Volume 2 (1), December 2010.

Yacef, K., Baker, R.S.J.D., Barnes, T. & Beck, J.E. (Eds.). (2009). JEDM - Journal of Educational Data Mining (ISSN 2157-2100), Volume 1 (1), October 2009.

Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M. & Stamper, J. (Eds.). (2012). Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012), Proceedings Editors.

== Book Chapters ==

Aleven, V. (2010). Rule-based Cognitive Modeling for Intelligent Tutoring Systems. In R. Nkambo, J. Bourdeau, & R. Mizoguchi (Eds.). Volume 308 of Studies in Computational Intelligence: Advances in Intelligent Tutoring Systems, 33-62. Springer.

Aleven, V., McLaren, B., & Koedinger, K.R. (2006). Towards computer-based tutoring of help-seeking skills. In S. Karabenick and R. Newman, (Eds.). Help seeking in academic settings: Goals, groups and contexts. Mahwah NJ: Erlbaum, 259-296.

Aleven, V., Roll, I., & Koedinger, K.R. (2012). Progress in assessment and tutoring of lifelong learning skills: An intelligent tutor agent that helps students become better help seekers. In P. J. Durlach, & A. M. Lesgold (Eds.). Adaptive technologies for training and education, 69-95. New York: Cambridge University Press.

Asterhan, C.S.C. (2013). Competitive and collaborative regulation of peer argumentation: Conceptualization and quantitative assessment. In M. Baker, J. Andriessen & S. Jarvela (Eds), Affective learning together.

Azvedo, R. & Aleven, V. (2013). Metacognition and Learning Technologies: An Overview of Current Interdisciplinary Research. International handbook of metacognition and learning technologies. Springer International Handbooks of Education: Vol. 26. New York: Springer. doi:10.1007/978-1-4419-5546-3. Page 1-16.

Baker, R.S.J.d. (2010). Data Mining for Education. In B. McGaw, P. Peterson, & E. Baker (Eds.). International Encyclopedia of Education (3rd edition), Vol. 7, 112-118. Oxford, UK: Elsevier.

Baker, R.S.J.d. (2010). Discovery with Models. In C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC.

Baker, R.S.J.d. (2010). Mining Data for Student Models. In R. Nkambo, J. Bourdeau, & R. Mizoguchi (Eds.) Volume 308 of Studies in Computational Intelligence: Advances in Intelligent Tutoring Systems, 323-338. Springer.

Baker, R.S.J.d. (2012). Guessing and Learning. In N.M. Seel (Ed.). Encyclopedia of the Sciences of Learning, 1397-1398. Springer. DOI 10.1007/978-1-4419-1428-6_23

Baker, R.S.J.d. (2012). Guessing Models. In N.M. Seel (Ed.). Encyclopedia of the Sciences of Learning, 1398-1399. Springer. DOI 10.1007/978-1-4419-1428-6_23

Baker, R.S.J.d. & Rossi, L.M. (2013). Assessing the Disengaged Behavior of Learners. In Sottilare, R., Graesser, A., Hu, X., & Holden, H. (Eds.). Design Recommendations for Intelligent Tutoring Systems, Volume 1 -- Learner Modeling. U.S. Army Research Lab, Orlando, FL, 155-166.

Baker, R.S.J.d., Corbett, A.T., Roll, I., Koedinger, K.R., Aleven, V., Cocea, M., Hershkovitz, A., de Carvalho, A.M.J.A., Mitrovic, A. & Mathews, M. (2013). Modeling and Studying Gaming the System with Educational Data Mining. In Azevedo, R., & Aleven, V. (Eds.). International Handbook of Metacognition and Learning Technologies. New York, NY: Springer, 97-116.

Bernacki, M. L., Nokes-Malach, T. J., & Aleven, V. (in press). Fine-grained assessment of motivation over long periods of learning with an intelligent tutoring system: Methodology, advantages, and preliminary results. In R. Azevedo and V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies. Berlin: Springer.

Chen, Z. & Klahr, D. (2008). Remote Transfer of Scientific Reasoning and Problem-Solving Strategies in Children. In R. V. Kail (Ed.) Advances in Child Development and Behavior, Vol. 36. (pp. 419 – 470) Amsterdam: Elsevier.

Chenoweth, N.A., Jones, C. & Tucker, G.R. (2006). Language online: Principles of design and methods of assessment. In R. P. Donaldson & M. A. Haggstrom (Eds.), Changing Language Education through CALL. New York, NY: Routledge, 147—167.

Chi M.T.H. (2008). Three types of conceptual change: Belief revision, mental model transformation, and categorical shift. In S. Vosniadou (Ed.), Handbook of research on conceptual change. Hillsdale, NJ: Erlbaum, 61-82.

Chi, M.T.H. (2006). Laboratory Methods for Assessing Experts’ and Novices’ Knowledge. (2006). In N. Charness, P. Feltovich, & R. Hoffman (Eds.), Cambridge Handbook of Expertise and Expert Performance. Cambridge University Press. p 167-184.

Chi, M.T.H. (2006). Two approaches to the study of experts’ characteristics. (2006). In N. Charness, P. Feltovich, & R. Hoffman (Eds.), Cambridge Handbook of Expertise and Expert Performance. Cambridge University Press. p 21-30.

Chi, M.T.H. & Ohlsson, S. (2005). Complex declarative learning. In:Holyoak, K.J. & Morrison, R.G. (Eds.) The Cambridge Handbook of Thinking and Reasoning, 371-399. Cambridge University Press.

Diziol, D. & Rummel, N. (2010). How to design support for collaborative e-learning. A framework of relevant dimensions. In B. Ertl (Ed.), E-collaborative knowledge construction: Learning from computer-supported and virtual environments, (pp. 162-179). Hershey, PA: IGI Global.

Dunbar, K. & Klahr, D. (2012). Scientific thinking and reasoning. In K. Holyoak & R.G. Morrison, (Eds.). Oxford Handbook of Thinking and Reasoning, 701-718. Oxford University Press.

Easterday, M.W. (2012). Policy world: A cognitive game for teaching deliberation. In N. Pinkwart & B. McLaren (Eds.), Educational technologies for teaching argumentation skills. Oak Park, IL: Bentham Science Publishers.

Eskenazi, M. & Brown, J. (2006). Teaching the creation of software that uses speech recognition. In P. Hubbard and M. Levy, (Eds.), Teacher Education in CALL. John Benjamins Publishing, 135-151.

Eskenazi, M. & Juffs, A. (2013). Information Retrieval for Reading Tutors. In C. Chapelle, (Ed.), The Encyclopedia of Applied Linguistics. New York: Cambridge University Press. DOI: 10.1002/9781405198431.wbeal0536

Forbes-Riley, K. & Litman, D.J. (2008). Analyzing Dependencies Between Student Certainness States and Tutor Responses in a Spoken Dialogue Corpus. In L. Dybkjaer and W. Minker (Eds.), Text, Speech and Language Technology: Recent Trends in Discourse and Dialogue, Vol. 39, 275-304. Springer Netherlands.

Frishkoff, G., White, G. & Perfetti, C. (2009). "In vivo" testing of learning and instructional principles: The design and implementation of school-based experimentation. In L. Dinella (Ed.), Conducting Science-Based Psychology Research in Schools. Washington, D.C.: APA Books, 153-173.

Glennan, T.K. Jr. & Resnick, L. B. (2004). School Districts as Learning Organizations: A Strategy for Scaling Education Reform. In T.K. Glennan, Jr., S.J. Bodilly, J. Galegher, and K. Kerr, (Eds.). Expanding the Reach of Education Reforms: Collected Essays by Leaders in the Scale-up of Educational Interventions. Santa Monica, CA: RAND, MG-177-FF.

Howley, I. Mayfield, E. & Rosé, C. (2012). Linguistic Analysis Methods for Studying Small Groups. Invited in C. Hmelo-Silver, A. O.Donnell, C. Chan, & C. Chin (Eds.). International Handbook of Collaborative Learning. Taylor and Francis, Inc.

Juffs, A. (2009). The Second language acquisition of the lexicon. In W. Ritchie and T. Bhatia, (Eds.), The New handbook of second language acquisition, 2nd edition, 181-209. Amsterdam, The Netherlands: Elsevier.

Juffs, A. (2010). Formal linguistic perspectives on second language acquisition. In R. Kaplan (Ed.), The Oxford Handbook of Applied Linguistics, 143-162. (Second, revised ed.). New York: Oxford University Press.

Junker, B. (2010). The role of psychometric methods in EDM. In C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC.

Klahr, D. (2007). Evolution of Scientific Thinking: Comments on Geary’s “Educating the Evolved Mind” In Carlson, J. & Levin, J. (Eds.) Psychological Perspectives on Contemporary Educational Issues. Greenwich, CT. Information Age Publishing.

Klahr, D. (2012). Patterns, Rules, & Discoveries in Life and in Science. In Carver, S., & Shrager, J..(Eds.) The Journey From Child to Scientist: Integrating Cognitive Development and the Education Sciences. Washington DC: American Psychological Association.

Klahr, D. (2012). Beyond Piaget: a Perspective from Studies of Children’s Problem Solving Abilities. In A. Slater & P. Quinn (Eds.) Refreshing Developmental Psychology: Beyond the Classic Studies. London: Sage Publications.

Klahr, D. (2012). Beyond Piaget: a Perspective from Studies of Children’s Problem Solving Abilities. In A. Slater & P. Quinn (Eds.) Refreshing Developmental Psychology: Beyond the Classic Studies. London: Sage Publications.

Klahr, D., Matlen, B., & Jirout, J. (2012). Children as Scientific Thinkers. In Feist. G. J. & Gorman, M. E. (Eds.) Handbook of the Psychology of Science. Springer.

Koedinger, K.R. & Corbett, A. (2006). Cognitive Tutors: Technology bringing learning science to the classroom. In K. Sawyer (Ed.) The Cambridge Handbook of the Learning Sciences, (pp. 61-78). Cambridge University Press.

Koedinger, K.R. & Roll, I. (2012). Learning to think: Cognitive mechanisms of knowledge transfer. In K. J. Holyoak, & R. G. Morrison (Eds.), The Oxford handbook of thinking and reasoning (2nd ed.). New York: Cambridge University Press.

Koedinger, K.R., Aleven, V., Roll, I. & Baker, R.S.J.d. (2009). In vivo experiments on whether supporting metacognition in intelligent tutoring systems yields robust learning. In D.J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of Metacognition in Education. New York: Routledge Taylor & Francis Group, 383-412.

Koedinger, K.R., Baker, R.S.J.d., Cunningham, K., Skogsholm, A., Leber, B. & Stamper, J. (2010). A Data Repository for the EDM community: The PSLC DataShop. In C. Romero, S. Ventura, M. Pechenizkiy, R.S.J.d. Baker (Eds.). Handbook of Educational Data Mining. Boca Raton, FL: CRC Press, 43-56.

Lynch, C., Ashley, K.D., Pinkwart, N. & Aleven, V. (2012). Ill-defined domains and adaptive tutoring technologies. In P. Durlach & A.M. Lesgold (Eds.). Adaptive Technologies for Training and Education. Cambridge University Press.

MacWhinney, B. (2005). Emergent Fossilization. Studies of Fossilization in Second Language Acquisition. Z. Han and T. Odlin (Eds.). Clevedon, UK: Multilingual Matters. 2005. p 134-156.

MacWhinney, B. (2005). A Unified Model of Language Acquisition. In J.F. Kroll & A.M.B.de Groot (Eds.). Handbook of bilingualism: Psycholinguistic approaches. 2004. p 49-67. New York: Oxford University Press.

MacWhinney, B. (2008). How Mental Models Encode Embodied Linguistic Perspectives. In Klatzky, R., MacWhinney, B., and Behrmann, M. (Eds.). Embodiment, Ego-Space, and Action, 365-405. Carnegie Mellon Symposia on Cognition. Psychology Press: Taylor & Francis Group.

MacWhinney, B. (2011). Item-based patterns in early syntactic development. In T. Herbst (Ed.). Valency relations. Berlin, Springer.

MacWhinney, B. (2011). The expanding horizons of corpus linguistics. In J. Newman, H. Baayen & S. Rice (Eds.). Corpus-based studies in language use, language learning, and language documentation. Amsterdam, Rodopi: 177-212.

MacWhinney, B. (2012). The logic of the Unified Model. The Routledge Handbook of Second Language Acquisition. S. Gass and A. Mackey. New York, Routledge: 211-227.

Masnick, A., Klahr, D. & Morris, B.J. (2007). Separating signal from noise: Children's understanding of error and variability in experimental outcomes. In M. Lovett & P. Shaw, P. (Eds) Thinking With Data. Mawah, NJ: Erlbaum.

Mayfield, E. & Rosé, C. P. (2013). LightSIDE: Open Source Machine Learning for Text Accessible to Non-Experts. Invited chapter in M.D. Shermis & J. Burstein (Eds.). Handbook of Automated Essay Evaluation: Current Applications and New Directions, Routledge, 124-135.

Mitsugi, S. & MacWhinney, B. (2010). Second language processing in Japanese scrambled sentences. In B. VanPatten & J. Jegerski (Eds.). Research in Second Language Processing and Parsing. New York, John Benjamins: 159-176.

Mostow, J., Beck, J., Cuneo, A., Gouvea, E., Heiner, C. & Juarez, O. (2010). Project LISTEN's session browser. In C Romero, S. Ventura, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Boca Raton, FL: CRC Press.

Murray, C. & VanLehn, K. (2005). Effects of dissuading unnecessary help requests while providing proactive help. In G. McCalla, C. K. Looi, B. Bredeweg & J. Breuker (Eds.), Artificial Intelligence in Education (pp. 887-889). Amsterdam, Netherlands: IOS Press.

Nokes, T. J., & Belenky, D. M. (2011). Incorporating motivation into a theoretical framework for knowledge transfer. In J. P. Mestre and B. H. Ross (Eds.), Cognition and Education: The Psychology of Learning and Motivation: Advances in Research and Theory. Volume 55 (pp. 109-135). San Diego, CA: Academic Press. doi: 10.1016/B978-0-12-387691-1.00004-1

Nokes, T.J., Schunn, C. & Chi, M.T.H. (2010). Problem solving and human expertise. In P. Peterson, E. Baker & B. McGaw (Eds.). International Encyclopedia of Education, 3rd Edition, Vol. 5, 265-272Oxford, UK: Elsevier.

Pavlik (2013). Spacing Effects. Encyclopedia of the Mind. SAGE Publications Ltd.

Pavlik, P. (2007). Timing is in order: Modeling order effects in the learning of information. In F. E. Ritter, S., J. Nerb, E. Lehtinen & T. O'Shea (Eds.), In order to learn: How order effects in machine learning illuminate human learning. New York: Oxford University Press.

Perfetti, C. & Dunlap, S. (2008). Learning to read: General principles and writing system variations. In K. Koda & A. Zehler (Eds.). Learning to read across languages (13-38). Mahwah, NJ: Erlbaum.

Perfetti, C. & Frishkoff, G. (2008). Neural bases of text and discourse processing. In B. Stemmer and H.A. Whitaker (Eds.), Handbook of neuroscience of language (pp. 165-174). Cambridge, MA: Elsevier.

Perfetti, C. & Liu, Y. (2006). Reading Chinese characters: Orthography, phonology, meaning, and the Lexical Constituency Model. In P. Li, L. H. Tan, E. Bates, & O. J. L. Tzeng (Eds.), Handbook of East Asian psycholinguistics (pp. 225-236). New York: Cambridge University Press. 225-236.

Perfetti, C., Landi, N. & Oakhill, J. (2005). The acquisition of reading comprehension skill. In M. J. Snowling & C. Hulme (Eds.), The science of reading: A handbook (pp. 227-247). Oxford: Blackwell.

Perfetti, C., Liu, Y., Fiez, J.A. & Tan, L. (2010). The neural bases of reading: Universals and Writing System Variations. In P. Cornelissen, M. Kringelbach, & P. Hansen (Eds.), The neural basis of reading, 147-172. Oxford University Press.

Presson, E., Davy, C. & MacWhinney, B. (2013). Experimentalized CALL for adult second language learners. In J. Schwieter (Ed.), Innovative research and practices in second language acquisition and bilingualism (pp. 139-164): John Benjamins.

Presson, N. & MacWhinney, B. (2011). The Competition Model and language disorders. In J. Guendozi, F. Loncke & M. Williams (Eds.). Handbook of psycholinguistic and cognitive processes. New York, Psychology Press: 31-48.

Presson, N., Davy, C. et al. (2012). Experimentalized CALL for adult second language learners. In J. Schwelter (Ed.). Handbook of Second Language Instruction. New York, Wiley.

Razzaq, L., Feng, M., Heffernan, N., Koedinger, K.R., Junker, B., Nuzzo-Jones, G., Macasek, M.A., Rasmussen, K.P., Turner, T.E. & Walonoski, J.A. (2007). A Web-based authoring tool for intelligent tutors: Assessment and instructional assistance. In N. Nedjah, et al. (Eds.). Intelligent Educational Machines. Intelligent Systems Engineering Book Series. Springer, 23-49.

Reed, S. (2008). Manipulating multimedia materials. In Robert Zheng (Ed), Cognitive Effects of Multimedia Learning (51-66). Hershey, PA: IGI Global, Inc.

Renkl, A. & Atkinson, R.K. (2007). Cognitive skill acquisition: Ordering instructional events in example-based learning. In F. E. Ritter, J. Nerb, E. Lehtinen, and T. O’Shea (Eds.), In order to learn: How ordering effect in machine learning illuminate human learning and vice versa. Oxford, UK: Oxford University Press.

Renkl, A., Hilbert, T., Schworm, S. & Reiss, K. (2007). Cognitive skill acquisition from complex examples: A Taxonomy of examples and tentative instructional guidelines. In M. Prenzel (Ed.), Studies on the educational quality of schools, 239-249. Münster, Germany: Waxmann

Resnick, L. (2007). Giving Psychology Away: From Individual Learning to Learning Organizations. In Jing, Q. (Ed.), Progress in Psychological Science around the World, Proceedings of the 28th International Congress of Psychology, Vol. 2, Social and Applied Issues. ISBN: 1841699624.

Resnick, L. & Spillane, J. P. (2006). From individual learning to organizational designs for learning. In L. Verschaffel, F. Dochy, M. Boekaerts, & S. Vosniadou, (Eds). Instructional psychology: Past, present and future trends. Sixteen essays in honor of Erik De Corte (Advances in Learning and Instruction Series). Oxford: Pergamon

Resnick, L. & Rosé, C. P. (in press). Classroom Language. Invited chapter in the Handbook of Educational Psychology on Classroom Teaching.

Resnick, L., Lesgold, A. & Hall, M.W. (2005). Technology and the new culture of learning: Tools for education professionals. In P. Gardenfors & P. Johansson (Eds.), Cognition, education, and communication technology (pp. 77-107). Mahwah, NJ: Erlbaum.

Resnick, Michaels, O'Connor (2010). How (well structured) talk builds the mind. In D. Preiss & R. Sternberg (Eds.), Innovations in Educational Psychology: Perspectives on Learning, Teaching, and Human Development. New York: Springer, 163-194.

Ritter, S., Haverty, L., Koedinger, K.R., Hadley, W. & Corbett, A. (2008). Integrating intelligent software tutors with the mathematics classroom. In G. Blume and K. Heid (Eds.), Research on Technology and the Teaching and Learning of Mathematics: Vol. 2 Cases and Perspectives. Charlotte, NC: IAP.

Rodrigo, M.M.T. & Baker, R.S.J.d. (2011). Comparing the Incidence and Persistence of Learners' Affect During Interactions with Different Educational Software Packages. Calvo, R.A., & D'Mello, S. (Eds.) New Perspectives on Affect and Learning Technologies, pp. 183-202. New York, NY: Springer.

Rodrigo, M.M.T. & Baker, R.S.J.d. (2011). Comparing the Incidence and Persistence of Learners' Affect During Interactions with Different Educational Software Packages. In R.A. Calvo & S. D'Mello (Eds.). New Perspectives on Affect and Learning Technologies. New York, NY: Springer.

Romero, C., Ventura, M., Viola, R., Pechenizkiy, R. & Baker, R.S.J.d. (2010). Conclusions and future trends. In C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC.

Romero, C., Ventura, M., Viola, R., Pechenizkiy, R. & Baker, R.S.J.d. (2010). Introduction to EDM. In C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC.

Rosé, C. P. (2012). Assessing Socio-Emotional Learning Around Technology. In R. Luckin, J. Underwood, N. Winters, P. Goodyear, B. Grabowski & S. Puntambekar, S. (Eds.).Handbook of Educational Technology, Taylor & Francis.

Rosé, C. P. & Tovares, A. (in press). What Sociolinguistics and Machine Learning Have to Say to One Another about Interaction Analysis. In L. Resnick, C. Asterhan & S. Clarke (Eds.). Socializing Intelligence Through Academic Talk and Dialogue, Washington, DC: American Educational Research Association.

Roy, M. & Chi, M.T.H. (2005). The self-explanation principle in multi-media learning. In R. Mayer (Ed.), Cambridge Handbook of Multimedia Learning (Pp. 271-286). Cambridge Press. 271-286.

Shih, B. (2010). A Response time model for bottom-out hints as worked examples. In C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC.

Siler, S. A., & Klahr, D. (2012). Detecting, Classifying and Remediating Children’s Explicit and Implicit Misconceptions about Experimental Design. In Proctor, R. W., & Capaldi, E. J. (Eds.), Psychology of Science: Implicit and Explicit Reasoning. New York: Oxford University Press.

Siler, S. A., Klahr, D. & Matlen, B. (2013). Conceptual Change When Learning Experimental Design. In S. Vosniadau (Ed). Handbook of Research on Conceptual Change, 2nd Edition. Routledge, 138-158.

Singh, A.P. & Gordon, G. (2008). A unified view of matrix factorization models. In R. Goebel, J. Siekmann, and W. Wahlster (Eds). Machine Learning and Knowledge Discovery in Databases (Proc. ECML PKDD), volume 5212/2008 of Lecture Notes in Computer Science, pages 358-373. Springer Berlin / Heidelberg, 2008.

Sionti, M., Ai, H., Rosé, C.P. & Resnick, L. (2012). A Framework for Analyzing Development of Argumentation through Classroom Discussions. In N. Pinkwart & B. McLaren (Eds.). Educational Technologies for Teaching Argumentation Skills. Bentham Science.

Sohmer, R., Michaels, S., O'Connor C. & Resnick, L. (2009). Guided construction of knowledge in the classroom: Teacher talk, task, and tools. In B. Schwarz, T. Dreyfus & R. Hershkowitz, (Eds.), Transformation of Knowledge Through Classroom Instruction, 105-129. London: Elsevier.

Stahl, G. & Rosé, C. P. (2011). Group Cognition in Online Teams. In E. Salas, S. Fiore & M. Letsky (Eds.). Theories of Team Cognition: Cross-Disciplinary Perspectives, Section V: Social Psychology and Communication Perspectives, American Psychological Society.

Tchounikine, P., Rummel, N., & McLaren, B. (2010). Computer Supported Collaborative Learning and Intelligent Tutoring Systems. In R. Nkambo, J. Bourdeau, & R. Mizoguchi (Eds.) Volume 308 of Studies in Computational Intelligence: Advances in Intelligent Tutoring Systems, 447-484. Springer.

Tokowicz, N. & Perfetti, C. (2005). Introduction to section II: Comprehension. In J. F. Kroll & A. M. B. de Groot (Eds.), Handbook of bilingualism: Psycholinguistic approaches (pp. 173-177). New York: Oxford University Press. p 173-178.

VanLehn, K. (2007). Getting out of order: Avoiding lesson effects through instruction. In F. E. Ritter, J. Nerb, T. O'Shea, & E. Lehtinen (Eds.), In order to learn: How the sequences of topics affect learning. Oxford University Press, 169-180.

VanLehn, K. (2008). Intelligent tutoring systems for continuous, embedded assessment. In C. A. Dwyer (Ed.), The future of assessment: Shaping teaching and learning. Mahwah, NJ: Erbaum.

VanLehn, K. & Chi M. (2012). Adaptive expertise as acceleration of future learning: A case study. In P. J. Durlach & A. M. Lesgold (Eds.) Adaptive Technologies for Training and Education, 28-45. Cambridge, UK: Cambridge University Press.

VanLehn, K. & van de Sande, B. (2009). Acquiring Conceptual Expertise from Modeling: The Case of Elementary Physics. In K. A. Ericsson (Ed.) The Development of Professional Performance: Approaches to Objective Measurement and Design of Optimal Learning Environments.

VanLehn, K., van de Sande, B., Shelby, R. & Gershman, S. (2010). The Andes Physics Tutoring System: An Experiment in Freedom. In R. Nkambo, J. Bourdeau, & R. Mizoguchi (Eds.) Volume 308 of Studies in Computational Intelligence: Advances in Intelligent Tutoring Systems, 421-446. Springer.

Wang, Z. (in press). On-line time pressure manipulations: L2 speaking performance under five types of planning and repetition conditions. In P. Skehan (Ed.). Investigating a processing perspective on task performance. Amsterdam: John Benjamins.

Wang, Z., & Skehan, P. (in press). Structure, Lexis, and time perspective: Influences on task performance. In P. Skehan (Ed.). Investigating a processing perspective on task performance. Amsterdam: John Benjamins.

White, G., Frishkoff, G., & Bullock, M. (2008). Bridging the gap between psychological science and educational policy and practice. In K. T. C. Fiorello. (Ed.), Cognitive development in K-3 classroom learning: Research applications (227-263). Mahwah, NJ: Lawrence Erlbaum Associates

== Journal Articles ==

Adams, D., McLaren, B.M., Durkin, K., Mayer, R.E., Rittle-Johnson, B., Isotani, S. & van Velsen, M. (2014). Using erroneous examples to improve mathematics learning with a web-based tutoring system. Computers in Human Behavior, 36: 401-411.

Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (in press). Towards Adapting Dynamic Collaborative Support to Student Ability Level. International Journal of AI in Education, special issue on Intelligent Support for Group Learning.

Aleven, V., McLaren, B., Roll, I. & Koedinger, K.R. (2006). Toward meta-cognitive tutoring: A Model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence in Education, 16, 101-128.

Aleven, V., McLaren, B., Sewall, J. (2009). Scaling up programming by demonstration for intelligent tutoring systems development: An open-access web site for middle school mathematics learning. IEEE Transactions on Learning Technologies, Special Issue on Real-World Applications of Intelligent Tutoring Systems, 2(2), 64-78.

Aleven, V., McLaren, B., Sewall, J. & Koedinger, K.R. (2009). A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors. International Journal of Artificial Intelligence in Education (IJAIED). Special Issue on "Authoring Systems for Intelligent Tutoring Systems." 19(2), 105-154.

Aleven, V., McLaren, B.M., Sewall, J., & Koedinger, K.R., K. (2009). Example-Tracing Tutors: A New Paradigm for Intelligent Tutoring Systems. International Journal of Artificial Intelligence in Education (IJAIED), Special Issue on "Authoring Systems for Intelligent Tutoring Systems”, 105-154.

Aleven, V., Roll, I., McLaren, B., Koedinger, K.R. (2010). Automated, Unobtrusive, Action-by-Action Assessment of Self-Regulation During Learning with an intelligent tutoring system. Educational Psychologist, 45(4), 224-233.
Alfieri, L., Nokes-Malach, T. J., & Schunn, C. D. (in press). Learning through case comparisons: A meta-analytic review. Educational Psychologist.

Anthony, L., Yang, J. & Koedinger, K.R. (2012). A paradigm for handwriting-based intelligent tutors, International Journal of Human-Computer Studies, November 2012, 866-887.

Asterhan C.S.C. & Schwarz, B.B (2009). The role of argumentation and explanation in conceptual change: Indications from protocol analyses of peer-to-peer dialogue. Cognitive Science, 33, 373-399.

Baker, R.S.J.d. & Winne, P. (2013). The Potentials of Educational Data Mining for Researching Metacognition, Motivation and Self-Regulated Learning. Journal of Educational Data Mining Special Issue on Motivation, Meta-cognition, and Self-regulated Learning, JEDM, 5(1).

Baker, R.S.J.d. & Yacef, K. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions, Journal of Educational Data Mining, 1(1), 3-17.

Baker, R.S.J.d., Corbett, A., Roll, I. & Koedinger, K.R. (2008). Developing a Generalizable Detector of When Students Game the System. User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI), 18(3), 287-314.

Baker, R.S.J.d., D'Mello, Rodrigo, M.T. & Graesser, A. (2010). Better to Be Frustrated than Bored: The Incidence, Persistence, and Impact of Learners’ Affect during Interactions with Three Different Computer-Based Learning Environments. International Journal of Human-Computer Studies, 68(4), 223-241.

Baker, R.S.J.d., Goldstein, A.B. & Heffernan, N.T. (2011). Detecting Learning Moment-by-Moment. International Journal of Artificial Intelligence in Education, Special issue on Best of ITS 2010, 21 (1-2), 5-25. http://dx.doi.org/10.3233/JAI-2011-015

Baker, R.S.J.d., Goldstein, A.B. & Heffernan, N.T. (2011). Detecting Learning Moment-by-Moment. International Journal of Artificial Intelligence in Education, 21 (1-2), 5-25.

Baker, R.S.J.d., Walonoski, J.A., Heffernan, N., Roll, I., Corbett, A. & Koedinger, K.R. (2008). Why Students Engage in "Gaming the System" Behavior in Interactive Learning Environments. Journal of Interactive Learning Research, 19(2), 185-224.

Balass, M., Nelson, J. R., & Perfetti, C. A. (2010). Word learning: An ERP investigation of word experience effects on recognition and word processing. Contemporary Educational Psychology, 35(2), 126-140.

Belenky, D. M., & Nokes-Malach, T. J. (2012). Motivation and transfer: The role of mastery-approach goals in preparation for future learning. Journal of the Learning Sciences, 21(3), 399-432. doi: 10.1080/10508406.2011.651232

Belenky, D.M. & Nokes, T.J. (2009). Examining the role of manipulatives and metacognition on engagement, learning, and transfer. Journal of Problem Solving, 2 (2), 102-129.

Ben-Yehudah, G., Guediche, S. &. Fiez, J.A. (2007). Cerebellar contributions to verbal working memory: Beyond cognitive theory. The Cerebellum, 63:193-201.

Blessing, S. B., Gilbert, S.G., Oureda, S. & Ritter, S. (2009). Authoring model-tracing cognitive tutors. International Journal of AI in Education, 19(2), 189-210.

Bolger, D.J., Balass, M., Landen, E. & Perfetti, C. (2008). Contextual variation and definitions in learning the meaning of words. Discourse Processes, 45(2), 122-159.

Bolger, D.J., Perfetti, C. & Schneider, W. (2005). A cross-cultural effect on the brain revisited: Universal structures plus writing system variation. Human Brain Mapping, Vol 25(1), 92-104.

Booth, J. & Siegler, R. (2008). Numerical magnitude representations influence arithmetic learning. Child Development, 79, 1016-1031.

Booth, J.L., & Koedinger, K.R. (2012). Are diagrams always helpful tools? Developmental and individual differences in the effect of presentation format on student problem solving. British Journal of Educational Psychology, 82, 492–511.

Butcher, K. (2006). Learning From Text With Diagrams: Promoting Mental Model Development and Inference Generation. Journal of Educational Psychology, 98(1), 182-197.

Butcher, K. (in press). Using student interactions to foster rule-diagram mapping during problem solving in an intelligent tutoring system. Journal of Educational Psychology.

Chang, K.M., Nelson, J., Pant, U. & Mostow, J. (2013). Toward Exploiting EEG Input in a Reading Tutor. International Journal of Artificial Intelligence in Education 22 (1, Special "Best of AIED2011" Issue), 29-41.

Chen, B., Zhou, H.X., Dunlap, S. & Perfetti, C. (2007). Age of acquisition effects in reading Chinese: Evidence in favour of the arbitrary mapping hypothesis. British Journal of Psychololgy, Vol 98(3): 499-516.

Cheng, C., Wang, M., & Perfetti, C. A. (2011). Acquisition of compound words in Chinese-English bilingual children. Applied Psycholinguistics [Special issue], Vol. 32:3, 583-600.

Chi M.T.H. (2004). Can Tutors Monitor Students’ Understanding Accurately?. Cognition and Instruction. Vol 22, No 3.. Pages 363-387.

Chi, M. & VanLehn, K. (2009). Meta-cognitive strategy instruction in intelligent tutoring systems: How, when, and why. Journal of Educational Technology and Society, 13(1), 25-39.

Chi, M. T. H. & VanLehn, K. (2012). Seeing deep structure from the interactions of surface features. Educational Psychologist, 47(3), 177-188.

Chi, M., VanLehn, K., Litman, D. & Jordan, P. (2011). Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies. Journal of User Modeling and User-Adapted Interaction, 21: 137-180. Springer Science and Business Media B.V. 2011.

Chi, M., VanLehn, K., Litman, D. & Jordan, P. (2011). An Evaluation of Pedagogical Tutorial Tactics for a Natural Language Tutoring System: A Reinforcement Learning Approach. International Journal of Artificial Intelligence and Education, 21, 1-2, pp. 83-113.

Chi, M.T.H., Roy, M. & Hausmann, R.G.M. (2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive Science, 32(2), 301-341.

Clarke, S., Chen, G. and Resnick, L. B. (in press) (in press). Classroom Discourse: The Social Turn. [Special Issue] International Journal of Educational Research.

Collins-Thompson, K. & Callan, J. (2005). Predicting reading difficulty with statistical reading models. Journal of the American Society for Information Science and Technology, 56(13) (pp. 1448-1462).

Connelly, J. & Katz, S. (2006). Intelligent dialogue support for physics problem solving: Some preliminary mixed results. Technology, Instruction, Cognition, and Learning, 4, 1-29.

Craig, S., Chi, MTH & VanLehn, K. (2009). Improving classroom learning by collaboratively observing human tutoring videos while problem solving. Journal of Educational Psychology, 101(4), 779-789.

De Jong, N. (2012). Oefenen met vloeiend spreken: Wat, hoe en waarom? In B. Bossers (Ed.), Vakwerk 8 (pp. 25-35). Amsterdam: BV NT2. [This is an edited volume for teachers of Dutch as a second language and other professionals in the field. The English translation of the title is “Practicing fluent speaking: What, how, and why?”]

de Jong, N. & Perfetti, C. A. (2011). Fluency training in the ESL classroom: An experimental study of fluency development and proceduralization. Language Learning, 61(2), 533-568.

Diziol, D., Walker, E., Rummel, N. & Koedinger, K.R. (2010). Using intelligent tutor technology to implement adaptive support for student collaboration. Educational Psychology Review, 22(1), 89-102. DOI 10.1007/s10648-009-9116-9

Dyke, G., Adamson, A., Howley, I., & Rosé, C. P. (in press). Enhancing Scientific Reasoning and Discussion with Conversational Agents. IEEE Transactions on Learning Technologies, special issue on Science Teaching.

Eskenazi, M. (2009). An overview of spoken language technology for education. Speech Communication, 51(10), 832-844.
Evans, K.L., Karabinos, M., Leinhardt, G. & Yaron, D. (2006). Chemistry in the field and chemistry in the classroom: A cognitive disconnect? Journal of Chemical Education 83 (4), 655-661.

Forbes-Riley, K., Rotaru, M. & Litman, D.J. (2008). The Relative Impact of Student Affect on Performance Models in a Spoken Dialogue Tutoring System. User Modeling and User-Adapted Interaction. Special issue on Affective Modeling and Adaptation. 18(1-2), 11-42.

Forsyth, C., Graesser, A., Pavlik, P., Cai, Z., Butler, H., Halpern, D. & Millis, K. (2013). Operation ARIES!: Methods, Mystery, and Mixed Models: Discourse Features Predict Affect in a Serious Game. Special Issue on Motivation, Meta-cognition, and Self-regulated Learning, Volume 5, Issue 1.

Frishkoff, G. A., Perfetti, C. A., & Collins-Thompson, K. (2011). Predicting robust vocabulary growth from measures of incremental learning. Scientific Studies of Reading, 15(1), 71-91.

Frishkoff, G., Collins-Thompson, K., Perfetti, C. & Callan, J. (2008). Measuring incremental changes in word knowledge: Experimental validation and implications for learning and assessment. Behavioral Research Methods, 40(4), 907-925.

Frishkoff, G., Perfetti, C. & Collins-Thompson, K. (2010). Lexical quality in the brain: ERP evidence for robust word learning from context. Developmental Neuropsychology Special Issue on Learning to Read: Early Latency Language ERP's, 1532-6942, Vol 35(4), pages 376-403.

Frishkoff, G., Perfetti, C. & Westbury, C. (2009). ERP Measures of Partial Semantic Knowledge: Left temporal indices of skill differences and lexical quality. Biological Psychology, 80(1), 130-147.

Gadgil, S. & Nokes, T. J. (2012). Overcoming collaborative inhibition through error-correction: A classroom experiment. Applied Cognitive Psychology, 26(3), 410-420.

Gadgil, S., & Nokes-Malach, T. J. (2012). Collaborative facilitation through error-detection: A classroom experiment. Applied Cognitive Psychology, 26(3), 410-420. doi: 10.1002/acp.18431

Gadgil, S., Nokes, T. J., & Chi, M. T. H. (2012). Effectiveness of holistic mental model confrontation in driving conceptual change. Learning and Instruction, 22(1), 47-61.

Gholson, B. & Craig, S. (2006). Promoting constructive activities that support vicarious learning during computer-based instruction. Educational Psychology Review, 18, 119-139.

Goldberg, R.F., Perfetti, C. & Schneider, W. (2006). Distinct and common cortical activations for multimodal semantic categories. Cognitive, Affective, and Behavioral Neuroscience. Volume 6, Number 3, September 2006, pp. 214-222(9).

Goldberg, R.F., Perfetti, C. & Schneider, W. (2006). Perceptual knowledge retrieval activates sensory brain regions. Journal of Neuroscience. 26:4917 – 4921

Goldberg, R.F., Perfetti, C., Fiez, J.A. & Schneider, W. (2007). Selective retrieval of abstract semantic knowledge in left prefrontal cortex. Journal of Neuroscience, 27:3790-8.

Goldin, I.M. & Ashley, K.D. (2012). Eliciting formative assessment in peer review, special issue of Journal of Writing Research 4(2), 203-237.

Graesser, A., McNamara, D. & VanLehn, K. (2005). Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iSTART. Educational Psychologist, 40(4), 225-234.

Guan, C. Q., Liu, Y., Chan, D. H. L., & Perfetti, C. A. (2011). Writing strengthens orthography and alphabetic-coding strengthens phonology in learning to read Chinese. Journal of Educational Psychology.

Gupta, N. K. & Rosé, C. P. (2010). Understanding Instructional Support Needs of Emerging Internet Users for Web-based Information Seeking, JEDM - Journal of Educational Data Mining, Vol 2(1), 38-82.

Gweon, G., Jain, M., Mc Donough, J., Raj, B. & Rosé, C. P. (in press). Measuring Prevalence of Other-Oriented Transactive Contributions Using an Automated Measure of Speech Style Accommodation. International Journal of Computer Supported Collaborative Learning.

Habte, B., Finger, S., Rosé, C. P. (2013). E-Learning in Engineering through Videoconferencing: The case of Addis Ababa Institute of Technology. International Journal of Engineering Pedagogy (iJEP), 3(2).

Harrer, A., McLaren, B., Walker, E., Bollen L. & Sewall, J. (2006). Creating cognitive tutors for collaborative learning: steps toward realization. User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI), 16: 175-209.

Harrer, A., Pinkwart, N., McLaren, B. & Scheuer, O. (2008). The Scalable Adapter Design Pattern: Enabling Interoperability Between Educational Software Tools. IEEE Transactions on Learning Technologies, 1(2), 131-143.
Hausmann, R.G.M. & VanLehn, K. (2010). The effect of self-explanation on robust learning. International Journal of Artificial Intelligence in Education, 20(4).

Heilman, M., Collins-Thompson, K., Eskenazi, M., Juffs, A. & Wilson, L. (2010). Personalization of Reading Passages Improves Vocabulary Acquisition. International Journal of Artificial Intelligence in Educaiton, Vol, 20(1), 73-98.
Hernandez, A., Li, P. & MacWhinney, B. (2005). The emergence of competing modules in bilingualism. TRENDS in Cognitive Sciences, 9(5),220-225.

Isotani, S., Bourdeau, J., Mizoguchi, R., Weiqin Chen, Wasson, B. & Jovanovic, J. (2011). Guest Editorial: Special Issue on Intelligent and Innovative Support Systems for CSCL. IEEE Transactions on Learning Technologies, January-March 2011, Vol 4(1), 1-4.

Jang, J., Schunn, C. D., & Nokes, T. J. (2011). Spatially distributed instructions reduce load to improve learning outcomes and efficiency. Journal of Educational Psychology, 103(1), 60-72

Jirout, J. & Klahr, D. (2012). Children’s scientific curiosity: In search of an operational definition of an elusive concept. Developmental Review, 32, #2, 125 – 160.

Juffs, A. (2007). Second language acquisition of relative clauses in the languages of East Asia. Studies in Second Language Acquisition, 29, 361-365.

Juffs, A., & Friedline, B. F. (2014). Sociocultural influences on the use of a web-based tool for learning English vocabulary. System, 42, 48-59.

Juffs, A. & Shirai, Y (in press). Convergence and Divergence in Functional and Formal Approaches to Second Language Acquisition. Second Language Research, 2015. Special edited edition.

Juffs, A., & Harrington, M. W. (2011). Aspects of working memory in L2 learning. Language Teaching: Reviews and Studies, 42.2, 137-166.

Kallai, A. Y., Schunn, C. D., & Fiez, J. A. (2012). Mental arithmetic activates analogic representations of internally generated sums. Neuropsychologia. 50, 2397-2407. doi: 10.1016/j.neuropsychologia.2012.06.009

Klahr, D. (2010). Coming Up for Air: But is it Oxygen or Phlogiston? A Response to Taber’s Review of Constructivist Instruction: Success or Failure? Education Review, Vol. 13 (13).

Klahr, D. (2012). Inquiry Science Rocks: Or Does It? Back Page, APS News. December 2012 (Volume 21, Number 11) http://www.aps.org/publications/apsnews/201212/backpage.cfm

Klahr, D. & Chen, Z. (2011). Finding one’s place in transfer space. Child Development Perspectives, 5(3), 196-204.

Klahr, D., Triona, L.M. & Williams, C. (2007). Hands On What? The Relative Effectiveness of Physical vs. Virtual Materials in an Engineering Design Project by Middle School Children. Journal of Research in Science Teaching , 44, 183-203.

Klahr, D., Zimmerman, C. & Jirout, J. (2011). Educational interventions to enhance, enrich, and encourage children’s scientific thinking. Science, 333, 971-975.

Koedinger, K.R. & Aleven, V. (2007). Exploring the assistance dilemma in experiments with Cognitive Tutors. Educational Psychology Review, 19: 239-264.

Koedinger, K.R. & Alibali, N. (2008). Trade-offs between grounded and abstract representations: Evidence from algebra problem solving. Cognitive Science 32(2), 366-397.

Koedinger, K.R., Corbett, A.T. & Perfetti, C. (2012). The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning. Cognitive Science 36(5): 757-798 (2012).

Kowalski, J. & Gordon, G. (2012). Refining an assessment for improving dictation skills of Chinese syllables. Journal of Educational Data Mining.

Kumar, R. & Rosé, C. P. (2011). Architecture for building Conversational Agents that support Collaborative Learning. IEEE Transactions on Learning Technologies, Special Issue on Intelligent and Innovative Support Systems for Computer Supported Collaborative Learning, Vol. 4:1; 21-34. IEEE Computer Society Press Los Alamitos, CA.

Kumar, R. & Rosé, C. P. (in press). Triggering Effective Social Support for Online Groups. ACM Transactions on Interactive Intelligent Systems.

Landi, N., Perfetti, C., Bolger, D.J., Dunlap, S. & Foorman, B.R. (2006). The role of discourse context in developing word form representations: A paradoxical relationship between reading and learning. Journal of Experimental Child Psychology. 94(2), 114-133.

Lane, H.C. & VanLehn, K. (2005). Teaching program planning skills to novices with natural language tutoring. Computer Science Education, 15(3), 183-201.

Li, P., Zhao, X. & MacWhinney, B. (2007). Dynamic self-organization and early lexical development in children. Cognitive Science, 31:4, 581-612.

Litman, D.J. & Forbes-Riley, K. (2006). Correlations between Dialogue Acts and Learning in Spoken Tutoring Dialogues. Natural Language Engineering, Vol 12(2), pp. 161-176, June 2006.

Litman, D.J. & Forbes-Riley, K. (2006). Recognizing Student Emotions and Attitudes on the Basis of Utterances in Spoken Tutoring Dialogues with both Human and Computer Tutors. Speech Communication, Vol 48(5), pp. 559-590, May 2006.

Liu, Y., Dunlap, S., Fiez, J.A. & Perfetti, C. (2007). Evidence for neural accommodation to a writing system following learning. Human Brain Mapping, 28: 1223-1234.

Liu, Y., Perfetti, C. & Wang, M. (2006). Visual analysis and lexical access of Chinese charactgers by Chinese as second language readers. Language and Linguistics, 7(3), 637-657. Institute of Linguistics, Academia Sinica in Taiwai. ISSN 1606-822X.

Liu, Y., Wang, M. & Perfetti, C. (2007). Threshold-style processing of Chinese characters for adult second language learners. Memory and Cognition, 35(3), 471-480.

MacWhinney, B. (2005). The emergence of linguistic form in time. Connection Science. 17 (Number 3-4/September-December 2005), 191-211.

Magner, U., Schwonke, R., Aleven, V., Popescu, O., & Renkl, A. (2012). Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments. Learning & Instruction, available online 30 July, 2012.

Makatchev, M., Jordan, P. & VanLehn, K. (2004). Abductive Theorem Proving for Analyzing Student Explanations and Guiding Feedback in Intelligent Tutoring Systems. Journal of Automated Reasoning. Special issue on Automated Reasoning and Theorem Proving in Education, 32(3), 187-226.

Martin, B., Mitrovic, A., Koedinger, K.R. & Mathan, S. (2011). Evaluating and improving adaptive educational systems with learning curves. UMUAI 21:3, 249–28.

Matlen, B. & Klahr, D. (2012). Sequential Effects of High and Low Instructional Guidance on Children's Acquisition and Transfer of Experimentation Skills. Instructional Science, June 2012.

Matsuda, N. & VanLehn, K. (2004). GRAMY: A geometry theorem prover capable of construction. Journal of Automated Reasoning, 32(1), 3-33.

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Cohen, W. W., Stylianides, G. & Koedinger, K.R. (in press). Cognitive anatomy of tutor learning: Lessons learned with SimStudent. Journal of Educational Psychology.

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G. J., & Koedinger, K. R. (2013). Studying the Effect of a Competitive Game Show in a Learning by Teaching Environment. International Journal of Artificial Intelligence in Education, 23, 1-21. DOI 10.1007/s40593-013-0009-1

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Cohen, W. W., Stylianides, G. J. & Koedinger, K.R. (2013). Cognitive anatomy of tutor learning: Lessons learned with SimStudent. Journal of Educational Psychology, 105(4), 1152-1163. doi: 10.1037/a0031955

McCormick, D. E., & Vercellotti, M. L. (2013). Examining the impact of self-correction notes on grammatical accuracy in speaking. TESOL Quarterly, 47 (2), 410-420.

McLaren, B.M., DeLeeuw, K.E. & Mayer, R.E. (2011). A politeness effect in learning with web-based intelligent tutors. International Journal of Human Computer Studies, 69(1-2), 70-79. doi:10.1016/j.ijhcs.2010.09.001.

McLaren, B.M., DeLeeuw, K.E. & Mayer, R.E. (2011). Polite web-based intelligent tutors: Can they improve learning in classrooms? Computers & Education, 56(3), 574-584. doi: 10.1016/j.compedu.2010.09.019.

Meier, A., Spada, H. & Rummel, N. (2007). A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning.

Michaels, S., O'Connor, C. & Resnick, L. (2007). Deliberative discourse idealized and realized: Accountable talk in the classroom and in civic life. Studies in Philosophy and Education. DOI 10.1007/S11217-007-9071-1.

Morett, L. & MacWhinney B. (2013). Syntactic transfer in English-speaking Spanish learners. Bilingualism: Language and Cognition. 16(1), 132-151.

Moss, J., Schunn, C. D., Schneider, W., McNamara, D. S. & VanLehn, K. (2011). The neural correlates of strategic reading comprehension: Cognitive control and discourse comprehension. NeuroImage, 58(2), 675-686.

Mostow, J. & Beck, J. (2006). Some useful tactics to modify, map and mine data from intelligent tutors. Natural Language Engineering, Cambridge University Press, 12(2), 195-208.

Mu, J., Stegmann, K., Mayfield, E., Rosé, C. P. & Fischer, F. (2012). The ACODEA Framework: Developing Segmentation and Classification Schemes for Fully Automatic Analysis of Online Discussions. International Journal of Computer Supported Collaborative Learning, 7(2), 285-305. DOI 10.1007/s11412-012-9147-y

Muldner, K., Burleson, W., van de Sande, B. & VanLehn, K. (2010). An Analysis of Students’ Gaming Behaviors in an Intelligent Tutoring System: Predictors and Impacts. Journal of User Modeling and User Adapted Interaction, Special Issue on Educational Data Mining. DOI: 10.1007/s11257-010-9086-0. Winner of 2011 James Chen Annual Award for Best UMUAI Paper.

Murray, R.C., VanLehn, K. & Mostow, J. (2004). Looking ahead to select tutorial actions: A decision-theoretic approach. International Journal of Artificial Intelligence and Education, 14, 235-278.

Nelson, J., Liu, Y., Fiez, J. & Perfetti, C. (2009). Assimilation and accommodation patterns in ventral occipitotemporal cortex in learning a second writing system. Human Brain Mapping, 30(3), 810-820.

Nelson, J.R., Balass, M. & Perfetti, C. (2005). Differences between written and spoken input in learning new words. Written Language & Literacy, 8(2), 25-44.

Nokes, T.J. (2009). Mechanisms of knowledge transfer. Thinking & Reasoning, 15, 1-36.

Nokes, T.J., Hausmann, R.G.M., VanLehn, K. & Gershman, S. (2011). Testing the instructional fit hypothesis: The case of self-explanation prompts. Instructional Science, 39(5), 645-666. DOI 10.1007/s11251-010-9151-4. Springer Science and Business Media B.V. 2010.

Nokes-Malach, T. J., VanLehn, K., Belenky, D. M., Lichtenstein, M. & Cox, G. (2012). Coordinating principles and examples through analogy and self-explanation. European Journal of Psychology of Education. DOI 10.1007/s10212-012-0164-z

Nokes-Malach, T.J. & Mestre J. (2013). Toward a Model of Transfer as Sense-Making. Educational Psychologist, 48:3, 184-207.

Pardos, Z.A., Baker, R.S.J.d., Gowda, S.M. & Heffernan, N.T. (2011). The Sum is Greater than the Parts: Ensembling Models of Student Knowledge in Educational Software. SIGKDD Explorations, 13 (2), 37-44.

Pavlik, P. (2006). Understanding and applying the dynamics of test practice and study practice. Instructional Science.

Pavlik, P. (2013). Mining the Dynamics of Student Utility and Strategy Use during Vocabulary Learning. Journal of Educational Data Mining. Special Issue on Motivation, Meta-cognition, and Self-regulated Learning, Volume 5, Issue 1.

Pavlik, P. & Anderson, J.R. (2008). Using a model to compute the optimal schedule of practice. Journal of Experimental Psychology: Applied, 14(2), 101-117.

Perfetti, C. (2007). Reading ability: Lexical quality to comprehension. Scientific Studies of Reading, 11(4), 357-383.

Perfetti, C. & Liu, Y. (2005). Orthography to phonology and meaning: Comparisons across and within writing systems. Reading and Writing, 18(3), 193-210.

Perfetti, C. & Bolger, D.J. (2004). The brain might read that way. Scientific Studies of Reading, 8(3), 293-304.

Perfetti, C., Liu, Y., Fiez, J.A., Nelson, J., Bolger, D.J. & Tan, L. (2007). Reading in two writing systems: Accommodation and assimilation in the brain’s reading network. Bilingualism: Language and Cognition, 10(2). 131-146. Special issue on “Neurocognitive approaches to bilingualism: Asian languages”, P. Li (Ed.).

Perfetti, C., Liu, Y., Tan, L.H. (2005). The Lexical Constituency Model: some implications of research on Chinese for general theories of reading. Psychological Review, 112(1), 43-59.

Perfetti, C., Tan, L.H. & Siok, W.T. (2006). Brain-behavior relations in reading and dyslexia: Implications of Chinese results. Brain and Language.

Perfetti, C., Wlotko, E.W. & Hart, L.A. (2005). Word learning and individual differences in word learning reflected in Event-Related Potentials. Journal of Experimental Psychology: Learning Memory and Cognition, 31(6), 1281-1292.

Perfetti, C., Yang, C. & Schmalhofer, F. (2008). Comprehension skill and word-to-text integration processes. Applied Cognitive Psychology, 22 (3), 303-318.

Popescu, O., Aleven, V. & Koedinger, K.R. (2005). Logic-Based Natural Language Understanding for Cognitive Tutors. Natural Language Engineering. Pages 1-15.

Porayska-Pomsta, K., Mavrikis, M., D'Mello, S., Conati, C., Baker, R.S.J.d. (in press). Knowledge Elicitation Methods for Affect Modeling in Education. International Journal of Artificial Intelligence in Education.

Presson, E., Sagarra, N., MacWhinney, B. & Kowalski, J. (2013). Compositional production in Spanish second language conjugation. Bilingualism: Language and Cognition.

Presson, N. & MacWhinney, B. (in press). Learning grammatical gender: The use of rules by novice learners. Applied Psycholinguistics.

Prior, A. & MacWhinney, B. (2012). Beyond inhibition: A bilingual advantage in task switching. Bilingualism: Language and Cognition 13: 253-262.

Prior, A., Kroll, J. & MacWhinney, B. (2012). Translation ambiguity but not word class predicts translation performance. Bilingualism: Language and Cognition, 16 (special issue 2), 458-474.

Prior, A., MacWhinney, B. & Kroll, J.F. (2007). Translation norms for English and Spanish: The role of lexical variables, word class, and L2 proficiency in negotiating translation ambiguity. Behavior Research Methods, 37, 134-140.

Prior, A., Wintner, S., MacWhinney, B. & Lavie, A. (2011). Translation ambiguity in and out of context. Applied Psycholinguistics, 32, 93-111.

Rau, M. A., Aleven, V., & Rummel, N. (2013). Interleaved practice in multi-dimensional learning tasks: which dimension should we interleave? Learning and Instruction, 23, 98-114.

Reed, S. K., Corbett, A., Hoffman, B., Wagner, A. & MacClaren, B. (2013). Effect of worked examples and Cognitive Tutor training on constructing equations. Instructional Science, 41, 1-24.

Reed, S. K., Stebick, S., Comey, B., & Carroll, D. (2012). Finding similarities and differences in the solutions of word problems. Journal of Educational Psychology, 104, 636-646.

Resnick, L. (2006). Making accountability really count. Educational Measurement: Issues and Practice, 25(1), 33-37.

Resnick, L. & Zurawsky, C. (2005). Getting Back on Course: Fixing Standards-Based Reform and Accountability. American Educator, 29(1), 8-46.

Ritter, S. (2005). Authoring model-tracing tutors. Technology, Instruction, Cognition and Learning, 2(3), 231-247.

Ritter, S., Anderson, J.R., Koedinger, K.R. & Corbett, A. (2007). The Cognitive Tutor: Applied research in mathematics education. Psychonomics Bulletin & Review, 14(2), pp. 249-255.

Rodrigo, M.M.T. & Baker, R.S.J.d. (2011). Comparing Learners' Affect While Using an Intelligent Tutoring System and a Simulation ProblemSolving Game. Research and Practice in Technology Enhanced Learning, 6(1), 43-66.

Rodrigo, M.M.T., Baker, R.S.J.d. (in press). Student Off-Task Behavior in Computer-Based Learning in the Philippines: Comparison to Prior Research in the USA. Teachers College Record.

Rodrigo, M.M.T., Baker, R.S.J.d., Agapito, J., Nabos, J., Repalam, M.C., Reyes, S.S. & San Pedro, M.C.Z. (2012). The Effects of an Interactive Software Agent on Student Affective Dynamics while Using an Intelligent Tutoring System. IEEE Transactions on Affective Computing, 3(2), 224-236.

Rodrigo, M.M.T., Baker, R.S.J.d., Agapito, J., Nabos, J., Repalam, M.C., Reyes, S.S. & San Pedro, M.O.C.Z. (2011). The Effects of an Embodied Conversational Agent on Student Affective Dynamics while Using an Intelligent Tutoring System. IEEE Transactions on Affective Computin, 2(4), 18-37.

Roll, Aleven, McLaren, Koedinger (2011). Improving students' help-seeking skills using meta-cognitive feedback in an intelligent tutoring system. Learning and Instruction, 21(2), 267-280.

Roll, I., Aleven, V., McLaren, B. & Koedinger, K.R. (2007). Designing for Metacognition - Applying Cognitive Tutor Principles to Metacognitive Tutoring. Metacognition and Learning, 2(2), 125-140.

Roll, I., Holmes, N. G., Day, J., & Bonn, D. (2012). Evaluating metacognitive scaffolding in guided invention activities. Instructional Science, 40, 691-710. doi:10.1007/s11251-012-9208-7

Roscoe, R.D. & Chi M.T.H. (2007). Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutors' explanations and questions. Review of Educational Research, 77(4), 534-574.

Roscoe, R.D. & Chi M.T.H. (2008). Tutor learning: The role of explaining and responding to questions. Instructional Science, 36(4), 321-350.

Rosé, C.P. & VanLehn, K. (2005). An Evaluation of a Hybrid Language Understanding Approach for Robust Selection of Tutoring Goals. International Journal of Artificial Intelligence in Education, 15(4), 325-355.

Rosé, C.P., Kumar, R., Aleven, V., Robinson, A. & Wu, C. (2006). CycleTalk: Data Driven Design of Support for Simulation Based Learning. International Journal of Artificial Intelligence in Education, 16, 195-223.

Rosé, C.P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K. Weinberger, A. & Fischer, F. (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning. International Journal of Computer Supported Collaborative Learning, 3(3), 237-271.

Salden, R., Aleven, V., Renkl, A. & Schwonke, R. (2009). Worked examples and tutored problem solving: redundant or synergistic forms of support? Topics in Cognitive Science, 1, 203-213.

Salden, R., Aleven, V., Schwonke, R. & Renkl, A. (2009). The Expertise Reversal Effect and Worked Examples in Tutored Problem Solving. Instructional Science, 38, 289-307. DOI 10.1007/s11251-009-9107-8.

Salden, R., Koedinger, K.R., Renkl, A., Aleven, V., McLaren, B. (2010). Accounting for Beneficial Effects of Worked Examples in Tutored Problem Solving. Educ Psychol Review, 22, 379-392. DOI 10.1007/s10648-010-9143-6

Schwonke, R., Ertelt, A., Otieno, C., Aleven, V., Salden, R., & Renkl, A. (2013). Metacognitive support promotes an effective use of instructional resources in intelligent tutoring. Learning and Instruction, 23, 136-150.

Schwonke, R., Renkl, A., Krieg, C., Wittwer, J., Aleven, V. & Salden, R. (2009). The Worked-example Effect: Not an Artifact of Lousy Control Conditions. Computers in Human Behavior, 25, 258-266.

Schwonke, R., Renkl, A., Salden, R., & Aleven, V. (2011). Effects of different ratios of worked solution steps and problem solving opportunities on cognitive load and learning outcomes. Computers in Human Bahavior, 27(1), 58-62.

Siler, S.A., Klahr, D., & Price, N (2012). Investigating the mechanisms of learning from a constrained preparation for future learning activity. Instructional Science. DOI: 10.1007/s11251-012-9224-7.

Siler, S.A. & VanLehn, K. (2009). Learning, interactional and motivational outcomes in one-to-one synchronous computer-mediated versus face-to-face tutoring. International Journal of Artificial Intelligence in Education. 19(1),73-102.

Siler, S. A. & VanLehn, K. (2014). Investigating microadaption in one-to-one tutoring. Journal of Experimental Education: Learning Instruction and Cognition, 00(0), 1-24. DOI: 10.1080/00220973.2014.907224

Siok, W.T., Niu, Z., Jin, Z. & Perfetti, C. Tan (2008). A structural-functional basis for dyslexia in the cortex of Chinese readers. Proceedings of the National Academy of Sciences, 105, 5561-5566.

Strand-Cary, Klahr, D. (2008). Developing elementary science skills: Instructional effectiveness and path independence. Cognitive Development, 23(4), 488-511.

Tan, L.H., Spinks, J.A., Eden, G.F., Perfetti, C. & Siok, W.T. (2005). Reading depends on writing, in Chinese. PNAS, 102, 8781-8785.

Tokowicz, N. & MacWhinney, B. (2005). Implicit and explicit measures of sensitivity to violations in second language grammar: An event-related potential investigation. Studies in Second Language Acquisition, 27: 173-204.

Tricomi, E. & Fiez, J.A. (2008). Feedback signals in the caudate reflect goal achievement on a declarative memory task. NeuroImage, 41(3), 1154-1167.

Triona, L.M. & Klahr, D. (2007). Hands-on science: Does it matter what the student's hands are on in 'hands-on’ science? The Science Education Review, 6, 121-125.

Tsovaltzi, D., Rummel, N., McLaren, B., Pinkwart, N., Scheuer, O., Harrer, A. & Braun, I. (2010). Extending a Virtual Chemistry Laboratory with a Collaboration Script to Promote Conceptual Learning. International Journal of Technology Enhanced Learning (IJTEL), 2(1-2), 91-110.

VanLehn, K. (2006). The Behavior of Tutoring Systems, International Journal of Artificial Intelligence in Education. 16(3), 227-265.

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems and other tutoring systems. Educational Psychologist, 46, 4, 197-221.

VanLehn, K., Graesser, A., Jackson, Jordan, P., Olney, A. & Rosé, C.P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science 31(1), 3-62.

VanLehn, K., Lynch, C., Schulze, K., Shapiro, J., Shelby, R., Taylor, L., Treacy, D., Weinstein, A. & Wintersgill, M. (2005). The Andes Physics Tutoring System: Lessons Learned. International Journal of Artificial Intelligence in Education, 15 (3). Pages 147-204.

Vercellotti, M.L. & De Jong, N. (in press). Use and Accuracy of Verb Complements in English L2 Speech. Dutch Journal of Applied Linguistics.

Vercellotti, M.L., Juffs, A. (in press). The development of lexical variety and the use of trigrams in spoken ESL. Special issue of Second Language Research, 2015.

Waalkens, M., Aleven, V., & Taatgen, N. (2013). Does supporting multiple student strategies lead to greater learning and motivation? Investigating a source of complexity in the architecture of intelligent tutoring systems. Computers & Education, 60(1), 159–171.

Walker, E., Rummel, N., Koedinger, K.R. (2009). CTRL: A Research Framework for Providing Adaptive Collaborative Learning Support. User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI), 19(5), 387-431.

Walker, E., Rummel, N., Koedinger, K.R. (2009). Integrating collaboration and cognitive tutoring data in evaluation of a reciprocal peer tutoring environment. Research and Practice in Technology Enhanced Learning, 4(3), 221-251.

Walkington, C. (in press). Using learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Article invited to special issue of Journal of Educational Psychology.

Walkington, C., Petrosino, A., & Sherman, M. (2013). Supporting algebraic reasoning through personalized story scenarios: How situational understanding mediates performance and strategies. Mathematical Thinking and Learning, 15(2), 89-120. DOI: 10.1080/10986065.2013.770717

Walkington, C., Sherman, M. & Petrosino, A. (2012). "Playing the game" of story problems: Coordinating situation-based reasoning with algebraic representation. Journal of Mathematical Behavior 31, 174-195.
Wang, H. C., Rosé, C. P. & Chang, C. Y. (2011). Agent-based Dynamic Support for Learning from Collaborative Brainstorming in Scientific Inquiry, International Journal of Computer Supported Collaborative Learning 6(3), pp 371-396.

Wang, M., Liu, Y. & Perfetti, C. (2004). The implicit and explicit learning of Chinese orthographic structure and function by alphabetic readers. Scientific Studies of Reading, 8(4), 357-379.

Wang, M., Perfetti, C. & Liu, Y. (2005). Chinese-English biliteracy acquisition: Cross-language and writing system transfer. Cognition, 97, 67-88.

Winne, P.H. & Baker, R.S.J.d. (2013). The Potentials of Educational Data Mining for Researching Metacognition, Motivation, and Self-Regulated Learning. Journal of Educational Data Mining, 5 (1), 1-8.

Yang, C.L. & Perfetti, C. (2006). Contextual Constraints on the Comprehension of Relative Clause Sentences in Chinese: ERPs Evidence. Language and Linguistics, 7(3): 697-730.

Yang, C.L., Perfetti, C. & Schmalhofer, F. (2007). ERP indicators of text integration across sentence boundaries. Journal of Experimental Psychology: Learning, Memory and Cognition. Vol 33(1) 55-89.

Yang, C.L., Perfetti, C. & Schmalhofer, F. (2005). Less skilled comprehenders’ ERPs show sluggish word-to-text integration processes. Written Language & Literacy, 8(2), 233-257.

Yang, Perfetti, C., Liu, Y. (2010). Sentence integration processes: An ERP study of Chinese sentence comprehension with relative clauses. Brain & Language, 112, 85-100.

Yoshimura, Y. & MacWhinney, B. (2010). Honorifics: A socio-cultural verb agreement cue in Japanese sentence processing. Applied Psycholinguistics 31: 551-569.

Yoshimura, Y. & MacWhinney, B. (2010). The use of pronominal case in English sentence interpretation. Applied Psycholinguistics 31: 619-633.

== Conference Papers ==

Adamson, D. & Rosé, C. (2012). Coordinating Multi-dimensional Support in CollaborativeConversational Agents (2012). In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 346-351.

Adamson, D., Bhartiya, D., Gurjal, B., Kedia, R., Singh, A. & Rosé, C.P. (2013). Automatically Generating Discussion Questions. Proceedings of AI in Education (AIED).

Adamson, D., Jang, H., Ashe, C., Yaron, D.& Rosé, C. P. (2013). Intensification of Group Knowledge Exchange with Academically Productive Talk Agents. Proceedings of Computer Supported Collaborative Learning.

Agarwal, N., Reddy, R. S., GVR, K., Rosé, C. P. (2011). A Multi-document Summarization System for Scientific Articles, in Companion Proceedings of the Annual Meeting of the Association for Computational Linguistics (demo).
Ai, H. & Litman, D.J. (2007). Knowledge Consistent User Simulations for Dialog Systems. Proceedings of Interspeech, Antwerp, Belgium, August 2007.

Aleahmad, T., Koedinger, K., & Zimmerman, J. (2012). Computer Support for Studying at the Right Times. Paper presented at AERA 2012, Vancouver, British Columbia, Canada.

Aleven, V. & Ashley, K. (2005). Toward supporting hypothesis formation and testing in an interpretive domain Proceedings of the 12th International Conference on Artificial Intelligence in Education. 732-734.

Aleven, A., Roll, I., McLaren, B. & Koedinger, K. (2012). Assessing Self-Regulated Learning: A (Meta)Cognitive Modeling Approach. Presentation in "Integrating Different Approaches to Investigating Self-Regulated Learning" Symposium, AERA 2012, Vancouver, British Columbia, Canada.

Aleven, V. & Roll, I. (2009). Analyzing patterns of help-seeking behavior using cognitive modeling and tree diagrams. Presentation in symposium, "Understanding the Complex Nature of Self-Regulatory Processes During Learning with Computer-based Learning Environments". AERA, 2009.

Aleven, V. & Rosé, C.P. (2005). Authoring plug-in tutor agents by demonstration: Rapid, rapid tutor development Proceedings of the 12th International Conference on Artificial Intelligence in Education. 735-737.

Aleven, V., McLaren, B., Roll, I. & Koedinger, K.R. (2005). Exploring meta-cognitive tutoring by the Help Tutor: An Interactive Event. Proceedings of the 12th International Conference on Artificial Intelligence in Education.

Aleven, V., McLaren, B., Roll, I., & Koedinger, K.R. (2004). Toward Tutoring Help Seeking: Applying Cognitive Modeling to Meta-Cognitive Skills; In the Proceedings of the Seventh International Conference on Intelligent Tutoring Systems (ITS-2004), Maceio, Brazil, August 2004. pp 227-239.

Aleven, V., McLaren, B., Ryu, E., Baker, R.S.J.d. & Koedinger, K.R. (2006). The Help Tutor: Does Metacognitive Feedback Improve Students' Help-Seeking Actions, Skills and Learning? 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan, 360-369.

Aleven, V., McLaren, B., Sewall, J. & Koedinger, K.R. (2006). The Cognitive Tutor Authoring Tools (CTAT): Preliminary evaluation of efficiency gains. In M. Ikeda, K.D. Ashley, & T-W. Chan (Eds), Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Lecture Notes in Computer Science, 4053 (pp. 61-70). Berlin: Springer.

Aleven, V., Myers, E., Easterday, Ogan, A. (2010). Toward a framework for the analysis and design of educational games. The 3rd IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning.

Aleven, V., Pinkwart, N., Ashley, K. & Lynch, C. (2006). Supporting Self-explanation of Argument Transcripts: Specific v. Generic Prompts . Workshop Proceedings on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems, 2006.

Aleven, V., Roll, I., McLaren, B., Ryu, E.J. & Koedinger, K.R. (2005). An architecture to combine meta-cognitive and cognitive tutoring: Pilot testing the Help Tutor. Proceedings of the 12th International Conference on Artificial Intelligence in Education. 2005. 17-24.

Aleven, V., Sewall, J., McLaren, B. & Koedinger, K.R. (2006). Rapid Authoring of Intelligent Tutors for Real-World and Experimental Use. In Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson, & W. Didderen (Eds.), Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies (ICALT 2006) (pp. 847-851). Los Alamitos, CA: IEEE Computer Society.

Anthony, L., Yang, J. & Koedinger, K.R. (2005). Evaluation of Multimodal Input for Entering Mathematical Equations on the Computer, ACM Conference on Human Factors in Computing Systems (CHI’2005), Portland, OR, 6 April 2005, p.1184-1187.

Anthony, L., Yang, J. & Koedinger, K.R. (2009). Interspersing annotated worked examples in algebra problem solving. Presented as part of "In Vivo Experimentation on Worked Examples Across Domains" Symposium at EARLI 2009

Anthony, L., Yang, J. & Koedinger, K.R. (2006). Towards the Application of a Handwriting Interface for Mathematics Learning, with IEEE Conference on Multimedia and Exp(ICME’2006), Toronto, Canada, July 2006.

Anthony, L., Yang, J. & Koedinger, K.R. (2007). Benefits of handwritten input for students learning algebra equation solving. In Proceedings of the International Conference on Artificial Intelligence in Education (AIED, 2007).

Anthony, L., Yang, J. & Koedinger, K.R. (2007). Adapting Handwriting Recognition for Applications in Algebra Learning. Proceedings of ACM Workshop on Educational Multimedia and Multimedia Education (EMME’2007), Augsburg, Germany, Sep 2007, pp. 47-56.

Anthony, L., Yang, J. & Koedinger, K.R. (2008). Steps toward enhancing robust learning through worked examples and handwriting-based input. Short paper presented at the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Anthony, L., Yang, J. & Koedinger, K.R. (2008). Toward Next-Generation, Intelligent Tutors: Adding Natural Handwriting Input. IEEE Multimedia 15(3), pp. 64-68.

Arguelle, J. & Rosé, C.P. (2006). InfoMagnets: Making Sense of Corpus Data. Companion Proceedings for the N. American Chapter of the Association for Computational Linguistics.

Arguelle, J. & Rosé, C.P. (2006). Museli: A Multi-source Evidence Integration Approach to Topic Segmentation of Spontaneous Dialogue, North American Chapter of the Association for Computational Linguistics (short paper).

Arguelle, J. & Rosé, C.P. (2006). Topic Segmentation of Dialogue. Proceedings of the NAACL Workshop on Analyzing Conversations in Text and Speech.

Asterhan, C.S.C., Butera, F., Nokes, T., Darnon, C., Schwarz, B. B., Butler, R., Levin, J., Belenky, D., & Gadgil, S. (2010). Motivation and affect in peer argumentation and socio-cognitive conflict. Proceedings of the 2010 International Conference of the Learning Sciences – ICLS 2010.

Asterhan, C.S.C., Butler, R., & Schwarz, B. B. (2010). On Competitive and Co-constructive dialectical Argumentation. Proceedings of the 2010 International Conference of the Learning Sciences, Vol 2, 213-215.

Asterhan, C.S.C. & Resnick, L. (2010). Structured dialogue and its effect on learning and development: A meta-review of the evidence. Paper presented at the 3rd annual inter-Science of Learning Centers (iSLC) conference. Boston University, Boston, MA.

Asterhan, C.S.C., Schwarz, B.B., Butera, F. Darnon, C., Nokes, T.J., Levine, J.M., Belenky, D.M., Gadgil, S. Resnick, L.B., & Sinatra, G. (2010). Motivation and affect in peer argumentation and socio-cognitive conflict. In S. Goldman and J. Pellegrino (Eds.), Proceedings of the International Conference for the Learning Sciences ICLS - 2010, Volume 2, 211-218. ISLS, USA.

Baker, R.S.J.d. (2012). Education Technology, Teacher Knowledge, and Classroom Impact: Frameworks and Approaches to Research. Symposium Discussant at AERA 2012, Vancouver, British Columbia, Canada.

Baker, R.S.J.d. (2007). Modeling and understanding students’ off-task behavior in intelligent tutoring systems. Proceedings of the SIGCHI conference on Human Factors in Computing Systems. ACM Publishers.

Baker, R.S.J.d. (2007). Is Gaming the System State-or Trait? On-Line Proceedings of the Workshop on Data Mining for User Modeling at the 11th International Conference on User Modeling 2007, 76-80.

Baker, R.S.J.d. (2009). Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task. Proceedings of the 2nd International Conference on Educational Data Mining (EDM 2009), 11-20.

Baker, R.S.J.d., de Carvalho, A.M.J.A., Raspat, J., Aleven, V., Corbett, A., Koedinger, K.R. (2010). Using Taxonomies and Educational Data Mining to Understand How Educational Software Design Impacts Gaming the System. "Factors That Impact Student Engagement and Learning Behaviors in ILEs" symposium presentation at American Educational Research Association.

Baker, R.S.J.D, Gowda, S., Corbett, A. & Ocumpaugh, J. (2012). Towards Automatically Detecting Whether Student Learning is Shallow. In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 444-453.

Baker, R.S.J.d. & Gowda, S.M. (2010). An Analysis of the Differences in the Frequency of Students' Disengagement in Urban, Rural, and Suburban High Schools. Proceedings of the 3rd International Conference on Educational Data Mining, 11-20.

Baker, R.S.J.d., Aleven, V. (2008). Help abuse and proper use: How helpful is on-demand help when it is used properly? Paper presented at the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Baker, R.S.J.d., Aleven, V., Koedinger, K.R., Rodrigo, M.T., Heffernan, N., Corbett, A., Roll, I. (2008). Gaming the System: Evidence from Data Mining and Human Observation on Affect, Attitudes, and Learning. Presentation at Technology, Instruction, Cognition, and Learning Symposium. (invited presentation)

Baker, R.S.J.d., Corbett, A. & Wagner, A. (2006). Human Classification of Low-Fidelity Replays of Student Actions. Proceedings of the Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (ITS 2006). Jhongli, Taiwan. Pages 29-36.

Baker, R.S.J.d., Corbett, A. & Aleven, V. (2009). Determining when an error is actually a slip. Presentation in "Educational Data Mining: Seeing How Students Really Err" Symposium at the 13th Biennial Conference of the European Association for Research on Learning and Instruction.

Baker, R.S.J.d., Corbett, A., Aleven, V. (2008). More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian Knowledge Tracing. Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008), 406-415.

Baker, R.S.J.d., Corbett, A., Aleven, V. (2008). Improving Contextual Models of Guessing and Slipping with a Truncated Training Set. Proceedings of the 1st International Conference on Educational Data Mining, 2008, 67-76.

Baker, R.S.J.d., Corbett, A., Koedinger, K.R. & Roll, I. (2005). Detecting When Students Game The System, Across Tutor Subjects and Classroom Cohorts 10th International Conference on User Modeling.

Baker, R.S.J.d., Corbett, A., Koedinger, K.R., & Roll, I. (2006). Generalizing Detection of Gaming the System Across a Tutoring Curriculum ; Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. p 402.-411.

Baker, R.S.J.d., Corbett, A., Koedinger, K.R., Evenson, S., Roll, I., Wagner, A., Naim, M., Raspat, J., Baker, D.J. & Beck, J. (2006). Adapting to When Students Game an Intelligent Tutoring System. In M. Ikeda, K. Ashley, & T-W. Chan (Eds.). ITS 2006, LNCS 4053, pp 392-401. Springer-Verlag Berlin Heidelberg 2006.

Baker, R.S.J.d., Corbett, A.T., Gowda, S.M. (in press). Affective states, and disengaged behaviors within an ITS. Proceedings of the 16th International Conference on Artificial Intelligence and Education, 31-40.

Baker, R.S.J.d., Corbett, A.T., Gowda, S.M., Wagner, A.Z., MacLaren, B.M., Kauffman, L.R., Mitchell, A.P. & Giguere, S. (2010). Contextual Slip and Prediction of Student Performance After Use of an Intelligent Tutor. Proceedings of the 18th Annual Conference on User Modeling, Adaptation, and Personalization, 52-63.

Baker, R.S.J.d., de Carvalho, A.M.J.A. (2008). Labeling Student Behavior Faster and More Precisely with Text Replays. Proceedings of the 1st International Conference on Educational Data Mining, 2008, 38-47.

Baker, R.S.J.d., de Carvalho, A.M.J.A., Raspat, J., Aleven, V., Corbett, A. & Koedinger, K.R. (2009). Educational Software Features that Encourage and Discourage "Gaming the System". Proceedings of the 14th International Conference on Artificial intelligence in Education (AIED), Frontiers in Artificial Intelligence and Applications, Vol. 200. IOS Press: Amsterdam, The Netherlands, 475-482.

Baker, R.S.J.d., de Carvalho, A.M.J.A., Raspat, J., Aleven, V., Corbett, A., Koedinger, K.R., Cocea, M. & Hershkovitz, A. (2010). Educational Data Mining Methods For Studying Student Behaviors Minute by Minute Across an Entire School Year. Symposium presentation at International Conference of the Learning Sciences.

Baker, R.S.J.d., Goldstein, A. B. & Heffernan, N.T. (2010). Detecting the Moment of Learning. Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 6094, 2010, 25-34. (People's Choice Award for Best Oral Presentation; Finalist for Best Paper Award).

Baker, R.S.J.d., Gowda, S. & Corbett, A. (2011). Automatically detecting a student's preparation for future learning: help use is key. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, and J. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Baker, R.S.J.D., Gowda, S., Wixon, M., Kalka, J., Wagner, A., Salvi, A., Aleven, A., Kusbit, G., Ocumpaugh, J. & Rossie, L. (2012). Sensor-free automated detection of affect in a Cognitive Tutor for Algebra. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012), 126-133.

Baker, R.S.J.d., Gowda, S.M. & Corbett, A.T. (2011). Towards predicting future transfer of learning. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 22-30. Springer-Verlag Berlin Heidelberg.

Baker, R.S.J.d., Isotani, S. & de Carvalho, A.M.J.A. (2011). Minera�¡ç�ão de Dados Educacionais: Oportunidades para o Brasil. Revista Brasileira de Inform��ática na Educa�ção.

Baker, R.S.J.d. & Koedinger, K.R. (2008). Educational Data Mining: Opportunities for the International Internet Classroom. Presentation at AAAI Fall Symposium: Education Informatics: Steps Towards the International Internet Classroom.

Baker, R.S.J.d., Mitrovic, A. & Mathews, M. (2010). Detecting Gaming the System in Constraint-Based Tutors. User Modeling, Adaptation, and Personalization; Lecture Notes in Computer Science 2010, Vol 6075/2010, 267-278.

Baker, R.S.J.d., Moore, G., Wagner, A., Kalka, J., Karabinos, M., Ashe, C. & Yaron, D. (2011). The Dynamics Between Student Affect and Behavior Occuring Outside of Educational Software. Proceedings of the 4th bi-annual International Conference on Affective Computing and Intelligent Interaction.

Baker, R.S.J.d., Pardos, Z., Gowda, S., Nooraei, B. & Heffernan, N. (2011). Ensembling predictions of student knowledge within intelligent tutoring systems. In J. Konstan, R. Conejo, J.L. Marzo & N. Oliver (Eds.). User Modeling, Adaptation and Personalization: 19th International Conference, UMAP 2011. Lecture Notes in Computer Science, Vol. 6787, 13-24. Springer-Verlag Berlin Heidelberg.

Baker, R.S.J.d., Rodrigo, M.T., Heffernan, N., Corbett, A., Roll, I., Aleven, V., Koedinger, K.R. (2008). Gaming the System: Evidence from data mining and human observation on affect, attitudes and learning. Abstract in Symposium: Learners Challenging ID – Unobtrusive Views on the Use of Instructional Interventions in CBE. (AERA 2008).

Baker, R.S.J.d., Roll, I., Corbett, A. & Koedinger, K.R. (2005). Do Performance Goals Lead Students to Game the System? Proceedings of the 12th International Conference on Artificial Intelligence in Education. 2005. 57-64.

Balass, M., Nelson, J.R. & Perfetti, C. (2009). Learning ESL Vocabulary with Context and Definitions: Order Effects and Self-Generation. Paper presented at the Second Annual Meeting of Inter-Science of Learning Center Student and Post-doctoral Conference, Seattle, WA.

Bauer, A. & Koedinger, K.R. (2006). Developing a Note Taking Tool from the Ground Up. Ed-Media 2005. AACE Press, 4181-4186.

Bauer, A. & Koedinger, K.R. (2006). Pasting and Encoding: Note-taking in Online Courses. IEEE International Conference on Advanced Learning Technologies (ICALT) 2006, pps 789-793.

Bauer, A. & Koedinger, K.R. (2007). Selection-based note-taking applications. ACM Symposium on Human Factors in Computing Systems 2007.

Beck, J. (2006). Using learning decomposition to analyze student fluency development. Proceedings of the Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (ITS 2006). Jhongli, Taiwan. Pages 21-28.

Beck, J. (2007). Does learner control affect learning? Paper presented at the 13th International Conference on Artificial Intelligence in Education (AIED 2007).

Beck, J. (2007). Difficulties in inferring student knowledge from observations (and why you should care). Proceedings of Workshop on Educational Data Mining (AIED 2007). 21-30.

Beck, J., Chang, J., Mostow, J. & Corbett, A. (2008). Does help help? A comparison of three evaluation frameworks. Paper presented at the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Beck, J. & Mostow, J. (2008). How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students. Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Belenky, D.M. & Nokes, T.J. (2010). Optimizing learning environments: An individual differences approach to learning and transfer. In S. Ohlsson & R. Catrambone (Eds.). Proceedings of the 32nd Annual Conference of the Cognitive Science Society, 459-464. Austin, TX: Cognitive Science Society.

Belenky, D.M. & Nokes, T.J. (2009). Motivation and Transfer: The role of achievement goals in preparation for future learning. Proceedings of the 31st Annual Meeting of the Cognitive Science Society, 2009, 1163-1168.

Belenky, D.M. & Nokes-Malach, T. J. (2012). Task-based versus course-level achievement goals: An experimental investigation of mastery-approach goals and knowledge transfer. Paper presented at the 2013 Annual Meeting of the American Educational Research Association, San Francisco, CA.

Belenky, D.M. & Nokes-Malach, T.J. (2012). How Mastery-Approach Goal Motivations Interact With Discovery by Contrasting Cases to Facilitate Transfer. Paper presented at "On the Design, Implementation, and Outcomes of Using Contrasts in Learning" Symposim at AERA 2012.

Bernacki, M.L., Nokes-Malach, T.J. & Aleven, V. (2012). Investigating Stability and Change in Unit-Level Achievement Goals and Their Effects on Math Learning With Intelligent Tutors. Presentation in "SIG Motivation in Education" Roundtable Session. AERA 2012, Vancouver, British Columbia, Canada.

Bhide, A., Gadgil, S., Zelinsky, C.M, & Perfetti, C. (2013). Does reading in an alphasyllabary affect phonemic awareness? Inherent schwa effects in Marathi-English bilinguals. Paper presented at the Society for the Scientific Study of Reading Conference in Hong Kong, July 2013.

Blessing, S. B., Gilbert, S.G., Oureda, S. & Ritter, S. (2007). Lowering the Bar for Creating Model-Tracing Intelligent Tutoring Systems. Proceedings of the 13th International Conference on Artificial Intelligence in Education.

Blessing, S.B., Gilbert, S.G. & Ritter, S. (2006). Developing an authoring system for cognitive models within commercial-quality ITSs. In Proceedings of the Nineteenth International FLAIRS Conference, pp. 497-502.

Bolger, D.J., Yang, C.L., Balass, M. & Perfetti, C. (2008). Learning the meanings of words from contexts and definitions: ERP evidence. Paper presented at the 15th Annual Meeting of the Society for the Scientific Study of Reading, Asheville, NC (July 2008).

Booth, J. (2012). Investigating Motivational Predictors of Traditional and Example-Based Algebra Learning. Proceedings of AERA 2013. (poster presentation)

Booth, J. (2013). Worked Examples: Who Do They Work For? Proceedings of AERA 2013

Booth, J. (2009). Improving Algebra Learning in Real World Classrooms with Worked Examples and Self-Explanation. Paper presented in the Presidential Symposium entitled The New Learning Sciences at the annual meeting of the Eastern Psychological Association, Pittsburgh, PA.

Booth, J. & Koedinger, K.R. (2009). Facilitating the Diagrammatic Advantage for Algebraic Word Problems. Paper presented at AERA, 2009.

Booth, J., Koedinger, K.R. & McLaughlin, E. (2012). Improving Math Learning with Worked Examples. Presented at ‘Cognition in the Classroom: Bringing Research-Based Principles to Middle School Math’ Invited Symposium. SREE 2012.

Booth, J.L. & Koedinger, K.R. (2012). Worked Examples and Self-Explanation. Paper presented at "Bridging Research and Practice: From Cognitive Principles to Design Principles of Curriculum, Instruction, and Assessment" Symposium at AERA 2012.

Booth, J. & Siegler, R. (2007). The Role of internal representations of magnitude in numerical estimation. Paper presented at the 12th Biennial Conference for Research on Learning and Instruction (EARLI). Budapest, Hungary, August, 2007.

Booth, J. & Koedinger, K.R. (2008). Key misconceptions in algebraic problem solving. In B.C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Cognitive Science Society (pp. 571-576). Austin, TX: Cognitive Science Society.

Booth, J. & Koedinger, K.R. (2010). Facilitating Low-Achieving Students’ Diagram Use in Algebraic Story Problems. In S. Ohlsson & R. Catrambone (Eds.). Proceedings of the 32nd Annual Conference of the Cognitive Science Society, 1649-1654. Austin, TX: Cognitive Science Society.

Booth, J., Koedinger, K.R. & Siegler, R. (2007). The effect of prior conceptual knowledge on procedural performance and learning in algebra. In D.S. McNamara & J.G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society. [Abstract]

Booth, J.L., Paré-Blagoev, J. & Koedinger, K.R. (2010). Transforming equation-solving assignments to improve algebra learning: A collaboration with the SERP-MSAN Partnership. Paper presented at the annual meeting of the American Educational Research Association

Borek, A., McLaren, B., Karabinos, M. & Yaron, D. (2009). How Much Assistance is Helpful to Students in Discovery Learning? Proceedings of the Fourth European Conference on Technology Enhanced Learning (EC-TEL 2009), Learning in the Synergy of Multiple Disciplines. Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 5794, 391-404.

Brown, J. & Eskenazi, M. (2005). Student Text And Curriculum Modelling For Reader-Specific Document Retrieval. Proceedings of IASTED International Conference on Human-Computer Interaction. 2005.

Brown, J. & Eskenazi, M. (2006). Using Simulated Students for the Assessment of Authentic Document Retrieval; Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan. P 685-688.

Brown, J., Frishkoff, G. & Eskenazi, M. (2005). Automatic question generation for vocabulary assessment. Proceedings of Human Language Technology, HLT/EMNLP 2005. Vancouver, B.C

Brunskill, E. (2011). Estimating prerequisite structure from noisy data. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, and J. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Brunskill, E. (2012). Student Variability and Automated Instructional Policies. Paper presented at Microsoft Research at University of Washington (MSR/UW) Summer Institute on Crowdsourcing Personalized Online Education, July 2012

Brunskill, E. & Russell, S. (2011). Partially observable sequential decision making for problem selection in an intelligent tutoring system. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, and J. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Butcher, K.(2010). How diagram interaction supports learning: Evidence from think alouds during intelligent tutoring. Diagrammatic Representation and Inference: Lecture Notes in Computer Science, 2010, Volume 6170/2010, 295-297. Springer

Butcher, K. & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D.S. McNamara & J.G. Trafton (Eds.), Proceedings of the 29th Annual Meeting of the Cognitive Science Society, (pp. 137-142).

Butcher, K. & Aleven, V. (2009). Visual self-explanation during intelligent tutoring: More than attentional focus? Presented as part of "In Vivo Experimentation on Self-Explanation Across Domains" Symposium at European Association for Research on Learning and Instruction, (EARLI 2009). Amsterdam, the Netherlands.

Butcher, K. & Chi, M.T.H. (2006). How can diagrams scaffold text comprehension. EARLI SIG2 Meeting, University of Nottingham.

Butcher, K. & Aleven, V. (2008). Diagram Interaction during Intelligent Tutoring in Geometry: Support for Knowledge Retention and Deep Transfer. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1736-1741). Austin, TX: Cognitive Science Society.

Butcher, K. & Aleven, V. (2008). Learning from visual-verbal sources in intelligent tutoring. Paper presented at the Inter-Science of Learning Center (iSLC) Conference, Carnegie Mellon University, Pittsburgh, PA.

Butcher, K. & Aleven, V. (2010). Learning during intelligent tutoring: When do integrated visual-verbal representations improve student outcomes? In S. Ohlsson & R. Catrambone (Eds.). Proceedings of the 32nd Annual Conference of the Cognitive Science Society, 2888-2893. Austin, TX: Cogn

Callan, J., Eskenazi, M. & Perfetti, C. (2006). Progress in Providing Reader-Specific lexical Practice for Inproved Reading Comprehension. IES Research Conference. June 15-16 2006, Washington DC

Cao, F., Vu, M., Chan, H., Lawrence, J., Harris, L., Guan, Q., Xu, Y., & Perfetti, C. A. (2010). Writing helps reading in English learners of Chinese: An fMRI study. Society for Neuroscience, San Diego, CA.

Carlson, R., Genin, K., Rau, M. & Scheines, R. (2013). Student Profiling from Tutoring System Log Data: When do Multiple Graphical Representations Matter? In D’Mello, S. K., Calvo, R. A., and Olney, A. (eds.) Proceedings of the 6th International Conference on Educational Data Mining. EDM 2013, 12-19.

Carlson, R., Keiser, V., Matsuda, N., Rosé, C. P. & Koedinger, K. (2012). Building a Conversational SimStudent. In S.A. Cerri, W. J. Clancey, G. Papadourakis & K. Panourgia (Eds). Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 563-569.

Cen, H., Koedinger, K.R. & Junker, B. (2005). Automating Cognitive Model Improvement by A*Search and Logistic Regression. Proceedings of AAAI Workshop on Educational Data Mining. 2005.

Cen, H., Koedinger, K.R. & Junker, B. (2006). Learning Factors Analysis – A General Method for Cognitive Model Evaluation and Improvement. In Ikeda et al (Eds.). Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), p 164-175. Springer: Berlin/Heidelberg.

Cen, H., Koedinger, K.R. & Junker, B. (2007). Is over practice necessary? – Improving learning efficiency with the Cognitive Tutor through educational data mining. In R. Luckin et al (Eds.). Proceedings of 13th International Conference on Artificial Intelligence in Education (AIED 2007), pp. 511-518. IOS Press.

Cen, H., Koedinger, K.R. & Junker, B. (2008). Comparing two IRT models for cognitive model evaluation. Short paper presented at the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Chan, H. L., Guan, Q., Liu, Y., Perfetti, C. & Wu, S. M. (2010). Pinyin plus writing: An integrated approach to learning Chinese characters. Paper session presented at Research in Reading Chinese and Related Asian Languages (RRC), Toronto, Canada.

Chang, K., Beck, J., Mostow, J. & Corbett, A. (2006). A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems; Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. p 104-113

Chase, C.C. & Shemwell, J.T. (2012). Learning Scientific Principles With Contrasting Cases: Key Ingredients ofEffective Contrast-Focused Instruction. Paper presented at "On the Design, Implementation, and Outcomes of Using Contrasts in Learning" Symposim at AERA 2012.

Chen G., Michaels, S. & O’Connor, C. (2011). Coding and analysis of classroom dialogue. Paper presented at the Social and Communicative Factors Thrust Workshop, Pittsburgh Science of Learning Center. Pittsburgh, PA, USA.

Chen, G. & Resnick, L. B. (2011). How accountable talk works in the classroom: Analyzing young children’s learning of science. Paper presented at the 9th International Conference on Computer Supported Collaborative Learning (CSCL 2011). Hong Kong: International Society of the Learning Sciences.

Chi, M. & VanLehn, K. (2007). Domain-specific and domain-independent interactive behaviors in Andes. In R. Luckin & K. Koedinger, K.R. (Eds.), Artificial Intelligence in Education. Amsterdam, Netherlands: IOS Press.

Chi, M. & VanLehn, K. (2007). Porting an intelligent tutoring system across domains. In R. Luckin & K. Koedinger, K.R. (Eds.), Artificial Intelligence in Education. Amsterdam, Netherlands: IOS Press.

Chi, M. & VanLehn, K. (2007). The impact of explicit strategy instruction on problem-solving behaviors across intelligent tutoring systems. In D. McNamara & G. Trafton (Eds.) Proceedings of the 29th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.

Chi, M. & VanLehn, K. (2007). Accelerated future learning via explicit instruction of a problem solving strategy. In R. Luckin, K. R. Koedinger, K.R. & J. Greer (Eds.) Artificial Intelligence in Education. pp. 409-416. Amsterdam, Netherlands: IOS Press.

Chi, M. & VanLehn, K. (2008). Eliminating the gap between the high and low students through meta-cognitive strategy instruction. Lecture Notes in Computer Science: Vol 5091. Proceedings of the 9th International Conference on Intelligent Tutoring Systems, 2008. Heidelberg: Springer Berlin, 603-614.

Chi, M. & VanLehn, K., Litman, D.J. (2010). Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning To Induce Pedagogical Tutorial Tactics. Intelligent Tutoring Systems: Lecture Notes in Computer Science 2010, Volume 6094/2010, 224-234.

Chi, M., Jordan, P., VanLehn, K. & Litman, D.J. (2009). To Elicit Or To Tell: Does It Matter? Proceedings of the 14th International Conference on Artificial Intelligence in Education (AIED 2009), 197-204.

Chi, M., Koedinger, K., Gordon, G., Jordan, P. & VanLehn, K. (2011). Instructional factors analysis: A Cognitive model for multiple instructional interventions. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, and J. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Chi, M., VanLehn & Litman, D.J. (2010). The More the Merrier? Examining Three Interaction Hypotheses. In S. Ohlsson & R. Catrambone (Eds.). Proceedings of the 32nd Annual Conference of the Cognitive Science Society, 2870-2876. Austin, TX: Cognitive Science Society.

Chi, M., VanLehn, K., Litman, D.J., & Jordan, P. (2010). Inducing Effective Pedagogical Strategies Using Learning Context Features. User Modeling, Adaptation, and Personalization; Lecture Notes in Computer Science 2010, Vol 6075/2010, 147-158. Springer.

Chi, M.T.H. (2007). Teaching a stand-alone module: Emergence for understanding science concepts. Paper in Symposium: Complex Systems and the Cognitive Sciences: Potential for Pervasive Theoretical and Research Implications? (CogSci 2007).

Clarke, S. (2012). The Right to Speak. In L.B. Resnick, C.A. Asterhan & S.N. Clarke (Eds.) Socializing Intelligence through Academic Talk and Dialogue. Washington, D.C.: American Educational Research Association.

Clarke, S.N. & Chen, G. (2012). The Structure of Productive Classroom Discussion. Paper presented at the annual meeting of the British Educational Research Association, Manchester, United Kingdom.

Clarke, S., Chen, G., Stainton, K., Katz, S., Greeno, J., Resnick, L., Howley, H., Adamson, D. & Rosé, C. P. (2013). The Impact of CSCL Beyond the Online Environment. Proceedings of Computer Supported Collaborative Learning.

Clarke, S.N., Resnick, L.B., Rosé, C.P., Chen, G., Stainton, C., Katz, S., Dyke, G., Adamson, D., Howley, I., Greeno, J., Spiegel, S., & Granger, R. (2012) (2012). Towards Discursive Instruction: From I-R-E to Accountable Talk. Paper presented at the LearnLab’s Annual Learning Science Workshop on Use of Technology Toward Enhancing Achievement and Equity in the 21st Century, Pittsburgh, PA.

Cocea, M., Hershkovitz, A. & Baker, R.S.J.d. (2009). The Impact of Off-Task and Gaming Behaviors on Learning: Immediate or Aggregate? Proceedings of the 14th International Conference on Artificial Intelligence in Education (AIED), 507-514.

Collins-Thompson, K. & Callan, J. (2007). Automatic and human scoring of word definition resopnses. In Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Rochester, NY. (April, 2007)

Connelly, J. & Katz, S. (2009). Toward more robust learning of physics via reflective dialogue extensions. Proceedings of World Convernece on Educational Multimedia, Hypermedia and Telecommunications (EDMEDIA 2009). Chesapeake: VA: AACE, 1946-1951.

Connelly, J. & Katz, S. (2009). Toward more robust learning of physics via reflective dialogue extensions. Proceedings of ED-MEDIA 2009.

Corbett, A., Reed, S., Hoffman, R., McLaren, B. & Wagner, A. (2010). Interleaving worked examples and Cognitive Tutor support for algebraic modeling of problem situations. In S. Ohlsson & R. Catrambone (Eds.). Proceedings of the 32nd Annual Conference of the Cognitive Science Society, 2882-2888. Austin, TX: Cognitive Science Society.

Corbett, A., Wagner, A., Lesgold, A., Ulrich, H. & Stevens, S. (2006). Analyzing Algebra Example Solutions. International Conference of the Learning Sciences (ICLS 2006). Bloomington, IN, USA. p. 99

Craig, S., VanLehn, K. & Chi, M. (2008). Promoting learning by observing deep-level reasoning questions on quantitative physics problem solving with Andes. In K. McFerrin et al. (Eds.) Proceedings of Society for Information Technology & Teacher Education International Conference 2008 (1065-1068). Chesapeake, VA: AACE.

Craig, S., VanLehn, K., Gadgil, S. & Chi, M. (2007). Learning from collaboratively observing videos during problem solving with Andes. In R. Luckin, K. R. Koedinger, K.R. & J. Greer (Eds.) Artificial Intelligence in Education. pp. 554-556. Amsterdam, Netherlands: IOS Press.

Cui, Y., Kumar, R., Rosé, C.P. & Koedinger, K.R. (2008). Story generation to accelerate math problem authoring for practice and assessment. Short paper presented at the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Cui, Y. & Rosé, C.P. (2008). An Authoring tool that facilitates the rapid development of dialogue agents for intelligent tutoring. Short paper presented at the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Davenport, J., Karabinos, M. & Yaron, D. (2005). Exploring the ways in which coordinating different representations of chemical processes influences conceptual learning in introductory chemistry. Nineteenth Biennial Conference on Chemical Education in West Lafayette, Indiana. July 31, 2006. P 104.

Davenport, J., Klahr, D. & Koedinger, K.R. (2007). The influence of diagrams on chemistry learning. Paper presented at the 12th Biennial Conference of the European Association for Research on Learning and Instruction (EARLI), August 2007

Davenport, J. L., Leinhardt, G., Greeno, J., Koedinger, K., Klahr, D., Karabinos, M., & Yaron, D. J. (2014). Evidence-Based Approaches to Improving Chemical Equilibrium Instruction. Journal of Chemical Education, 91(10), 1517-1525.

Davenport, J., McEldoon, K. & Klahr, D. (2007). Depicting invisible processes: The influence of molecular-level diagrams in Chemistry instruction. Proceedings of the 29th Annual meeting of the Cognitive Science Society, p. 1737, August 2007

Davenport, J., Rafferty, A., Timms, M., Yaron, D. & Karabinos, M. (2012). ChemVLab+: Evaluating a Virtual Lab Tutor for High School Chemistry (short paper), ICLS2012, Volume 2, 381-385.

Davenport, J., Yaron, D., Karabinos, M. & Greeno, J. (2008). Conceptual development in chemical equilibrium. Paper presented in Research in Chemical Education Symposium at the 20th Biannual Conference on Chemical Education, Bloomington, IN (July 2008).

Davenport, J., Yaron, D., Karabinos, M., Klahr, D. & Koedinger, K.R. (2007). Chemical equilibrium: an evaluation of a new type of instruction. Gordon, G. Conference for Chemistry Education Research and Practice, 2007.

Davenport, J., Yaron, D., Klahr, D. & Koedinger, K.R. (2008). When do diagrams enhance learning? A framework for designing relevant representations. Proceedings of the 2008 International Conference of the Learning Sciences, Utrecht, Netherlands, June 2008.

Davenport, J., Yaron, D., Klahr, D. & Koedinger, K.R. (2008). When do diagrams enhance science learning? Presented at the First Annual Inter-Science of Learning Center Conference in Pittsburgh, PA, February 2008.

De Jong, N. (2012). Does time pressure help or hinder oral fluency? In N. de Jong, K. Juffermans, M. Keijzer, & L. Rasier (Eds.), Papers of the Anéla 2012 Applied Linguistics Conference (pp. 43-52). Delft: Eburon.

De Jong, N. (2012). Technieken voor het oefenen van vloeiend spreken. Workshop given at the LES Conference, November 10, 2012, Amsterdam. [English translation of the title: Techniques for practicing fluent speaking; this is a conference for teachers of Dutch as a second language and other professionals in the field.]

De Jong, N. & Halderman, L. (2009). The role of vocabulary and grammar knowledge in second-language oral fluency: A correlational study. Paper presented at the Second Language Research Forum, East Lansing, MI, October 2009.

De Jong, N. & Halderman, L. (2009). Training formulaic sequences has mixed short-term effects on L2 oral fluency. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009.

De Jong, N. & Poelmans, P. (2011). Accuracy and complexity in second language speech: Do specific measures make the difference? Presentation at the EuroSLA conference, Stockholm, September 2011.

De Jong, N. & Vercellotti, M. L. (2011). Norming picture story prompts for second language production research: Fluency, linguistic items, and speakers’ perception. Paper presented at the American Association for Applied Linguistics conference 2011, Chicago, IL, March 2011.

De Jong, N., Halderman, L. & Ross, M. (2009). The effect of formulaic sequences training on fluency development in an ESL classroom. Paper presented at the American Association for Applied Linguistics Conference, Denver, CO, March 2009.

De Jong, N., McCormick, D., O'Neill, C. & Bradin Siskin, C. (2007). Self-correction and fluency in ESL speaking development. Paper presented at the American Association for Applied Linguistics (AAAL)Conference, April 2007 in Costa Mesa, CA.

Dela Rosa, K. & Eskenazi, M. (2011). Impact of Word Sense Disambiguation on Ordering Dictionary Definitions in Vocabulary Learning Tutors, FLAIRS 2011.

Dela Rosa, K. & Eskenazi, M. (2011). Self-Assessment of Motivation: Explicit and Implicit Indicators in L2 Vocabulary Learning. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 296-303. Springer-Verlag Berlin Heidelberg.

Dela Rosa, K. & Eskenazi, M. (2011). Effect of Word Complexity on L2 Vocabulary Learning. Proceedings of the 49th annual meeting of the Association for Computational Linguistics: Human Language Technologies' 6th Workshop on Innovative Use of NLP for Building Educational Applications (ACL-HLT: BEA 2011).

Dela Rosa, K., Parent, G. & Eskenazi, M. (2010). Multimodal learning of words: A study on the use of speech synthesis to reinforce written text in L2 language learning

Dela Rosa, K., Parent, G. & Eskenazi, M. (2010). M.Multimodal learning of words: A study on the use of speech synthesis to reinforce written text in L2 language learning. Proceedings of the ISCA Workshop on Speech and Language Technology in Education. (SLaTE 2010).

Dickison, D., Ritter, S., Nixon, T., Harris, T. K., Towle, B., Murray, R. C. & Hausmann, R.G.M. (2010). Predicting the Effects of Skill Model Changes on Student Progress. Intelligent Tutoring Systems Lecture Notes in Computer Science, 2010, Volume 6095/2010, 300-302, DOI: 10.1007/978-3-642-13437-1_51

Diziol, D., Rummel, N. & Spada, H. (2007). Unterstützung von computervermitteltem kooperativem Lernen in Mathematik durch Strukturierung des Problemlöseprozesses und adaptive Hilfestellung [Supporting computer-mediated collaborative learning in mathematics by structuring the problem-solving process and offering adaptive support]. Paper presented at the 11th Conference of the "Fachgruppe Pädagogische Psychologie der Deutschen Gesellschaft für Psychologie" [German Psychological Association]. Berlin, Germany.

Diziol, D., Rummel, N. & Spada, H. (2009). Procedural and Conceptual Knowledge Acquisition in Mathematics: Where is Collaboration Helpful? In C. O’Malley, D. Suthers, P. Reimann, & A. Dimitracopoulou (Eds.), Computer Supported Collaborative Learning Practices - CSCL2009 Conference Proceedings. International Society of the Learning Sciences, Inc., Volume 1, 178-187.

Diziol, D., Rummel, N. & Spada, H. (2009). Procedural and Conceptual Knowledge Acquisition in Algebra – When Does Collaboration Make a Difference? 13th European Conference for Research on Learning and Instruction (EARLI) 2009. Amsterdam, The Netherlands.

Diziol, D., Rummel, N., Kahrimanis, Guevara, Holz, Spada, H., Fiotakis (2008). Using contrasting cases to better understand the relationship between students’ interactions and their learning outcome. In G. Kanselaar, V. Jonker, P.A. Kirschner, & F. Prins, (Eds.), International perspectives of the learning sciences: Cre8ing a learning world. Proceedings of the Eighth International Conference of the Learning Sciences (ICLS 2008), Vol 3 (pp. 348-349). International Society of the Learning Sciences, Inc. ISSN 1573-4552.

Diziol, D., Rummel, N., Spada, H. (2008). Erwerb von prozeduralem und konzeptuellem Wissen in Mathematik – Wo ist kooperatives Lernen hilfreich? [Acquisition of procedural and conceptual knowledge in mathematics – When is cooperative learning beneficial?] Paper presented at the 71st conference of the "Arbeitsgemeinschaft für Empirische Pädagogische Forschung (AEPF)" [Consortium for empirical educational research]. Kiel.

Diziol, D., Rummel, N., Spada, H. & McLaren, B. (2007). Promoting learning in mathematics: script support for collaborative problem solving with the Cognitive Tutor Algebra. In C.A. Chinn, G. Erkins & S. Puntambekar (Eds.), Mice minds and society: Proceedings of the Conference on Computer Supported Collaborative Learning (CSCL-07), 8(1), 39-41.

Diziol, D., Rummel, N., Spada, H., & Haug, S. (2010). Learning in mathematics: Effects of procedural and conceptual instruction on the quality of student interaction. In K. Gomez, L. Lyons, & J. Radinsky (Eds.), Learning in the Disciplines: Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010), Vol 2 (pp. 370-371). International Society of the Learning Sciences: Chicago IL.

Doddannara, L.S., Gowda, S.M., Baker, R.S.J.d., Gowda, S.M. & de Carvalho, A.M.J.A. (2013). Exploring the relationships between design, students’ affective states, and disengaged behaviors within an ITS. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 31-40. Springer-Verlag Berlin Heidelberg.

Donmez, P., Rosé, C.P., Stegmann, K., Weingberger, A. & Fischer, F. (2005). Supporting CSCL with Automatic Corpus Analysis Technology, Proceedings of Computer Supported Collaborative Learning 2005, 1-10. (nominated for best paper award)

Duffy, B., Carney, J., & Stamper, J. (2013). A Case Study on the Gamification of Traditional Courseware, presented as part of the Industry and Innovation Track at AIED 2013. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 2013. Springer-Verlag Berlin Heidelberg.

Dyke, G., Goggins, G., Mayfield, E. & Rosé, C. P. (2013). Comparison of Network Heuristics for Understanding Small Groups in Synchronous Collaborative Learning. Proceedings of Learning Analytics and Knowledge.

Dyke, G., Howley, I., Adamson, D. & Rosé, C. (2012). Towards Academically ProductiveTalk Supported by Conversational Agents (2012). In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 531-540.

Dyke, G., Kumar, R., Ai, H. & Rosé, C. (2012). Challenging Assumptions: using sliding window visualizations to reveal time‐based irregularities in CSCL processes. ICLS2012, Vol 1, 363-370. Best Paper Nominee.

Dyke, G., Mayfield, E., Howley, I., Adamson, D. & Rosé, C. P. (2013). Analysis of Discourse and the Importance of Time. 1st International Workshop on Discourse-Centric Learning Analytics (invited paper).

Easterday, M.W. (2010). An intelligent debater for teaching argumentation. Intelligent Tutoring Systems: Lecture Notes in Computer Science 2010, Volume 6095/2010, 218-220.

Easterday, M.W., Aleven, V., Scheines, R., & Carver, S. M. (2011). Using tutors to improve educational games. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 63-71. Springer-Verlag Berlin Heidelberg.

Easterday, M.W., Kanarek, J. & Harrell, M. (2011). Design requirements of argument mapping software for teaching deliberation. Conference on Online Deliberation.

Eskenazi, M, Lin, Y. & Saz, O. (2013). Tools for non-native readers: the case for translation and simplification. Proceedings of Natural Language Processing for Improving Textual Accessibility (NLP4ITA) Workshop conducted at Language Resources and Evaluation Conference (LREC) 2013.

Evans, K.L., Karabinos, M., Leinhardt, G. & Yaron, D. (2005). Chemistry in the field and chemistry in the classroom: A disconnect? First-Year Undergraduate Chemistry Education International Conference, Urbana-Champagne, IL, May 2005.

Evans, K.L., Yaron, D. & Leinhardt, G. (2008). Learning stoichiometry: A comparison of text and multimedia formats. Paper presented at the 20th Biannual Conference on Chemical Education, Bloomington, IN (July 2008).

"Falakmasir, M., Ashley, K. & Schunn, C. (2013). Using Argument Diagramming to Improve Peer Grading of Writing Assignments. Proceedings of the 1st Workshop on Massive Open Online Courses at the 16th Annual Conference on

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Falakmasir, M.H., Pardos, Z.A., Gordon, G.J. & Brusilovsky, P. (2013). A Spectral Learning Approach to Knowledge Tracing. In D’Mello, S. K., Calvo, R. A., and Olney, A. (eds.). Proceedings of the 6th International Conference on Educational Data Mining. EDM 2013, 28-34.

Fancsali, S., Nixon, T., Vuong, A. & Ritter, S. (2013). Simulated Students, Mastery Learning, and Improved Learning Curves for Real-World Cognitive Tutors. Paper presented at AIED Workshop on Simulated Learners in conjunction with AIED 2013, July 9, 2013, Memphis, Tennessee.

Fancsali, S.E., Nixon, T. & Ritter, S. (2013). Optimal and Worst-Case Performance of Mastery Learning Assessment with Bayesian Knowledge Tracing. Proceedings of EDM 2013, 35-42.

Feeney, C.M. & Heilman, M. (2008). Automatically Generating and Validating Reading-Check Questions. Proceedings of the Ninth International Conference on Intelligent Tutoring Systems, Lecture Notes in Computer Science. Springer Berlin/Heidelberg, Volume 5091/2008, 659-661.

Feenstra, L., Aleven, V., Rummel, N. & Taatgen, N. (2010). Multiple interactive representations for fractions learning. 10th International Conference on Intelligent Tutoring systems (ITS), 221-3.

Feenstra, L., Aleven, V., Rummel, N., Rau, M. & Taatgen, N. (2011). Thinking with your Hands: Interactive Graphical Representations in a Tutor for Fractions Learning. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 453-455. Springer-Verlag Berlin Heidelberg.

Feng, M. & Heffernan, N. (2005). Informing Teachers Live about Student Learning: Reporting in Assistment System. 12th Annual Conference on Artificial Intelligence in Education Workshop on Usage Analysis in Learning Systems. 2005. Amsterdam. P25-32.

Feng, M., Beck, J., Heffernan, N. & Koedinger, K.R. (2008). Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standardized Test? 1st International Conference on Educational Data Mining, 2008. [full paper].

Feng, M., Heffernan, N. & Koedinger, K.R. (2005). Looking for Sources of Error in Predicting Student’s Knowledge. Proceedings of AAAI 2005 workshop on Educational Data Mining. 2005.

Feng, M., Heffernan, N. & Koedinger, K.R. (2006). Predicting State Test Scores Better with Intelligent Tutoring Systems: Developing Metrics to Measure Assistance Required; In the Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. p 31-40.

Fiez, J.A. (2007). Educational neuroscience: Time for a bridge? In J Geake & U Goswami (Organizers) Challenges and Opportunities for Educational Neuroscience. Workshop sponsored by the National Science Foundation, Washington, D.C.

Finkelstein, S., Yarzebinski, E., Vaughn, C., Ogan, A. & Cassell, J. (2013). The effects of culturally congruent educational technologies on student achievement. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 2013, 493-502. Springer-Verlag Berlin Heidelberg.

Forbes-Riley, K. & Litman, D.J. (2009). Adapting to Student Uncertainty Improves Tutoring Dialogues. Proceedings of the 2009 Conference on Artificial intelligence in Education: Building Learning Systems that Care: From Knowledge Representation To Affective Modelling. V. Dimitrova, R. Mizoguchi, B. du Boulay, and A. Graesser, Eds. Frontiers in Artificial Intelligence and Applications, vol. 200. IOS Press, Amsterdam, The Netherlands, 33-40.

Forbes-Riley, K., Litman, D.J. & Rotaru. M. (2008). Responding to student uncertainty during computer tutoring: An Experimental evaluation. Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Forbes-Riley, K., Litman, D.J., Purandare, A., Rotaru, M. & Tetreault, J. (2007). Comparing Linguistic Features for Modeling Learning in Computer Dialogue Tutoring. Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED), Los Angeles, CA, July, 2007.

Forbes-Riley, K., Litman, D.J., Silliman, S. & Purandare, A. (2008). Uncertainty Corpus: Resource to Study User Affect in Complex Spoken Dialogue Systems. Proceedings of the 6th Language Resources and Evaluation Conference (LREC 2008), Marrakech, Morocco, (May-June 2008).

Forbes-Riley, K., Rotaru, M., Litman, D.J. & Tetreault, J. (2007). Exploring affect-context dependencies for adaptive system development. In Proceedings of Human Language technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), 41-44, Rochester, NY. (April, 2007)

Friedline, B. & Juffs, A. (2010). L1 influences on the development of L2 morphosyntactic features. Pennsylvania Association of Applied Linguistics Consortium (PAALC) Graduate Research Symposium. State College: Pennsylvania State University. January 2010.

Frishkoff, G. (2007). ERP measures of word learning: Separation of N3, MFN, and N4 semantic components, Paper presented at the 47th Annual Meeting of the Society for Psychophysiological Research. Savannah, Georgia, October 19, 2007.

Frishkoff, G. (2007). Neural correlates of vocabulary acquisition: Evidence from dense-array EEG. Presented at the 2007 meeting of the American Educational Research Association, Chicago, IL.

Frishkoff, G. & Perfetti, C. (2007). Partial word knowledge and measures of Incremental word learning: Brain and behavioral experiments with adults and children (Ages 9 - 11). Presented at the 2007 meeting of the American Educational Research Association, Chicago, IL.

Frishkoff, G., Levin, L., Pavlik, P., Idemaru, K. & De Jong, N. (2008). A model-based approach to second-language learning of grammatical constructions. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1665-1670). Austin, TX: Cognitive Science Society.

Frishkoff, G., Pavlik, P., Levin, de Jong (2008). Providing optimal support for robust learning of syntactic constructions in ESL. Paper presented at the Annual Meeting of the Cognitive Science Society, 2008.

Frishkoff, G., Perfetti, C. (2008). ERP Evidence for stages of meaning acquisition in the development of the print lexicon. Paper presented at the 15th Annual Meeting of the Society for the Scientific Study of Reading, Asheville, NC (July 2008).

Frishkoff, G., Perfetti, C., Collins-Thompson, K. & Callan, J. (2006). Effects of Misleading Contexts on Word Learning: How Malapropisms May Affect the Development of Stable and Robust Word Representations. American Educational Research Association (2006).

Gadgil, S. & Nokes, T.J. (2009). Analogical scaffolding in collaborative learning. Proceedings of the 31st Annual Meeting of the Cognitive Science Society, 2009, 3115-3120.

Gadgil, S., Nokes, T. J., Pathcan, M., Belenky, D. & Jang, J. (2010). Collaborative facilitation through error-detection: A classroom experiment. In S. Ohlsson & R. Catrambone (Eds.). Proceedings of the 32nd Annual Conference of the Cognitive Science Society, 2583-2588. Austin, TX: Cognitive Science Society.

Gholson, B., Graesser, A. & Craig, S. (2007). The Transfer of Deep-Level Reasoning Questions and Their Effects on Science Learning. IES AERA Symposium: What Conditions Support Transfer of Knowledge? New Research in Mathematics and Science Education.

Gianfortoni, P., Adamson, D. & Rosé, C. P. (2011). Modeling Stylistic Variation in Social Media with Stretchy Patters, in Proceedings of First Workshop on Algorithms and Resources for Modeling of Dialects and Language Varieties

Gobert, J.D., Baker, R.S.J.d., Azevedo, R., Roll, I. & van Joolingen W. (2010). Symposium on qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors. In Proceedings of the International Conference of the Learning Sciences 2010.

Gobert, J.D. & Koedinger, K. (2012). Using Model-tracing to Conduct Performance Assessment of Students’ Science Inquiry Skill at Conducting Experiments Within a Microworld. Presentation in Symposium "The Future of Assessment: Measuring Science Reasoning and Inquiry Skills Using Simulations and Immersive Environments" conducted at ICLS 2012.

Gobert, J., Raziuddin, J. & Koedinger, K.R. (2013). Auto-scoring discovery and confirmation bias in interpreting data during science inquiry in a microworld. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of the 16th International Conference on Artificial Intelligence in Education, 770-773.

Goguadze, G., Sosnovsky, S., Isotani, S. & McLaren, B.M. (2011). Evaluating a Bayesian student model of decimal misconceptions. In M. Pechenizky, T. Calders, C. Conati, S. Ventura, C. Romero & J.C. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Goldin, I., Koedinger, K.R. & Aleven, V. (2012). Learner Differences in Hint Processing. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012).

Goldin, I.M. & Carlson, R. (2013). Learner Differences and Hint Content. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 2013, 522-531. Springer-Verlag Berlin Heidelberg.

Goldin, I.M., Koedinger, K.R., & Aleven, V. (2013). Hints: You can't have just one. Proceedings of EDM 2013, 232-235.

Gonzalez Sanchez, J., Chavez Echeagaray, M. E., VanLehn, K. & Burleson, W. (2011). From behavioral descriptions to a pattern-based model for intelligent tutoring systems. In Proceedings of the 18th International Conference on Pattern Languages of Programs (PLoP). ACM Press.

González-Brenes, J. P. & Mostow, J. (2012). Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models. The 5th International Conference on Educational Data Mining (EDM 2012). Chania, Greece

González-Brenes, J. P. & Mostow, J. (2012). Topical Hidden Markov Models for Skill Discovery in Tutorial Data. In NIPS 2012 Workshop on Personalizing Education With Machine Learning, Lake Tahoe, California.

González-Brenes, J. P. & Mostow, J. (2013). What and When do Students Learn? Fully Data-Driven Joint Estimation of Cognitive and Student Models. The 6th International Conference on Educational Data Mining (EDM 2013). Memphis, TN.

Gowda, S., Pardos, Z. & Baker, R.S.J.D. (2012). Content learning analysis using the moment-by-moment learning detector (2012). In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 434-443.

Gowda, S.M., Rowe, J.P., Baker, R.S.J.d., Chi, M. & Koedinger, K.R. (2011). Improving models of slipping, guessing, and momement-by-moment learning with estimates of skill difficulty. In M. Pechenizky, T. Calders, C. Conati, S. Ventura, C. Romero & J.C. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Gowda, S.M., Rowe, J.P., Baker, R.S.J.d., Chi, M., Koedinger, K.R. (2011). Improving Models of Slipping, Guessing, and Moment-by-Moment Learning with Estimates of Skill Difficulty. Proceedings of the 4th International Conference on Educational Data Mining, 199-208.

Guo, Heffernan, N., Beck, J. (2008). Trying to reduce bottom-out hinting: Will telling students how many hits they have left help? Short paper presented at the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Gweon, G., Agarwal, P., Udani, M., Raj, B. & Rosé, C. P. (2011). The Automatic Assessment of Knowledge Integration Processes in Project Teams. Proceedings of Computer Supported Collaborative Learning CSCL 2011. [Best Student Paper Award].

Gweon, G., Arguello, J., Pai, C., Carey, R., Zaiss, Z. & Rosé, C.P. (2005). Towards a Prototyping Tool for Behavior Oriented Authoring of Conversational Agents for Educational A. Proceedings of the Second Workshop for Building Educational Applications using NLP. Association for Computational Linguistics 2005.

Gweon, G., Jain, M., McDonough, J., Ray, B. & Rosé, C. (2012). Predicting Idea Co‐Construction in Speech Data using Insights from Sociolinguistics. Proceedings of ICLS2012, Vol 1, 435-442.

Gweon, G., Kane, A., Rosé, C. P. (2011). Facilitating knowledge transfer between groups through idea co-construction processes. In Proceedings of INGroup ‘11

Gweon, G., Rosé, C.P., Albright, E. & Cu, Y. (2007). Evaluating the Effect of Feedback from a CSCL Problem Solving Environment on Learning, Interaction, and Perceived Interdependence. Proceedings of the Conference on Computer Supported Collaborative Learning (CSCL-07). Rutgers University.

Gweon, G., Rosé, C.P., Wittwer, J. & Nueckles, M. (2005). Supporting Efficient and Reliable Content Analysis Using Automatic Text Processing Technology, Proceedings of Interact ’05 (short paper) Pp 1112

Gweon, G., Rosé, C.P., Zaiss, Z. & Carey. R. (2006). Providing Support for Adaptive Scripting in an On-Line Collaborative Learning Environment, Proceedings of CHI 06: ACM conference on human factors in computer systems. New York: ACM Press. (nominated for a best paper award)

Harpstead, E., MacLellan, C.J., Koedinger, K.R., Aleven, V., Dow, S.P. & Myers, B.A. (2013). Investigating the Solution Space of an Open-Ended Educational Game Using Conceptual Feature Extraction. Proceedings of EDM 2013, 51-58.

Harrer, A., McLaren, B., Walker, E., Bollen, L. & Sewall, J. (2005). Collaboration and Cognitive Tutoring: Integration, Empirical Results, and Future Directions. 12th International Conference on Artificial Intelligence in Education; Amsterdam, the Netherlands. July 2005.

Harrer, A., Pinkwart, N., McLaren, B. & Scheuer, O. (2008). How Do We Get the Pieces to Work Together? A New Software Architecture to Support Interoperability between Educational Software Tools. In B. Woolf, E. Aimeur, R. Nkambou, S. Lajoie (Eds), Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS-08), Lecture Notes in Computer Science, 5091 (pp. 715-718). Berlin: Springer.

Hatfield, D. & Juffs, A. (2013). Refugee Policy and Language Learning in Pittsburgh, PA. Low Educated Second Language and Literacy Acquisition (LESLLA) Symposium, 2013. San Francisco.

Hausmann, R.G.M. (2006). Why do elaborative dialogs lead to effective problem solving and deep learning? In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Meeting of the Cognitive Science Society (pp.1465-1469). Alpha, NJ: Sheridan Printing.

Hausmann, R.G.M. (2007). An analysis of generative dialogue patterns across interactive learning environments: Explanation, elaboration, and co-construction. Paper presented at the Intelligent Tutoring in Serious Games Workshop, hosted by the Institute for Creative Technologies at USC, Marina del Rey, CA.

Hausmann, R.G.M., Nokes, T.J., VanLehn, K. & Gershman, S. (2009). The impact of prompting on self-explanation and robust learning. Symposium at European Association for Research on Learning and Instruction (EARLI, 2009).

Hausmann, R.G.M. & van de Sande, B. (2007). An Analysis of Student Learning Using the Andes Intelligent Tutor Homework System. Paper presented at the summer meeting of the American Association of Physics Teachers, Greensboro, NC. August 2007.

Hausmann, R.G.M. & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K.R. Koedinger, K.R. & J. Greer (Eds.), Artificial Intelligence in Education: Building technology rich learning contexts that work (Vol 158, pp. 417-424). Amsterdam: IOS Press. [Best Paper Award]

Hausmann, R.G.M. & VanLehn, K. (2007). Self-explaining in the Classroom: Learning Curve Evidence. Proceedings of the 29th Annual Conference of the Cognitive Science Society. 1067-1072. Austin, TX: Cognitive Science Society

Hausmann, R.G.M., Nokes, T.J., VanLehn, K. & Gershman, S. (2009). Revising models or filling gaps? The impact of prompting on self-explanation and robust learning. Presented as part of "In Vivo Experimentation on Self-Explanations Across Domains" Symposium conducted at 13th Biennial European Association for Research on Learning and Instruction Conference (EARLI). Amsterdam, Netherlands, 2009.

Hausmann, R.G.M., Nokes, T.J., VanLehn, K. & Gershman, S. (2009). The design of Self-explanation prompts: The fit hypothesis. Proceedings of the 31st Annual Meeting of the Cognitive Science Society, 2626-2631.

Hausmann, R.G.M., van de Sande, B. & VanLehn, K. (2008). Are self-explaining and coached problem solving more effective when done by pairs of students than alone? In B. C. Love, K. McRae & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pp. 2369-2374). Austin, TX: Cognitive Science Society.

Hausmann, R.G.M., van de Sande, B. & VanLehn, K. (2008). Trialog: How peer collaboration helps remediate errors in an ITS. Proceedings of the 21st International FLAIRS Conference, (pp. 415-420), Menlo Park: CA, AAAI Press.

Hausmann, R.G.M., van de Sande, B. & VanLehn, K. (2008). Shall we explain? Augmenting learning from intelligent tutoring systems and peer collaboration. In B. P. Woolf, E. Aimeur, R. Nkambou & S. Lajoie (eds). Intelligent Tutoring Systems: 9th International Conference, ITS2008, pp. 636-645. Amsterdam: IOS Press.

Hausmann, R.G.M., van de Sande, B., van de Sande, C. & VanLehn, K. (2008). Productive Dialogue During Collaborative Problem Solving. In P.A. Kirschner, F. Prins, V. Jonker, & G. Kanselaar (Eds.), Proceedings of the International Conference for the Learning Sciences -- ICLS 2008 (Vol. 1, pp. 327-334). The Netherlands: ISLS.

Heilman, M. & Eskenazi, M. (2006). Language Learning: Challenges for Intelligent Tutoring Systems. Proceedings of the Workshop of Intelligent Tutoring Systems for Ill-Defined Domains. 8th International Conference on Intelligent Tutoring Systems. June 2006, pp 20-28.

Heilman, M. & Eskenazi, M. (2007). Application of automatic thesaurus extraction for computer generation of vocabulary questions. Proceedings of the SLaTE2007 Workshop on Speech and Language Technology in Education, Farmington, PA (October 2007).

Heilman, M. & Feeney, C. (2008). Automatically generating and validating reading-check questions. In B. Woolf et al (Eds.): ITS 2008, LNCS 5091. Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS). Springer-Verlag Berlin Heidelberg, 659-661.

Heilman, M., Collins-Thompson, K., Callan, J. & Eskenazi, M. (2006). Classroom success of an intelligent tutoring system for lexical practice and reading comprehension. Proceedings of the 9th International Conference on Spoken Language Processing (ICSLP)

Heilman, M., Collins-Thompson, K., Callan, J. & Eskenazi, M. (2007). Combining lexical and grammatical features to improve readability measures for first and second language texts. Proceedings of the Human Language Technology Conference. Rochester, NY, (2007).

Heilman, M., Collins-Thompson, K., Eskenazi, M. (2008). An Analysis of Statistical Models and Features for Reading Difficulty Prediction. Proceedings of the 3rd Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, 71-79.

Heilman, M., Juffs, A. & Eskenazi, M. (2007). Choosing reading passages for vocabulary learning by topic to increase intrinsic motivation. Proceedings of the 13th International Conference on Artificial Intelligence in Education. Marina del Rey, CA., 2007

Heilman, M., Zhao, L., Pino, J. & Eskenazi, M. (2008). Retrieval of Reading Materials for Vocabulary and Reading Practice. The 3rd Workshop on Innovative Use of NLP for Building Educational Applications. Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Columbus OH, 2008.

Heiner, C., Beck, J. & Mostow, J. (2006). Automated Vocabulary Instruction in a Reading Tutor. In M, Ikeda, K. Ashley, & T-W. Chan (Eds.); ITS-2006, LNCS 4053, pp 741-743. Springer-Verlag Berlin Heidelberg 2006.

Hershkovitz, A., Baker, R.S.J.d., Gowda, S.M. & Corbett, A.T. (2013). Predicting Future Learning Better Using Quantitative Analysis of Moment-by-Moment Learning. Proceedings of EDM 2013, 74-81.

Howley, I. & Rosé, C. P. (2010). Student Dispositions and Help-Seeking in Collaborative Learning. Proceedings of Intelligent Tutoring Systems, Young Researcher’s Track/Doctoral Consortium.

Howley, I. & Rosé, C. P. (2010). Student Dispositions and Help-Seeking in Collaborative Learning. Young Researcher's Track paper presented at the Tenth International Conference on Intelligent Tutoring Systems (ITS), 2010.

Howley, I. & Rosé, C. P. (2011). Modeling the Rhetoric of Human-Computer Interaction. In J.A. Jacko (Ed.). Human-Computer Interaction: Interaction Techniques and Environments. Proceedings of the 14th International Conference, HCI International 2011, Vol. 6762/2011, 341-350.

Howley, I., Adamson, D., Dyke, G., Mayfield, E., Beuth, J. & Rosé, C.P. (2012). Group Composition and Intelligent Dialogue Tutors for Impacting Students’ Academic Self-Efficacy. In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 551-556.

Howley, I., Mayfield, E. & Rosé, C. P. (2011). Missing Something? Authority in Collaborative Learning. Proceedings of Computer Supported Collaborative Learning.

Hu, W., Wu, S., Zhang, A. & Cai, J. (2007). Bridging between classical and modern Chinese. Panel participants at the American Council on the Teaching of Foreign Languages (ACTFL) Annual Meeting, 2007.

Hua, A., Sionti, M., Wang, Y.C. & Rosé, C.P. (2010). Finding Transactive Contributions in Whole Group Classroom Discussions. Proceedings of the International Conference of the Learning Sciences 2010.

Isotani, S., McLaren, B.M., & Altman, M. (2010). Towards Intelligent Tutoring with Erroneous Examples: A Taxonomy of Decimal Misconceptions. Proceedings of Intelligent Tutoring Systems (ITS), 346-348.

Jain, M., McDonogh, J., Gweon, G., Raj, B., Rosé, C. P. (2012). An Unsupervised Dynamic Bayesian Network Approach to Measuring Speech Style Accommodation. In Proceedings of the European Association for Computational Linguistics (15% acceptance rate for oral presentations)

Johnson, M.W., Eagle, M., Stamper, J. & Barnes, T. (2013). An Algorithm for Reducing the Complexity of Interaction Networks. Proceedings of EDM 2013, 248-251.

Jones, C. (2007). French Online and the Open Learning Initiative. Kentucky Foreign Language Conference, April 2007, Lexington, Kentucky.

Jones, C. & Queuniet, S. C. (2006). French Online and the French LearnLab: Instruction and Research. European Computer Assisted Language Learning 2006.

Jones, C. & Siskin M. (2007). Building the New French Online: The Challenges of shared infrastructure. CALICO (Computer-Assisted Language Instruction Consortium), May 2007, Texas State University, San Marcos.

Jordan, P. (2004). Using Student Explanations as Models for Adapting Tutorial Dialogue. Proceedings of 17th International FLAIRS Conference. P905-910.

Jordan, P. (2007). Topic initiative in a simulated peer dialogue agent. Proceedings of the 13th International Conference on Artificial Intelligence in Education, (AIED), Marina del Ray, CA (July, 2007).

Jordan, P. & Litman, D.J. (2008). Minimal feedback during tutorial dialogue. Short paper presented at the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Jordan, P. & VanLehn, K. (2006). Discourse Processing for Explanatory Essays in Tutorial Applications. Proceedings of the 3rd SIGdial Workshop on Discourse and Dialogue, Vol. 2, from the Annual Meeting of the ACL, pp 74-83

Jordan, P., Albacete, VanLehn, K. (2005). Taking Control of Redundancy in Scripted Tutorial Dialogue. Proceedings of Int. Conference on Artificial Intelligence in Education, pp. 314 - 321.

Jordan, P., Hall, B. Ringenberg, M., Cue, Y. & Rosé, C.P. (2007). Tools for authoring a dialogue agent that participates in learning studies. Proceedings of the 13th International Conference on Artificial Intelligence in Education. Marina del Rey, CA. (July 2007).

Jordan, P., Litman, D.J., Lipschultz, M. & Drummond, J. (2009). Evidence of Misunderstandings in Tutorial Dialogue and their Impact on Learning. Proceedings of the 14th International Conference on Artificial Intelligence in Education (AIED), Brighton, UK, July 2009.

Jordan, P., Makatchev, M. & VanLehn, K. (2004). Combining Competing Language Understanding Approaches in an Intelligent Tutoring System. Proceedings of Intelligent Tutoring Systems Conference, vol 3220, pp 346-357.

Jordan, P., Makatchev, M., Pappuswamy, U., VanLehn, K. & Albacete, P. (2006). A natural language tutorial dialogue system for physics. In G. Sutcliffe & R. Goebel (Eds.), 19th International FLAIRS Conference. Menlo Park, CA: AAAI Press. P 521-526.

Jordan, P., Ringenberg, R. & Hall, B. (2006). Rapidly Developing Dialogue Systems that Support Learning Studies. Workshop Proceedings on Teaching With Robots, Agents, and NLPat, 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan

Juffs, A. (2012). Functional and Formal Approaches to SLA. Colloquium conducted at the 31st Second Language Research Forum Conference (SLRF). Pittsburgh, PA.

Juffs, A. & Rodriguez (2007). Working memory capacity in context: differential effects on comprehension of relative clauses and binding. Second Language Research Forum. University of Illinois, Champaign Urbana. October 13, 2007.

Juffs, A. & Rodriguez, G. A. (2012). Processing relative clauses and working memory. Georgetown University Roundtable on Linguistics. March 9, 2012.

Juffs, A., Eskenazi, M., Heilman, M., Wilson, L. & Friedline, B. (2007). Activity theory and computer assisted learning of English vocabulary. Proceedings of the American Association for Applied Linguistics, 2007.

Juffs, A., Eskenazi, M., Wilson, L., Pelletreau, T., Sanders, J., Callan, J. & Brown, J. (2006). Promoting robust learning of vocabulary through computer assisted language learning, Joint conference of AAAL and ACLA/CAAL 2006, Montreal, June 2006.

Juffs, A., Petrich, J. & Han, N. (2013). Tracking the development of lexical diversity in Intensive English Program Students in the US. American Association of Applied Linguistics. Houston, 2013.

Juffs, A., Petrich, J. & Han, N. (2013). Tracking the development of lexical diversity in Intensive English Program Students in the US. American Association of Applied Linguistics. Dallas, TX, 2013.

Juffs, A., Wilson, L., Eskenazi, M. & Heilman, M. (2008). Robust learning of vocabulary in classrooms and in CALL. Paper presented at the American Association of Applied Linguistics, Washington, DC.

Kallai, A. Y., Schunn, C. D., & Fiez J. A. (2011). Improving foundational number representations through simple arithmetical training. Paper presened at The Society for Research on Educational effectiveness (SREE) Fall 2011 Conference, Washington, D.C.

Kao, Y., Roll, I. & Koedinger, K.R. (2007). The composition effect in geometry area problems. Proceedings of the Twenty-Ninth Meeting of the Cognitive Science Society, CogSci 2007, 1145-1150.

Kao, Y., Roll, I. & Koedinger, K.R. (2007). Source of difficulty in multi-step geometry area problems. In D.S. McNamara & J.G. Trafton (Eds.). Proceedings of the 29th Annual Meeting of the Cognitive Science Society, (1145-1150). Austin TX: Cognitive Science Society

Kao, Y.S. & Anderson, J.R. (2008). Contributions of spatial skills to geometry achievement. Paper presented at Conference on Research and Training in Spatial Intelligence, Evanston, IL

Kao, Y.S. & Anderson, J.R. (2009). Contributions of Spatial Skills to Geometry Achievement II. Paper presented at Conference on Research and Training in Spatial Intelligence, Evanston, IL.

Katz, S., Connelly, J. & Wilson, C. (2005). When should dialogues in a scaffolded learning environment take place? In P. Kommers & G. Richards (Eds.), Proceedings of EdMedia 2005 (pp. 2850-2855). Norfolk: VA: AACE.

Katz, S., Connelly, J. & Wilson, C. (2007). Out of the lab and into the classroom: An evaluation of reflective dialogue in Andes. In K. Koedinger, K.R. & R. Luckin (Eds). In Proceedings of Artificial Intelligence in Education: Building Technology Rich Learning Contexts that Work (pp. 425-432). Amsterdom: IOS Press.

King, S.O., Stein, M., Schunn, C.D. & Boston, M.D. (2012). Designing Educative Teacher Guides for Informal Learning. Paper presented as part of "Developing and Studying Educative Science and Mathematics Curriculum Materials" Symposium at AERA 2012.

Klahr, D. (2007). Learning & Development, Primary & Secondary Processes, Instruction & Learning. Invited Presidential Symposium, Cognitive Development Society Biennial Meeting. Santa Fe, NM. October, 2007.

Klahr, D. & Chen Z. (2007). Remote Transfer of Scientific Reasoning and Problem-Solving Strategies in Children and Adults. Presentation at Symposium on Learning and Transfer: Application of Developmental Psychology Research to Educational Issues. SRCD 2007 Biennial Meeting. Boston, MA March 2007

Koedinger, K.R. (2009). Fostering Learning in the Networked World: Presenting the 21st-Century Cyber Learning Opportunity and Challenge for the National Science Foundation. SIG-Advanced Technologies for Learning. AERA symposium, 2009.

Koedinger, K.R. (2009). Using online tutoring systems for in vivo experimentation and educational data mining. Presented as part of "Innovative Methodologies for Relevant Basic Research on Technology-Enhanced Learning in Classrooms" Symposium at EARLI 2009.

Koedinger, K.R. (2008). Confronting the Assistance Dilemma: Is it Better to Give Than to Receive? Learning and Instruction Symposium (AERA 2008).

Koedinger, K.R, Pavlik, P., Stamper, J., Nixon, T. & Ritter S. (2011). Avoiding Problem Selection Thrashing with Conjunctive Knowledge Tracing. Proceedings of Educational Data Mining (EDM 2011).

Koedinger, K.R. (2006). Cognitive Tutors and Opportunities for Convergence of Human and Machine Learning Theory. AAAI 2006.

Koedinger, K.R. (2007). Enabling technologies from the Pittsburgh Science of Learning Center. Presented at the 2007 meeting of the American Educational Research Association, Chicago, IL.

Koedinger, K.R. (2012). Crowdsourcing Cognitive Models for Assessment, Tutoring, and In-Game Support. Paper presented at Microsoft Research at University of Washington (MSR/UW) Summer Institute on Crowdsourcing Personalized Online Education, July 2012

Koedinger, K.R. & Baker, R.S.J.d. (2006). Comparing Knowledge Representations and Methods for Creating Cognitive Models in Advanced Learning and Tutorial Systems. American Educational Research Association (2006).

Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.). Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pp. 471-476.) Austin, TX: Cognitive Science Society.

Koedinger, K.R., Aleven, V. & Baker, R.S.J.d. (2007). In vivo experiments on whether tutoring meta-cognition yields robust learning. Presented at the 2007 meeting of the American Educational Research Association, Chicago, IL.

Koedinger, K.R., Aleven, V. & Baker, R.S.J.d. (2007). In vivo experiments on whether tutoring meta-cognition yields robust learning. Paper presented at the 12th Biennial Conference for Research on Learning and Instruction (EARLI). Budapest, Hungary, August, 2007.

Koedinger, K.R., Aleven, V., Baker, R.S.J.d. & Roll, I. (2007). Toward understanding when tutoring meta-cognition enhances domain learning. Proceedings of Workshop on Metacognition and SRL. (AIED 2007).

Koedinger, K.R., Aleven, V., Heffernan, N., McLaren, B. & Hockenberry, M. (2004). Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration;. In the Proceedings of the Seventh International Conference on Intelligent Tutoring Systems (ITS-2004), Maceio, Brazil, August 2004.

Koedinger, K. R., Booth, J. L., & Klahr, D. (2013). Instructional complexity and the science to constrain it. Science, 342(6161), 935-937.

Koedinger, K.R., Cunningham, Skogsholm, Leber (2008). An open repository and analysis tools for fine-grained, longitudinal learner data. Proceedings of the 1st International Conference on Educational Data Mining, 2008. [full paper], 157-166.

Koedinger, K.R., K. & Stamper, J. (2010). A Data Driven Approach to the Discovery of Better Cognitive Models. In Baker, R.S.J.d., Merceron, A., Pavlik, P.I. Jr. (Eds.) Proceedings of the 3rd International Conference on Educational Data Mining (EDM 2010), 325-326.

Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated Student Model Improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.). Proceedings of the 5th International Conference on Educational Data Mining. [Best Paper Award]

Koedinger, K.R., Pavlik, P., McLaren, B. & Aleven, V. (2008). Is it Better to Give than to Receive? The Assistance Dilemma as a Fundamental Unsolved Problem in the Cognitive Science of Learning and Instruction. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 2155-2160). Austin, TX: Cognitive Science Society.

Koedinger, K.R., Stamper, J., Mclaughlin, E. & Nixon, T. (2013). Using data-driven discovery of better student models to improve student learning. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 421-430. Springer-Verlag Berlin Heidelberg.

Kulkarni, A. & Callan, J. (2008). Dictionary Definitions based Homograph Identification using a Generative Hierarchical Model. Proceedings of ACL-08: HLT, Short Papers (Companion Volume), 85-88, Columbus, OH, June 2008. Association for Computational Linguistics.

Kulkarni, A., Callan, J. & Eskenazi, M. (2007). Dictionary definitions: The Likes and the unlikes. Proceedings of the SLaTE2007 Workshop on Speech and Language Technology in Education, Farmington, PA (October 2007).

Kulkarni, A., Heilman, M., Eskenazi, M. & Callan, J. (2008). Word Sense Disambiguation for Vocabulary Learning. Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS 2008), Lecture Notes in Computer Science, Vol 5091, pp 500-509. Springer-Verlag: Berlin, Heidelberg.

Kulkarni, R., Tushar, S., Trivedi, G., Wen, M., Zheng, Z., & Rosé, C. P. (2012). Supporting Collaboration in Wikipedia between Language Communities, Proceedings of the 4th ACM International Conference on Intercultural Collaboration

Kumar, R. & Rosé, C. P. (2011). Comparing Triggering Policies for Social Behaviors. Proceedings of SIGDIAL 2011.

Kumar, R., Beuth, J. & Rosé, C. P. (2011). Conversational Strategies that Support Idea Generation Productivity in Groups. Proceedings of Computer Supported Collaborative Learning

Kumar, R., Gweon, G., Joshi, M., Cui, Y. & Rosé, C.P. (2007). Supporting students working together on math with social dialogue. Paper presented at the SLaTE2007 Workshop on Speech and Language Technology in Education, Farmington, PA (October 2007).

Kumar, R., Rosé, C.P., Aleven, V., Iglesias, A., & Robinson, A. (2006). Evaluating the Effectiveness of Tutorial Dialogue Instruction in an Exploratory Learning Context; Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. p666-674.

Kumar, R., Rosé, C.P., Wang, Y., Joshi, M. & Robinson, A. (2007). Tutorial Dialogue as adaptive collaborative learning support, AIED 2007 (nominated for a best paper award).

Lane, H.C. & VanLehn, K. (2004). A dialogue-based tutoring system for beginning programming. In V. Barr & Z. Markov (Eds.), Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS) (pp. 449-454). Menlo Park, CA: AAAI Press.

Lane, H.C. & VanLehn, K. (2005). Intention-based scoring: An approach to measuring success at solving the composition problem. In W. Dann, P. T. Tymann, & D. Baldwin (Eds.), Proceedings of the 36th ACM Technical Symposium on Computer Science Education (SIGCSE).: ACM Press. P373-374

Lange, K.E., Booth, J.L., Koedinger, K.R., & Jones Newton, K. (2012). Differentiating Between Correct and Incorrect Examples for Improving Student Learning in Algebra. Poster presented at AERA 2013.

Lau, M., Stein, M, Reynolds, B., Schunn, C.D., Ruppel, R., Cox, C. & Bender, S. (2012). Educative or Not: How Teachers’ Framing of Activities Impacts Their Learning From Curricular Materials. Paper presented as part of "Developing and Studying Educative Science and Mathematics Curriculum Materials" Symposium at AERA 2012.

Lee, D.M., Rodrigo, M.M., Baker, R.S.J.d., Sugay, J. & Coronel, A. (2011). Exploring the Relationship Between Novice Programmer Confusion and Achievement. Proceedings of the 4th bi-annual International Conference on Affective Computing and Intelligent Interaction.

Lee, J.I. & Brunskill, E. (2012). The Impact on Individualizing Student Models on Necessary Practice Opportunities. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012).

Lee, J-K. & Lee, J-H. (2006). The effect of learning management system quality and self-regulated learning strategy on effectiveness of an e-Learning. E-Learning Conference, 2006, page 8.

Li, J., Klahr, D. & Jabbour, A. (2006). When the Rubber Meets the Road -- Putting Research-based Methods to Test in Urban Classrooms. International Conference of the Learning Sciences (ICLS 2006). Bloomington, IN, USA. P. 418.

Li, N., Cohen, W. & Koedinger, K. (2013). Discovering Student Models with a Clustering Algorithm Using Problem Content. Proceedings of EDM 2013, 98-105.

Li, N., Cohen, W., & Koedinger, K.R., K. (2010). A computational model of accelerated future learning through feature recognition. In V. Aleven, J. Kay & J. Mostow (Eds.). Proceedings of the Tenth International Conference on Intelligent Tutoring Systems (ITS). LNCS Volume 6095, 368-370. Springer.

Li, N., Cohen, W., Koedinger, K.R., K., & Matsuda, N. (2010). Towards a computational model of why some students learn faster than others. Proceedings of the AAAI 2010 Fall Symposium on the Cognitive and Metacognitive Educational Systems. Arlington, VA.

Li, N., Cohen, W.W. & Koedinger, K.R. (2012). Efficient Cross-Domain Learning of ComplexSkills. In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 493-498.

Li, N., Cohen, W.W. & Koedinger, K.R. (2012). Problem Order Implications for LearningTransfer. In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 185-194.

Li, N., Khandelwal, A., Phan, T., Touretzky, D.S., Cohen, W.W., & Koedinger, K.R. (2013). Creating an Educational Robot by Embedding a Learning Agent in the Physical World. Proceedings of the 44th ACM technical Symposium on Computer Science Dducation. 759-760, SIGCSE 2013. (abstract).

Li, N., Matsuda, N., Cohen, W.W. & Koedinger, K. (2011). A Machine learning approach for automatic student model discovery. In M. Pechenizky, T. Calders, C. Conati, S. Ventura, C. Romero & J.C. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Li, N., Stampfer, E., Cohen, & Koedinger, K.R. (2013). General and efficient cognitive model discovery using a simulated student. In M. Knauff, N. Sebanz, M. Pauen & I wachsmuth (Eds.). Proceedings of the 35th Annual Conference of the Cognitive Science Society. Cognitive Science Society: Austin, TX., 894-899.

Li, N., Tian, Y., Cohen, W.W., & Koedinger, K.R. (2013). Integrating Perceptual Learning with External World Knowledge in a Simulated Student. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 2013, 400-410. Springer-Verlag Berlin Heidelberg.

Litman, D. (2011). Spoken dialogue for intelligent tutoring systems: Responding to not only what students say, but how they say it. Paper presented at Socializing Intelligence Through Academic Talk and Dialogue: Invitational AERA Research Conference. University of Pittsburgh, September 22-25, 2011.

Litman, D.J., Rosé, C.P., Forbes-Riley, K., VanLehn, K., Bhembe, D. & Silliman, S. (2004). Spoken versus typed human and computer dialogue tutoring. In J. C. Lester, R. M. Vicari, & F. Paraguacu, (Eds.), Intelligent Tutoring Systems: 7th International Conference (pp. 368-379). Berlin: Springer-Verlag Berlin & Heidelberg GmbH & Co. K

Liu, Y. (2009). Chinese ESL Readers’ On-line Inferences in Text Processing. Paper presented at the American Association for Applied Linguistics Conference, March, 2009.

Liu, Y., Massaro, D.W., Chen, T.H., Chan, D. & Perfetti, C. (2007). Using visual speech for training Chinese pronounciation: An in-vivo experiment. Proceedings of the SLaTE2007 Workshop on Speech and Language Technology in Education, Farmington, PA (October 2007).

Liu, Y., Wang, M., Perfetti, C., Brubaker, B., Wu, S. & MacWhinney, B. (2006). Learning a tonal language by attending to the tone. 13th annual meeting of Society for the Scientific Study of Reading, Vancouver, July 5-8, 2006.

Liu, Y., Wang, M., Perfetti, C., Brubaker, B., Wu, S. & MacWhinney, B. (2007). Learning a tonal language by attending to the tone: An in-vivo experiment. Paper presented at the 12th Biennial Conference for Research on Learning and Instruction, EARLI 2007, Aug 2007. Budapest, Hungary. Symposium title: Understanding robust learning via in vivo experimentation.

Liu, Z., Pataranutaporn, V., Ocumpaugh, J., & Baker, R.S.J.d. (2013). Sequences of Frustration and Confusion, and Learning. Proceedings of EDM 2013, 113-120.

Lloyd, N.M., Heffernan, N. & Ruiz, C. (2007). Predicting student engagement in intelligent tutoring systems using teacher expert knowledge. Proceedings of Workshop on Educational Data Mining (AIED 2007) 40-49.

Lomas, J.D., Ching, S., Stampfer, E., Sandoval, M. & Koedinger, K.R. (2012). Battleship Numberline: A Digital Game for Improving EstimationAccuracy on Fraction Number Lines. Paper presented at AERA 2012.

Long, Y. & Aleven V. (2011). Students' understanding of their student model. Artificial Intelligence in Education (AIED), Lecture Notes in Computer Science, 2011, Volume 6738, 179-186.

Long, Y. & Aleven, V. (2012). Skill Diaries: Can Periodic Self-Assessment Improve Students’ Learning with an Intelligent Tutoring System? In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 673-674.

Long, Y. & Aleven, V. (2013). Learning with an Open Learner Model in a Linear Equation Tutor. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 219-228. Springer-Verlag Berlin Heidelberg.

Long, Y. & Aleven, V. (2013). Skill Diaries: Improve Student Learning in an Intelligent Tutoring System with Periodic Self-Assessment. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 249-258. Springer-Verlag Berlin Heidelberg. [Awarded Best Student Paper].

Lu, C. (2006). The Effects of Word Knowledge Depth and Proficiency Level on Word Association for Learners of Chinese as a Second Language, The Annual Meeting of Chinese Language Teachers Association (CLTA/ ACTFL), 2006.

Lynch, C., Ashley, K., Aleven, V. & Pinkwart, N. (2006). Defining Ill-Defined Domains; A literature survey. Workshop Proceedings on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems, 2006

Lynch, C., Ashley, K., Pinkwart, N. & Aleven, V. (2007). Argument diagramming as focusing device: does it scaffold reading? Proceedings of Workshop on Applications in Ill-Defined Domains (AIED 2007).

Lynch, C., Ashley, K., Pinkwart, N. & Aleven, V. (2008). Argument graph classification with Genetic Programming and C4.5. 1st International Conference on Educational Data Mining, 2008. [full paper].

Maass, J.K. & Pavlik, P.I. (2013). Using Learner Modeling to Determine Effective Conditions of Learning for Optimal Transfer. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 2013, 189-198. Springer-Verlag Berlin Heidelberg

MacLellan, C.J., Matsuda, N. & Koedinger, K.R. (2013). Toward a reflective SimStudent: Using experience to avoid generalization errors. Paper presented at the AIED Workshop on Simulated Learners. AIED 2013.

MacWhinney, B. (2005). Item-based Constructions and the Logical Problem. Proceedings of the Second Workshop on Psychocomputational Models of Human Language Acquisition. 2005. Pages 53-68.

MacWhinney, B., Presson, N. & Heilman, M. (2010). Embodied spatial language in L2 acquisition. Presented at the 'Toward Embodied Language Learning' Colloquium at the Second Language Research Forum (SLRF), University of Maryland, October 2010.

Magner, U., Schwonke, R., Renkl, A., & Aleven, V. (2010). Pictorial illustrations in intelligent tutoring systems: Do they distract or elicit interest and engagement? In K. Gomez, L. Lyons, & J. Radinsky, J. (Eds.), Learning in the Disciplines: Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010) - Volume 1, Full Papers. International Society of the Learning Sciences: Chicago IL.

Magner, U., Schwonke, R., Renkl, A., Aleven, V., & Popescu, O. (2010). Seductive illustrations: Double-edged effects? In M. Hopp & F. Wagner (Eds.), Instructional design for motivated and competent learning in a digital world (Proceedings of the EARLI SIG 6&7 Conference 2010) (pp. 161-163). Ulm, Germany: University of Ulm.

Makatchev, M. & VanLehn, K. (2005). Analyzing completeness and correctness of utterances using an ATMS. In G. McCalla, C. K. Looi, B. Bredeweg & J. Breuker (Eds.), Proceedings of the International Conference on Artificial Intelligence in Education, AIED2005, (pp. 403-410). Amsterdam, Netherlands: IOS Press.

Makatchev, M. & VanLehn, K. (2007). Combining Bayesian networks and formal reasoning for semantic classification of student utterances. Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED-07).

Makatchev, M., Hall, B.S., Jordan, P. W., Pappuswamy, U. & VanLehn, K. (2005). Mixed language processing in the Why2-Atlas tutoring system. Proceedings of the Workshop on Mixed Language Explanations in Learning Environments, AIED2005. Amsterdam, Netherlands

Makatchev, M., Jordan, P. & VanLehn, K. (2004). Modeling student’s reasoning about qualitative physics: Heuristics for abductive proof search. In J. C. Lester, R. M. Vicari, & F. Paraguacu, (Eds.), Intelligent Tutoring Systems: 7th International Conference (pp. 699-709). Berlin: Springer-Verlag Berlin & Heidelberg GmbH & Co. K.

Makatchev, M., Jordan, P., Pappuswamy, U., & VanLehn, K. (2004). Abductive proofs as models of qualitative reasoning. In J. de Kleer & K. Forbus (Eds.), Proceedings of Workshop on Qualitative Reasoning (pp. 11-18). Evanston, IL .

Makatchev, M., Jordan, P., Pappuswamy, U., & VanLehn, K. (2004). Abductive proofs as models of students’ reasoning about qualitative physics. In Sixth International Conference on Cognitive Modeling (pp. 166-171). Mahwah, NJ: Erlbaum.

Makatchev, M., VanLehn, K., Jordan, P. & Pappuswamy, U. (2006). Representation and reasoning for deeper natural language understanding in a physics tutoring system. In G. Sutcliffe & R. Goebel (Eds.), Proceedings of the 19th International FLAIRS conference. Menlo Park, CA: AAAI Press, 682-687.

Martin, B., Koedinger, K.R., Mitrovic, A. & Mathan S. (2005). On Using Learning Curves to Evaluate ITS . Proceedings of the 12th International Conference on Artificial Intelligence in Education. 2005.

Martin, K. & Juffs, A. (2011). Reading in English: A Comparison of Native Arabic, Native Chinese, and native English speakers. Poster. International Symposium on Bilingualism 8 (ISB8). 15th – 18th June 2011

Martin, K. I., & Juffs, A. (2012). Reading in English: A Comparison of Native Arabic, Native Chinese, and Native English Speakers. Paper presented at Second Language Research Forum, Iowa State University, Ames, IA.

Martin, K. I., & Juffs, A. (2012). The Effects of L1 on Sensitivity to Vowel Information while Reading: A Comparison of Native Arabic, Native Chinese, and Native English Speakers. In L. M. Morett (Chair), The Psycholinguistic Bases of Second Language Acquisition: Consistency and Change Across Languages. Symposium conducted at the meeting of the Eastern Psychological Association, Pittsburgh, PA.

Massaro, D.W., Liu, Y., Chen, T.H. & Perfetti, C. (2006). A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. Proceedings of the 9th International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP), September, Pittsburgh, PA. 825-828.

Mathan, S. & Koedinger, K.R. (2006). Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring. American Educational Research Association

Matlen, B.J., Atit, K., Göksun, T., Rau, M.A., & Ptouchkina, M. (2012). Representing space: Exploring the relationship between gesturing and children’s geoscience understanding. In K. Schill, C. Stachniss, D. Uttal (Eds.), Proceedings of Spatial Cognition, LNAI 7643, pp. 405 – 415. Springer, Heidelberg.

Matlen, B.J., Shipley, T.F., Chaurasia, N., Wilson, M.L., Wilson, D.L., & Klahr, D. (2013). A comparison of comparison types: Applications of analogical instruction in mineralogy identification. Submitted to the Conference of the American Education Research Association (AERA 2013).

Matsuda, N. (2006). Building Robust Learning Theories for Robust Learning (2006). International Symposium on e-Learning, Osaka Prefecture University, May 2006, Osaka, Japan

Matsuda, N. & VanLehn, K. (2005). Advanced geometry tutor: An intelligent tutor that teaches proof-writing with construction. In G. McCalla, C. K. Looi, B. Bredeweg & J. Breuker (Eds.), Artificial Intelligence in Education (pp.443-450). Amsterdam: IOS Press.

Matsuda, N. & VanLehn, K. (2005). Advanced Geometry Tutor: An Intelligent Tutoring System for Proof-Writing with Construction. Proceedings of Japan National Conference on Information and Systems in Education. 2005.

Matsuda, N., Cohen, W. & Koedinger, K.R. (2005). Applying Programming by Demonstration in an Intelligent Authoring Tool for Cognitive Tutors. AAAI Workshop on Human Comprehensible Machine Learning (Technical Report WS-05-04). 2005. Pages 1-8.

Matsuda, N., Cohen, W. & Koedinger, K.R. (2005). An Intelligent Authoring System with Programming by Demonstration. Proceedings of the the Japan National Conference on Information and Systems in Education, Kanazawa, Japan.

Matsuda, N., Cohen, W. & Koedinger, K.R. (2005). Building Cognitive Tutors with Programming by Demonstration. In S. Kramer & B. Pfahringer (Eds.), Technical report: TUM-I0510 (Proceedings of the International Conference on Inductive Logic Programming, 41-46): Institut fur Informatik, Technische Universitat Munchen. 2005.

Matsuda, N., Cohen, W. W., Koedinger, K.R. & Stylianides, G. (2010). Learning to solve algebraic equations by teaching a computer agent. In M. F. Pinto & T. F. Kawasaki (Eds.), Proceedings of the Conference of the International Group for the Psychology of Mathematics Education (Vol. 2, pp. 69).

Matsuda, N., Cohen, W. W., Koedinger, K.R., Stylianides, G., Keiser, V., & Raizada, R. (2010). Tuning Cognitive Tutors into a Platform for Learning-by-Teaching with SimStudent Technology. Proceedings of the International Workshop on Adaptation and Personalization in E-B/Learning using Pedagogic Conversational Agents (APLeC) (pp.20-25), Hawaii.

Matsuda, N., Cohen, W., Lacerda, G. & Koedinger, K.R. (2007). Predicting students’ performance with SimStudent that learns cognitive skills from observation. Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED-07).

Matsuda, N., Cohen, W., Sewall, J., Lacerda, G. & Koedinger, K.R. (2007). Evaluating a simulated student using real students’ data for training and testing. In Proceedings of the International Conference on User Modeling, Corfu, Greece, 2007.

Matsuda, N., Cohen, W., Sewall, J., Lacerda, G. & Koedinger, K.R. (2008). Why tutored problem solving may be better than example study. In B. P. Woolf, E. Aimeur, R. Nkambou & S. Lajoie (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 111-121). Heidelberg, Berlin: Springer.

Matsuda, N., Keiser V., Raizada R, Tu A., Stylianides, G., Cohen, W. et al (2010). Learning by Teaching SimStudent: Technical Accomplishments and an Initial Use with Students. In V. Aleven, J. Kay & J. Mostow (Eds.), Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 6095/2010, 449, 317-326. DOI: 10.1007/978-3-642-13437-1_106.

Matsuda, N., Keiser, V., Raizada, R., Yarzebinski, E., Watson, S.P., Stylianides, G. , Cohen, W., & Koedinger, K.R. (2012). Studying the Effect of Tutor Learning using a Teachable Agent that Asks the Student Tutor for Explanations. In M. Sugimoto, V. Aleven, Y. S. Chee& B. F. Manjon (Eds.), Proceedings of the International Conference on Digital Game and Intelligent Toy Enhanced Learning (DIGITEL 2012) (pp. 25-32). Los Alamitos, CA:IEEE Computer Society. Best Paper Finalist.

Matsuda, N., Lee, A., Cohen, W. & Koedinger, K.R. (2009). A Computational Model of How Learner Errors Arise from Weak Prior Knowledge. Proceedings of the Annual Meeting of the Cognitive Science Society, 2009, 1288-1293.

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G. & Koedinger, K.R. (2012). Motivational factors for learning by teaching: The effect of a competitive game show in a virtual peer-learning environment. In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 101-111.

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G., Cohen, W. W. (2011). Learning by Teaching SimStudent – An Initial Classroom Baseline Study comparing with Cognitive Tutor. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 213-221. Springer-Verlag Berlin Heidelberg.

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., William, W. C., Stylianides, G., et al. (2012). Shallow learning as a pathway for successful learning both for tutors and tutees. In N. Miyake, D. Peebles & R. P. Cooper (Eds.),Proceedings of the Annual Conference of the Cognitive Science Society. [38%acceptance rate out of 537 submissions]

Mayfield, E. & Rosé, C. P. (2011). Recognizing Authority in Dialogue with an Integer Linear Programming Constrained Model. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies.

Mayfield, E., Adamson, D. & Rosé, C. P. (2012). Hierarchical Conversation Structure Prediction in Multi-Party Chat. In Proceedings of the 13th Annual SIGdial Meeting on Discourse and Dialogue.

Mayfield, E., Adamson, D., & Rosé, C. P. (2013). Recognizing Rare Social Phenomena in Conversation: Empowerment Detection in Support Group Chatrooms. Proceedings of the Association for Computational Linguistics.

Mayfield, E., Rudnicky, A., & Rosé, C. P. (2012). Computational Representation of Discourse Practices in Task-based Dialogue, Proceedings of the 4th ACM International Conference on Intercultural Collaboration

McCormick, D. & Vercellotti, M. (2009). To Err is Human, to Self-correct Divine: Examining Classroom Recorded Speaking Activity Data to Support ESL Self-correction as Noticing. Paper presented at the American Association for Applied Linguistics Conference, March 2009.

McCormick, D., O'Neill, M.C. & Siskin C. B. (2006). Serving three mistresses in CALL: Students, teachers, researchers. CALICO Symposium, Honolulu.

McLaren, B., Bollen, L., Walker, E., Harrer, A. & Sewall, J. (2005). Cognitive Tutoring of Collaboration: Developmental and Empirical Steps Towards Realization. Computer Supported Collaborative Learning Conference. 2005.

McLaren, B., DeLeeuw, K.E., Mayer (2010). A Politeness Effect in Learning with Web-Based Intelligent Tutors. Proceedings of the American Educational Research Association (AERA) Annual Meeting, April 30 – May 4, 2010, Denver, Colorado.

McLaren, B. & Isotani, S. (2011). When is it best to learn with all worked examples? In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 222-229. Springer-Verlag Berlin Heidelberg.

McLaren, B. & Koedinger, K.R. (2009). In vivo learning experiences with a stoichiometry tutor: Worked exmples lead to more efficient learning. Presented as part of "In Vivo Experimentation on Worked Examples Across Domains" Symposium at EARLI 2009.

McLaren, B., Koedinger, K.R., Schneider, M., Harrer, A. & Bollen, L. (2004). Toward Cognitive Tutoring in a Collaborative, Web-Based Environment. In the Proceedings of the Workshop on Adaptive Hypermedia and Collaborative Web-Based Systems (AHCW-04), Munich, Germany, July 2004.

McLaren, B., Koedinger, K.R., Schneider, M., Harrer, A. & Bollen, L. (2004). Bootstrapping Novice Data: Semi-Automated Tutor Authoring Using Student Log Files; In the Proceedings of the Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes, Seventh International Conference on Intelligent Tutoring Systems (ITS-2004), Maceio, Brazil, August 2004.

McLaren, B., Lim, S., & Koedinger, K.R. (2008). When is assistance helpful to learning? Results in combining worked examples and intelligent tutoring. In B. Woolf, E. Aimeur, R. Nkambou, S. Lajoie (Eds), Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS-08), Lecture Notes in Computer Science, 5091 (pp. 677-680). Berlin: Springer.

McLaren, B., Lim, S., & Koedinger, K.R. (2008). When and How Often Should Problem Solutions be given to Students? New Results and a Summary of the Current State of Research. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 2176-2181). Austin, TX: Cognitive Science Society.

McLaren, B., Lim, S., Gagnon, F., Yaron, D. & Koedinger, K.R. (2006). Studying the Effects of Personalized Language and Worked Examples in the Context of a Web-Based IntelligentTutor. In M. Ikeda, K.D. Ashley, & T-W. Chan (Eds), Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Lecture Notes in Computer Science, 4053 (pp. 318-328). Berlin: Springer. (Finalist for the Best Paper Award)

McLaren, B., Lim, S., Yaron, D. & Koedinger, K.R. (2007). Can a Polite Intelligent Tutoring System Lead to Improved Learning Outside of the Lab? In R. Luckin, K.R. Koedinger, K.R., & J. Greer (Eds). Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED 2007), IOS Press, (p. 443-440).

McLaren, B., Roll, I., Aleven, V. & Koedinger, K.R. (2007). Modeling and tutoring help seeking with a cognitive tutor. Paper presented at the 12th Biennial Conference for Research on Learning and Instruction (EARLI). Budapest, Hungary, August, 2007.

McLaren, B. & Rummel, N. (2009). Adapting Assistance to the Student(s): Preliminary Ideas from Individual and Collaborative Computer-Supported Learning Contexts. Symposium Session “The Assistance Dilemma in CSCL”, Computer-Supported Collaborative Learning (CSCL09), June 8-13, 2009, Rhodes Greece.

McLaren, B., Rummel, N., Pinkwart, N., Tsovaltzi, D., Harrer, A. & Scheuer, O. (2008). Learning Chemistry through Collaboration: A Wizard-of-Oz Study of Adaptive Collaboration Support. In the Proceedings of the Workshop on Intelligent Support for Exploratory Environments (ISSE 08) at the European Conference on Technology Enhanced Learning (EC-TEL 2008), Maastricht, the Netherlands, September 17, 2008.

McLaren, B., Rummel, N., Tsovltzi, D., Braun, I., Scheurer, O., Harrer, A. & Pinkwart, N. (2007). The CoChemEx Project: Conceptual chemistry learning through experimentation and adaptive collaboration. In Proceedings of the Workshop on ‘Emerging Technologies for Inquiry Based Learning in Science’, AIED, pp. 36-48.

McLaren, B., Scheuer, O., De Laat, M., Hever, R., De Groot, R. & Rosé, C.P. (2007). Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions. In the Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED 2007), IOS Press, (p. 141-147).

Michaels, S. & O'Connor, C. (2011). Making thinking and productive talk visible: Exploring the use of video in three models of scalable professional development for productive talk. Paper presented at Socializing Intelligence Through Academic Talk and Dialogue: Invitational AERA Research Conference. University of Pittsburgh, September 22-25, 2011.

Mitsugi, S., MacWhinney, B. et al. (2010). Cue-based processing of relative clauses in L2 Japanese. Selected Proceedings of the 2008 Second Language Research Forum. M. Prior. Somerville, MA, Cascadilla: 123-138.

Mostow, J. & Zhang (2008). Analytic comparison of three methods to evaluate tutorial behaviors. 1st International Conference on Educational Data Mining, 2008. [full paper].

Mostow, J. & Beck, J. (2006). Refined micro-analysis of fluency gains in a Reading Tutor that listens: Wide reading beats rereading -- but not by much Thirteenth Annual Meeting Society for the Scientific Study

Mostow, J., Beck, J., Cen, H., Cuneo, A., Gouvea E. & Heiner, C. (2005). An Educational Data Mining Tool to Browse Tutor-Student Interactions: Time Will Tell!. Proceedings of AAAI Workshop on Educational Data Mining (2005), 15-22.

Mu, J., Stegmann, K., Mayfield, E., Rosé, C. P. & Fischer, F. (2011). ACODEA: A Framework for the Development of Classification Schemes for Automatic Classification of Online Discussions, in Proceedings of Computer Supported Collaborative Learning

Muldner, K., Burleson, W., & VanLehn, K. (2010). "Yes!": Using tutor and sensor data to predict moments of delight during instructional activities. In P. De Bra, A. Kobsa & D. Chin (Eds.) User Modeling, Adaptation and Personalization: 18th International Conference, UMAP 2010 (pp. 159-170) Heidelberg, Germany: Springer.

Muldner, K., Burleson, W., van de Sande, B. & VanLehn, K. (2010). An analysis of gaming behaviors in an intelligent tutoring system. In V. Aleven, J. Kay & J. Mostow (Eds), Intelligent Tutoring Systems: 10th International Conference, ITS 2010 LNCS 6094, 184-193. Springer-Verlag Berlin Heidelberg.

Murray, R.C. & Mostow, J. (2006). A Comparison of Decision-Theoretic, Fixed-Policy and Random Tutorial Action Selection. In M. Ikeda, K. Ashley, & T-W. Chan (Eds.). ITS 2006, LNCS 4053, pp 114-123. Springer-Verlag Berlin Heidelberg 2006.

Navaroli, D., Siler, S. A., Magaro, C. (2013). Comparison of teacher-generated to coupled teacher/student-generated analogy in cell biology. Paper presented at the 2013 National Science Teachers’ Association Conference, Portland, Oregon, October 2013.

Nokes, T. J., & Gadgil, S. (2010). Analogical comparison supports collaborative learning in physics. In the symposium on Collaborative Learning and Remembering Part 1. Paper presented at the 22nd Annual Convention for the Association for Psychological Science: Boston, MA.

Nokes, T. J., Levine, J. M., Belenky, D. M. & Gadgil, S. (2010). Investigating the impact of dialectical interaction on engagement, affect, and robust learning. Proceedings of ICLS 2010.

Nokes, T. J., Mestre, J., Ross, B. H., & Richey, J. E. (2010). Conceptual analysis and student learning in physics. In the symposium on Solving Problems in School: Concepts, Procedures and Instruction to Support Learning. Paper presented at the 22nd Annual Conference for the Association for Psychological Science: Boston, MA.

Nokes, T.J. & Ross, B.H. (2007). Near-Miss versus surface-different comparisons in analogical learning and generalization. Proceedings of the 29th Annual Meeting of the Cognitive Science Society. (CogScie 2007).

Nokes-Malach, T. J., Mestre, J. P., & Belenky, D. M. (2012). A theoretical framework for transfer as sense-making: Applications and examples. Paper presented at the 2013 Annual Meeting of the American Educational Research Association, San Francisco, CA.

Nuzzo-Jones, G., Walonoski, J.A., Heffernan, N.T. & Livak, T. (2005). The eXtensible Tutor Architecture: A New Foundation for ITS. Proceedings of the 12th Annual Conference on Artificial Intelligence in Education. 2005.

Nwaigwe, A. & Koedinger, K. (2011). The simple location heuristic is better at predicting students' changes in error rate over time compared to the simple temporal heuristic. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, and J. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Nwaigwe, A., Koedinger, K.R., VanLehn, K., Hausmann, R.G.M. & Weinstein, A. (2007). Exploring Alternative Methods for Error Attribution in Learning Curves Analysis in Intelligent Tutoring Systems. Proceedings of the International Conference on Artificial Intelligence in Education 2007.

O'Connor, C. & Michaels, S. (2011). Explicating student learning through discourse coding tools and representations. Paper presented at Socializing Intelligence Through Academic Talk and Dialogue: Invitational AERA Research Conference. University of Pittsburgh, September 22-25, 2011.

Ogan, A., Aleven, V. & Jones, C. (2005). Improving Intercultural Competence by Predicting in French Film. G. Richards (Ed.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcar. 2005. Pages 3101-3106.

Ogan, A., Aleven, V. & Jones, C. (2006). Culture in the Classroom: Challenges for Assessment in Ill-Defined Domains. Workshop Proceedings on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems, 2006.

Ogan, A., Aleven, V., & Jones, C. (2008). Pause, predict and ponder: Use of narrative videos to improve cultural discussion and learning. In M. Czerwinski, A.M. Lund & D.S. Tan (Eds), Proceedings of the 2008 Conference on Human Factors in Computing Systems, CHI 2008, Florence Italy.

Ogan, A., Aleven, V. & Jones, C. (2009). Pause, predict and ponder: Use of narrative videos to improve cultural discussion and learning. Presented as part of the "Computer-Supported Learning with Digital Videos in Multiple Educational Settings" Symposium at the 13th Biennial Conference of EARLI 2009.

Ogan, A., Finkelstein, S., Mayfield, E., D'Adamo, C., Matsuda, N., & Cassell, J. (2012). “Oh, dear Stacy!” Social interaction, elaboration, and learning with teachable agents Proceedings of CHI2012 [23% acceptance rate out of 1577 submissions]

Ogan, A., Finkelstein, S., Walker, E. Carlson, R. & Cassell, J. (2012). Rudeness and Rapport: Insults and Learning Gains in Peer Tutoring. In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 11-21.

Ogan, A., Jones, C. & Aleven, V. (2006). Focusing attention on critical moments: Evaluation of a system for teaching intercultural competence. European Computer Assisted Language Learning.

Ogan, A., Jones, C. & Aleven, V. (2007). Intelligent Tutoring in a Cultural Discussion Forum. Paper presented at European Computer Assisted Language Learning (EuroCALL 2007) Ulster, Northern Ireland, September 2007.

Ogan, A., Jones, C., Aleven, V., Walker, E., Wylie, R. & Jones, C. (2006). A Tense Situation: Applying Cognitive Tutor Methodology to Ill-Defined Domains. European Computer Assisted Language Learning 2006.

Ogan, A., Walker, E., Aleven, V. & Jones, C. (2008). Using a Peer Moderator to Support Collaborative Cultural Discussion. Appeared in the Culturally Aware Tutoring Systems Workshop at ITS 2008.

Ogan, A., Walker, E., Jones, C. & Aleven, V. (2008). Toward supporting collaborative discussion in an ill-defined domain. In B.P. Woolf, E. Aimeur, R. Nkambou & S.P. Lajoie, (Eds.), Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). Springer Lecture Notes in Computer Science, 825-827.

Ogan, A., Wylie, R. & Walker, E. (2006). The challenges in adapting traditional techniques for modeling student behaviors in ill-defined domains. In Workshop Proceedings on Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006.

Ogan, A., Wylie, R. & Walker, E. (2006). Defining the ill-defined: Modeling student behaviour in making aspectual distinctions; Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006.

Olsen, M. and Juffs, A. (2012). The Effect of Animacy on Pronominal Object Clitic Distinction in L2 Spanish. Second Language Research Forum, October, 2012.

Otieno, C., Schwonke, R., Renkl, A., Aleven, V. & Salden, R. (2011). Measuring learning progress via self-explanations versus problem solving - A suggestion for optimizing adaptation in intelligent tutoring systems. In L. Carlson, C. Hölscher, & T. F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 84-89). Austin, TX: Cognitive Science Society.

Pappuswamy, U., Bhembe, D., Jordan, P. & VanLehn, K. (2005). A multi-tier NL-knowledge clustering for classifying students’ essays. In I. Russell & Z. Markov (Eds.), Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS05) (pp. 566-571). Menlo Park, CA: AAAI Press.

Pappuswamy, U., Bhembe, D., Jordan, P. & VanLehn, K. (2005). A supervised clustering method for text classification. In A. Gelbukh (Ed.), Proceedings of Computational Linguistics and Intelligent Text Processing: 6th International Conference, CICLing: Vol. 3406. (pp. 692-702). Berlin: Springer-Verlag Berlin & Heidelberg GmbH & Co. K.

Pappuswamy, U., Jordan, P. & VanLehn, K. (2005). Resolving Discourse Deictic Anaphors in Tutorial Dialogues. In C. Sassen, A. Benz, & P. Kühnlein (Eds.), Proceedings of Constraints in Discourse (pp. 96-103). Dortmund University, Germany.

Pardos, Z. & Yudelson, P. (2013). Towards Moment of Learning Accuracy. Paper presented at AIED Workshop on Simulated Learners in conjunction with AIED 2013, July 9, 2013, Memphis, Tennessee.

Pardos, Z., Baker, R.S.J.D., San Pedro, M.O.C.Z., Gowda, S. & Gowda, S.M. (2013). Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. Third Conference on Learning Analytics and Knowledge (LAK 2013) in Leuven, Belgium April 8-12, 2013

Pavlik, P. (2006). Understanding the effectiveness of direct instruction methods. Paper presented at the 24th Annual Meeting of the California Association for Behavior Analysis, Burlingame, CA

Pavlik, P. (2006). Transfer effects in Chinese vocabulary learning. In R. Sun (Ed.), Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society (pp. 2579). Mahwah, NJ: Lawrence Erlbaum.

Pavlik, P. (2007). Understanding why practice should be fast and accurate. Paper presented at the 33rd Annual Meeting of the Association for Behavior Analysis, San Diego, CA. (May, 2007)

Pavlik, P. (2010). Data Reduction Methods Applied to Understanding Complex Learning Hypotheses. Proceedings of the The 3rd International Conference on Educational Data Mining (pp. 311-312).

Pavlik, P. & Toth, J. (2010). How to Build Bridges between Intelligent Tutoring System Subfields of Research. In J. Kay, V. Aleven & J. Mostow (Eds.). Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS), Part II, 103-112. Springer: Heidelberg.

Pavlik, P. & Wu, S. (2011). A Dynamical system model of microgenetic changes in performance, efficacy, strategy use and value during vocabulary learning. In M. Pechenizky, T. Calders, C. Conati, S. Ventura, C. Romero & J.C. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Pavlik, P., Bolster, Wu, S., Koedinger, K.R. & MacWhinney, B. (2008). Using optimally selected drill practice to train basic facts. In B. Woolf, E. Aimer & R. Nkambou (Eds.). Proceedings of the 9th International Conference on Intelligent Tutoring Systems ITS2008, LNCS 5091, pp 593-602, 2008. Springer-Verlag Berlin Heidelb

Pavlik, P., Cen, H., Koedinger, K.R. (2009). Performance Factors Analysis - A New Alternative to Knowledge Tracing. In V. Dimitrova & R. Mizoguchi (Eds.). Proceedings of the 14th International Conference on Artificial intelligence in Education (AIED), Amsterdam: IOS Press, 531-538.

Pavlik, P., Cen, H., Koedinger, K.R. (2009). Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models. Proceedings of the 2nd International Conference on Educational Data Mining (EDM 2009), 121-130.

Pavlik, P., Cen, H., Wu, S., Koedinger, K.R. (2008). Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor. In R. S. J. d. Baker & J. E. Beck (Eds.), Proceedings of the 1st Annual Educational Datamining Conference, 2008. [full paper], 77-86.

Pavlik, P., Presson, N. & Koedinger, K.R. (2007). Optimizing knowledge component learning using a dynamic structural model of practice. In R. Lewis & T. Polk (Eds.), Proceedings of the Eigth International Conference of Cognitive Modeling. Ann Arbor: University of Michigan, 47-52.

Pavlik, P., Presson, N., Dozzi, G., Wu, S., MacWhinney, B. & Koedinger, K.R. (2007). The FaCT (Fact and Concept Training) System: A new tool linking cognitive science with educators. In D.s. McNamara & J.G. Trafton (Eds.), Proceedings of 29th Annual Meeting of the Cognitive Science Society, Austin, TX: Cognitive Science Society, 1379-1384.

Pavlik, P.I., Yudelson, M., & Koedinger, K.R. (2011). Using contextual factors analysis to explain transfer of least common multiple skills. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education Conference (AIED 2011), Lecture Notes in Computer Science, Vol. 6738, 256-263.

Perfetti, C. (2008). Development of Word Meanings and Reading Skill. Symposium at the 15th Annual Meeting of the Society for the Scientific Study of Reading, Asheville, NC (July 2008).

Perfetti, C. (2009). Instructional interventions based on theory-targeted learning: Examples from second language learning. Society for Research on Educational Effectiveness, SREE, Washington, D.C. February.

Perfetti, C. (2010). Reading processes and reading problems: progress toward a universal reading science. Extraordinary Brain Symposium, National Yang-Ming University, Taiwan.

Perfetti, C. (2012). Reading in a second Language: Processes and Challenges. Colloquium conducted at the 31st Second Language Research Forum Conference (SLRF). Pittsburgh, PA.

Pinkwart, N., Aleven, V., Ashley, K. & Lynch, C. (2006). Toward Legal Argument Instruction with Graph Grammars and Collaborative Filtering Techniques. Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, 227-236.

Pinkwart, N., Ashley, K., Aleven, V., Lynch, C. (2008). Graph Grammars: An ITS Technology for diagram representations. Paper presented at 21st International FLAIRS Conference, May 15-17, 2008, Coconut Grove, Florida.

Pinkwart, N., Lynch, C., Ashley, K., Aleven, V. (2008). Re-evaluating LARGO in the classroom: Are diagrams better than text for teaching argumentation skill? Paper presented at the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Pino, J. & Eskenazi, M. (2009). Measuring hint level in open cloze questions. FLAIRS 2009.

Pino, J., Heilman, M., Eskenazi, M. (2008). A Selection Strategy to Improve Cloze Question Quality. Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains. 9th International Conference on Intelligent Tutoring Systems, 22-34.

Prata, D.N., Baker, R.S.J.d., Costa, E.d.B., Rosé, C.P., Cui, Y. & de Carvalho, A.M.J.B. (2009). Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments. Proceedings of the 2nd International Conference on Educational Data Mining (EDM 2009), 131-140.

Presson, N. (2008). An adaptive tutor for explicit instruction of French grammatical gender cues. Paper presentation at The Nature and Development of L2 French, Southampton, UK.

Presson, N. & MacWhinney, B. (2009). Explicitness and category breadth improve grammar learning and generalization. Paper presentation at the 7th International Symposium on Bilingualism, Utrecht, Netherlands.

Rau, M. & Pardos, Z. (2012). Investigating Practice Schedules of Multiple Fraction Representations Using Knowledge Tracing Based Learning Analysis Techniques. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012).

Rau, M., Aleven, V. & Rummel, N. (2009). Intelligent Tutoring Systems with Multiple Representations and Self-Explanation Prompts Support Learning of Fractions. In V. Dimitrova, R. Mizoguchi, & B. du Boulay (Eds.), Proceedings of the 14th International Conference on Artificial Intelligence in Education (AIED). Amsterdam, the Netherlands: IOS Press, 441-448.

Rau, M., Rummel, N. & Aleven, V. (2009). Self-explanation prompts enable students to benefit from learning with multiple graphical represntations of fractions. Presented as part of "In Vivo Experimentation on Self-Explanations Across Domains" Symposium at EARLI 2009.

Rau, M., Aleven, V. & Rummel, N. (2010). Blocked versus Interleaved Practice with Multiple Representations in an Intelligent Tutoring System for Fractions. Intelligent Tutoring Systems 2010. Lecture Notes in Computer Science, 2010, Vol 6094/2010, 413-422.

Rau, M., Aleven, V. & Rummel, N. (2013). Complementary effects of sense-making and fluency-building support for connection making: a matter of sequence? In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 329-338. Springer-Verlag Berlin Heidelberg.

Rau, M., Aleven, V., Rummel, N. & Rohrbach, S. (2012). Sense Making Alone Doesn’t Do It: Fluency Matters Too! ITS Support for Robust Learning with Multiple Representations. In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 174-184.

Rau, M., Rummel, N., Aleven, V., Tunc-Pekkan, Z., & Pacilio, L. (2012). How to schedule multiple graphical representations? A classroom experiment with an intelligent tutoring system for fractions. In J. v. Aalst, K. Thompson, M. J. Jacobson & P. Reimann (Eds.), The future of learning: Proceedings of the 10th International Conference of the Learning Sciences (ICLS 2012) (Vol. Volume 1, Full Papers, 64-71). Sydney, Australia: ISLS.

Rau, M. & Scheines, R. (2012). Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012).

Rau, M., Scheines, R., Aleven, V. & Rummel, N. (2013). Does Representational Understanding Enhance Fluency - Or Vice Versa? Searching for Mediation Models. Proceedings of EDM 2013, 161-168. [Awarded Best Conference Paper]

Razzaq, L., Feng, M., Nuzzo-Jones, G., Heffernan, N.T. & Koedinger, K.R. (2005). Blending Assessment and Instructional Assisting. Proceedings of the 12th Artificial Intelligence in Education (AIED) Confernce, 2005. Pages 555-562.

Rehak, K.M. & Juffs, A. (2011). Native and Non-Native Processing of Morphologically Complex English Words. In Selected Proceedings of the 2010 Second Language Acquisition Research Forum. ed. Gisela Granena et al., 125-142. Somerville, MA: Cascadilla Proceedings Project.

Reichle, E., Tokiwicz, N., Liu, Y. & Perfetti, C. (2006). Using ERP to Examine When the Eyes Move During Reading. Thirteenth Annual Meeting Society for the Scientific Study of Reading. July 5-8, 2006. Vancouver, Canada.

Renkl, A., Schwonke, R., Witter, J., Krieg, C., Aleven, V. & Salden, R. (2007). Faded worked-out examples in an intelligent tutoring system: Do they further improve learning? Paper presented at the 12th Biennial Conference for Research on Learning and Instruction (EARLI). Budapest, Hungary, August, 2007.

Resnick (2007). How (Well Structured) Talk Builds the Mind. Proceedings of the National Academies Eighth Olympiad of the Mind Symposium, Washington, DC.

Resnick, L., Leinhardt, G. & Petrosky, A.R. (2007). Disciplinary literacy: Cognitive apprenticeship for secondary school teachers and students. Presented at the 2007 meeting of the American Educational Research Association, Chicago, IL.

Richey, J.E., Bernacki, M.L., Belenky, D.M. & Nokes-Malach, T.J. (2012). Predicting Performance With a Task-Based Behavioral Measure of Achievement Goals. Presentation in "SIG Motivation in Education" Roundtable Session. AERA 2012, Vancouver, British Columbia, Canada.

Ringenberg, M. (2007). A Student model based on Item Response Theory for a tutorial dialogue agent. Proceedings of AIED2007, Young Researchers Track.

Ringenberg, M. & VanLehn, K. (2006). Scaffolding Problem Solving with Annotated Worked-Out Examples to Promote Deep Learning. In Intelligent Tutoring Systems: Eighth International Conference (ITS 2006), Jhongli, Taiwan. Springer-Verlag Lecture Notes in Computer Science. pages 625-634.

Ringenberg, VanLehn, K. (2008). Does solving ill-defined physics problems elicit more learning than conventional problem solving? In B. P. Woolf, E. Aimeur, R. Nkambou & S. Lajoie (Eds) Doctoral Consortium, Intelligent Tutoring Systems: 9th International Conference, ITS2008.

Ritter, S., Joshi, A., Fancsali, S.E. & Nixon, T. (2013). Predicting Standardized Test Scores from Cognitive Tutor Interactions. Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013). Memphis, TN.

Ritter, S., Kulikowich, J., Lei, P., McGuire, C.L. & Morgan, P. (2007). What evidence matters? A randomized field trial of Cognitive Tutor Algebra I. In T. Hirashima, U. Hoppe & S. S. Young (Eds.), Supporting Learning Flow through Integrative Technologies (Vol. 162, pp. 13-20). Amsterdam: IOS Press. Proceedings of the 15th International Conference on Computers in Education, ICCE 2007.

Rivers, K. & Koedinger, K. (2013). Automatic Generation of Programming Feedback: A Data-Driven Approach. Paper presented as part of the First Workshop on AI-supported Education for Computer Science (AIEDCS), AIED 2013. 50-59.

Roberge, D., Rojas, A. & Baker, R.S.J.d. (2012). Does the Length of Time Off-Task Matter? Proceedings of the 2nd International Conference on Learning Analytics and Knowledge.

Rodrigo, M.M., Baker, R.S.J.D., McLaren, B, Jayme, A. & Dy, T. (2012). Development of a Workbench to Address the Educational Data Mining Bottleneck. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012).

Rodrigo, M.M.T, Baker, R.S.J.d. & Nabos, J.Q (2010). The Relationships Between Sequences of Affective States and Learner Achievement. In S.L. Wong et al (Eds.). Proceedings of the 18th International Conference on Computers in Education. Putrajaya, Malaysia: Asia-Pacific Society for Computers in Education.

Rodrigo, M.M.T., Agapito, J., Nabos, J., Repalam, M.C., Reyes, S. & Baker, R.S.J.d. (2010). The Effects of an Embodied Conversational Agent on Student Affective Dynamics while Using an Intelligent Tutoring System. Proceedings of HumanCom 2010

Rodrigo, M.M.T., Baker, R.S.J.d. & Nabos, J.Q. (2010). The Relationships Between Sequences of Affective States and Learner Achievements. Proceedings of the 18th International Conference on Computers in Education.

Rodrigo, M.M.T., Baker, R.S.J.d., Agapito, J., Nabos, J., Repalam, M.C., & Reyes, S.S. (2010). Comparing Disengaged Behavior within a Cognitive Tutor in the USA and Philippines. ITS: Lecture Notes in Computer Science, 2010, Vol 6095/10`0, 263-265. Springer.

Rodrigo, M.M.T., Baker, R.S.J.d., Jadud, M.C., Amarra, A.C.M., Dy, T., Espejo, M.B.V., Lim, S.A.L., Pascua, S.A.M.S., Sugay, J.O. & Tabanao, E.S. (2009). Affective and Behavioral Predictors of Novice Programmer Achievement. Proceedings of the 14th ACM-SIGCSE Annual Conference on Innovation and Technology in Computer Science Education, New York: ACM, 156-160.

Rodrigo, M.T. & Baker, R.S.J.d. (2009). Coarse-Grained Detection of Student Frustration in an Introductory Programming Course. Proceedings of ICER 2009: the International Computing Education Workshop

Rodrigo, M.T., Anglo, Sugay, J. & Baker, R.S.J.d. (2008). Use of Unsupervised Clustering to Characterize Learner Behaviors and Affective States while Using an Intelligent Tutoring System. Proceedings of International Conference on Computers in Education, 49-56.

Rodrigo, M.T., Baker, R.S.J.d., D'Mello, Gonzalez, C.T., Lagud, M., Lim, S., Macapanpan, A., Pascua, S., Santillano, J., Sugay, J., Tep, S. & Viehland, N. (2008). Comparing learners’ affect while using an intelligent tutoring system and a simulation problem solving game. Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008), 40-49.

Rodrigo, M.T., Baker, R.S.J.d., Lagud, M., Lim, S., Macapanpan, A., Pascua, S., Santillano, J., Sevilla, L., Sugay, J., Tep, S. & Viehland, N. (2007). Affect and Usage Choices in Simulation Problem Solving Environments. Proceedings of Artificial Intelligence in Education 2007, 145-152.

Rodrigo, M.T., Rebolledo-Mendez, Baker, R.S.J.d., du Boulay, Sugay, J., Lim, S., Espejo-Lahoz & Luckin (2008). The Effects of Motivational Modeling on Affect in an Intelligent Tutoring System. Proceedings of International Conference on Computers in Education.

Roll, I. (2012). Assessments that matter. The 8th Annual Conference of the Alternative Education Resource Organization (AERO). Portland, OR.

Roll, I. (2012). Coevolution of Qualitative and Symbolic Reasoning in Invention Activities. Proceedings of AERA 2012.

Roll, I. (2012). Coevolution of qualitative and symbolic reasoning in invention activities. Symposium 'On the design, implementation, and Outcomes of Using Contrasts in Learning' conducted at the the annual meeting of the American Education Research Association (AERA) 2012, Vancouver, BC.

Roll, I. (2013). Using Learning Analytics to Inform Theories of Help Seeking. Proceedings of AERA 2013.

Roll, I., (2010). Scaffold and feedback in scientific inquiry environments: The case of the invention lab. In I. Roll, M. Mavrikis, & S. G. Santos (Eds.). 3Rd workshop on Intelligent Support in Exploratory Enviornments (ISEE), in conjunction with the 9th International Conference of the Learning Sciences (ICLS). Chicago, IL.

Roll, I., Aleven, V. & Koedinger, K.R. (2004). Promoting Effective Help-Seeking Behavior Through Declarative Instruction. International Conference on Intelligent Tutoring Systems (ITS), 2004. Pages 857-859.

Roll, I., Aleven, V. & Koedinger, K.R. (2009). Helping Students Know ‘Further’ – Increasing Flexibility of Students’ Knowledge Using Symbolic Invention Tasks. Proceedings of the Annual Meeting of the Cognitive Science Society, 1169-1174.

Roll, I., Aleven, V., & Koedinger, K. R. (2011). Outcomes and mechanisms of transfer in invention activities. In L. Carlson, C. Holscher, & T. Shipley (Eds.). Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, 2824-2829. Austin, TX: Cognitive Science Society.

Roll, I., Aleven, V., Koedinger, K.R. (2008). Instruments and challenges in assessing help-seeking knowledge and behavior. In Proceedings of Workshop on Metacognition and Self-regulated Learning in Educational Technologies in conjunction with the 9th International Conference on Intelligent Tutoring Systems (ITS) 2008. Montreal, Canada.

Roll, I., Aleven, V., Koedinger, K.R. (2008). Designing structured invention tasks to prepare for future learning [abstract]. In proceedings of the 30th annual conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

Roll, I., Aleven, V. & Koedindger, K.R. (2012). Automated Task Adaptation to Support Students’ inquiry Learning. In P. Blikstein (Chair). Building (Timely) Bridges between Learning Analytics, Educational Data Mining and Core Learning Sciences Perspectives. Symposium conducted at the 10th International Conference of the Learning Sciences (ICLS 2012).

Roll, I., Aleven, V., & Koedinger, K.R. (2010). Analysis of students' actions during online invention activities - seeing the thinking through the numbers. In S. Goldman, & J. Pellegrino (Eds.). Symposium at the 9th International Conference of the Learning Sciences. Chicago, IL, 45-52.

Roll, I., Aleven, V., & Koedinger, K.R. (2011). The relationships between data mining, cognitive modeling, and learning theories: Assessing and improving help-seeking skills. Presented in Symposium entitled "Computing What the Eye Cannot See: Educational Data Mining, Learning Analytics, and Computational Techniques for Detecting and Evaluating Patterns in Learning" at the Annual meeting of the American Educational Research Association (AERA 2011).

Roll, I., Aleven, V., McLaren, B. & Koedinger, K. R. (2011). Metacognitive practice makes perfect: Improving students' self-assessment skills with an intelligent tutoring system. In Biswas, G., Bull, S. Kay, J. & Mitrovic, A. (Eds.). Artificial Intelligence in Education (AIED) Lecture Notes in Computer Science, Vol 6738/2011, 288-295. Berlin: Springer Verlag.

Roll, I., Aleven, V., McLaren, B. & Koedinger, K.R. (2007). Can help seeking be tutored? Searching for the secret sauce of metacognitive tutoring. In R. Luckin, K.R. Koedinger, K.R., & J. Greer (Eds), Proceedings of the International Conference on Artificial Intelligence in Education 2007. IOS Press. (p. 203-210).

Roll, I., Aleven, V., McLaren, B., Ryu, E., Baker, R.S.J.d. & Koedinger, K.R. (2006). The Help Tutor: Does Metacognitive Feedback Improve Students’ Help-Seeking Actions, Skills and Learning? In M. Ikeda, K.D. Ashley, & T-W. Chan (Eds), Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Lecture Notes in Computer Science, 4053 (pp. 360-369). Berlin: Springer.

Roll, I., Baker, R.S.J.d., Aleven, V. & Koedinger, K.R. (2004). What goals do students have when choosing the actions they perform? Proceedings of the Sixth International Conference on Cognitive Modeling. 2004, 380-381. Mahwah, NJ: Lawrence Erlbaum.

Roll, I., Baker, R.S.J.d., Aleven, V., Koedinger, K.R. (2004). A Metacognitive ACT-R Model of Students' Learning Strategies in Intelligent Tutoring Systems. Proceedings of the Seventh International Conference of Intelligent Tutoring Systems. 2004. Pages 854-856. Berlin: Springer-Verlag.

Roll, I., Baker, R.S.J.d., Aleven, V., Mclaren, B. & Koedinger, K.R. (2005). Modeling Students’ Metacognitive Errors in Two Intelligent Tutoring Systems. In L. Ardissono, P. Brna, & A. Mitrovic (Eds.), Proceedings of the 10th International Conference on User Modeling (UM'2005) (pp. 379-388). Berlin: Springer-Verlag.

Roll, I., Holmes, N.G., Day, J., Park, A.H.K. & Bonn, D.A. (2013). Process and Outcome Benefits for Orienting Students to Analyze and Reflect on Available Data in Productive Failure Activities. Paper presented at the Scaffolding in Open-Ended Learning Environments Workshop at AIED 2013, 61-68.

Roll, I., Koedinger, K.R., Aleven (2010). The Invention Lab: Using a hybrid of model tracing and constraint-based tutoring to offer intelligent support in inquiry environments. Intelligent Tutoring Systems: Lecture Notes in Computer Science 2010, Volume 6095/2010, 115-124. Springer

Roll, I., Ryu, E., Sewall, J., Leber, B., McLaren, B., Aleven, V. & Koedinger, K.R. (2006). Towards Teaching Metacognition: Supporting Spontaneous Self-Assessment. In M. Ikeda, K.D. Ashley, & T. W. Chan (Eds.). Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 738-740. Berlin: Springer Verlag.

Rosé, C. (2011). What sociolinguistics and machine learning have to say to one another about interaction analysis. Paper presented at Socializing Intelligence Through Academic Talk and Dialogue: Invitational AERA Research Conference. University of Pittsburgh, September 22-25, 2011.

Rosé, C. P. & Kam, M. (2010). LearnLab India: towards "in vivo" international comparative education research. Proceedings of the "Internationalizing the learning sciences from formal to informal learning environments" symposium conducted at the 9th International Conference of the Learning Sciences, ICLS, Vol 2, 102-103.

Rosé, C. P. & Tovares, A. (2011). What Sociolinguistics and Machine Learning Have to Say to One Another about Interaction Analysis. In L. Resnick, C. Asterhan & S. Clarke (Eds.). Socializing Intelligence Through Academic Talk and Dialogue, Washington, DC: American Educational Research Association.

Rosé, C.P. (2005). Making authoring of conversational interfaces accessible. Workshop on Authoring Tools for Advanced Learning Systems with Standards, November 2005

Rosé, C.P. (2005). Facilitating reliable content analysis of corpus data with automatic and semi-automatic text classification technology, EPFL switzerland

Rosé, C.P. & Donmez, P. (2005). TagHelper: An application of text classification technology to automatic and semi-automatic modeling of collaborative learning interactions, Proceedings of the AIED 2005 Workshop on Representing and Analyzing Collaborative Interactions: What works? When does it work? To what extent?

Rosé, C.P., Aleven, V., Carey, R. & Robinson, A. (2005). A First Evaluation of the Instructional Value of Negotiable Problem Solving Goals on the Exploratory Learning Continuum . Proceedings of the 12th International Conference on Artificial Intelligence in Education. 2005.

Rosé, C.P., Donmez, P., Cohen, W., Koedinger, K.R. & Heffernan, N. (2005). Automatic and Semi-Automatic Skill Coding With a View Towards Supporting On-Line Assessment. Proceedings of the 12th International Conference on Artificial Intelligence in Education. 2005.

Rosé, C.P., Pai, C. & Arguello, J. (2005). Enabling Non-linguists to Author Advanced Conversational Interfaces Easily, Proceedings of FLAIRS 05. p.572-577.

Rotaru, M. & Litman, D.J. (2007). The Utility of a Graphical Representation of Discourse Structure in Spoken Dialogue Systems. Proceedings of 45th Annual Meeting of the Association for Computational Linguistics (ACL), June, 2007

Rummel, N. (2012). Exploring new spaces without reinventing the wheel. Presentation in the Invited Presidential Session "The Future of Learning and the Learning Sciences" at ICLS 2012.

Rummel, N., Diziol, D. & Spada, H. (2007). Förderung mathematischer Kompetenz durch kooperatives Lernen: Erweiterung eines intelligenten Tutorensystems [Promoting mathematical competency through collaborative learning: Extension of an intelligent tutoring system]. Paper presented at the 5th Conference of the "Fachgruppe Medienpsychologie der Deutsche Gesellschaft für Psychologie" [German Psychological Association]. Dresden, Germany

Rummel, N., Diziol, D., Spada, H. (2008). Analyzing the effects of scripted collaboration in a computer-supported learning environment by integrating multiple data sources. Paper presented at the Annual Conference of the American Educational Research Association (AERA) 2008. New York City, NY, USA.

Rummel, N., Diziol, D., Spada, H. & McLaren, B. (2007). Scripting collaborative problem solving with the Cognitive Tutor Algebra: A Way to promote learning in mathematics. Proceedings of 12th meeting of the European Association for Research on Learning and Instruction (EARLI-07). Budapest, August 28 - September 1, 2007.

Rummel, N., Diziol, D., Spada, H., McLaren, B., Walker, E. & Koedinger, K.R. (2006). Flexible support for collaborative learning in the context of the Algebra I Cognitive Tutor. Workshop paper presented at the Seventh International Conference of the Learning Sciences (ICLS), Bloomington, IN, USA.

Rummel, N., Hauser, S. & Spada, H. (2007). How does net-based interdisciplinary collaboration change with growing domain expertise? Proceedings of the Conference on Computer Supported Collaborative Learning (CSCL-07). Rutgers University

Rummel, N., Spada, H. & Diziol, D. (2007). Can collaborative extensions to the Algebra I Cognitive Tutor enhance robust learning? An in vivo experiment. Paper presented at the Annual Conference of the American Educational Research Association (AERA-07). Chicago, IL, USA, April 2007.

Rummel, N., Spada, H. & Diziol, D. (2007). Evaluating collaborative extensions to the Cognitive Tutor Algebra in an in vivo experiment: Lessons learned. Proceedings of the European Association for Research on Learning and Instruction (EARLI-07). Budapest, August 28 - September 1, 2007.

Rummel, N., Spada, H. & Hauser, S. (2006). Learning to collaborate in a computer-mediated setting: Observing a model beats learning from being scripted. Seventh International Conference of the Learning Sciences (ICLS). Bloomington, IN, USA., P. 634

Sagae, K., Davis, E., Lavie, A., MacWhinney, B. & Wintner, S. (2007). High-accuracy annotation and parsing of CHILDES transcripts. Proceedings of the 45th Meeting of the Association for Computational Linguistics. Prague, ACL.

Salden, R., Aleven, V., Renkl, A. & Schwonke, R. (2008). Worked examples and the assistance dilemma. Abstract in Symposium: Confronting the Assistance Dilemma: Is it Better to Give Than Receive? (AERA 2008).

Salden, R., Aleven, V., Renkl, A. & Schwonke, R. (2008). Worked examples and tutored problem solving: Redundant or synergistic forms of support? Proceedings of the 30th Annual Meeting of the Cognitive Science Society, Washington DC, USA, July 2008, 589-594

Salden, R., Aleven, V., Renkl, A. & Schwonke, R. (2009). Does learning from examples improve tutored problem solving? Presented as part of "In Vivo Experimentation on Worked Examples Across Domains" Symposium at EARLI 2009.

Salden, R., Aleven, V., Renkl, A. & Witter, J. (2006). Does Learning from Examples Improve Tutored Problem Solving? Paper presented at the 14th Biannual Conference of the European Association for Research on Learning and Instruction (EARLI), August 28-September 1, 2007, Budapest, Hungary.

Salden, R., Koedinger, K.R., Aleven, V. & McLaren, B. (2009). Does Cognitive Load Theory Account for Beneficial Effects of Worked Examples in Tutored Problem Solving? Proceedings of the 3rd International Cognitive Load Theory Conference (CLT-09). Heerlen, the Netherlands, March 2-4, 2009.

Sao Pedro, M.O.C., Baker, R.S.J.d. & Rodrigo, M.M.T (2011). The Relationship between Carelessness and Affect in a Cognitive Tutor. Proceedings of the 4th bi-annual International Conference on Affective Computing and Intelligent Interaction

Sao Pedro, M.O.C., Baker, R.S.J.d. & Rodrigo, M.M.T (2011). Detecting Carelessness through Contextual Estimation of Slip Probabilities among Students Using an Intelligent Tutor for Mathematics. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 304-311. Springer-Verlag Berlin Heidelberg.

Sao Pedro, M.O.C., Baker, R.S.J.d., Bowers, A. & Heffernan, N. (2013). Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School. Proceedings of EDM 2013, 177-184.

Sao Pedro, M.O.Z., Baker, R.S.J.d., Gowda, S.M. & Heffernan, N.T. (2013). Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System. Proceedings of the 16th International Conference on Artificial Intelligence and Education, 41-50.

Sao Pedro, M., Gobert, J., & Baker, R. (2012). Assessing the Learning and Transfer of Data Collection Inquiry Skills Using Educational Data Mining on Students' Log Files. Paper presented at The Annual Meeting of the American Educational Research Association. Vancouver, BC, CA: Retrieved April 15, 2012, from the AERA Online Paper Repository. Best Student Paper Award - AERA SIG Advanced Technologies for Learning/Learning Sciences

Sao Pedro, M.A., Gobert, J.D. & Baker, R.S.J.d. (2012). The Development and Transfer of Data Collection Inquiry Skills across Physical Science Microworlds. Paper presented at AERA 2012, Vancouver, British Columbia, Canada.

Schunn, C.D., Merlino, F.J., Cromley, J.G., Massey, C.M., Newcombe, N. & Nokes, T.J. (2010). Implementing Best-Practice Methodology Given School Realities: Approaches from a Middle School Science Intervention Evaluation. Paper presented at AERA 2010.

Schwarz, B. B., & Asterhan, C. S. C. (2010). E-moderation of synchronous argumentation: A nascent practice. Paper presented at the 2010 International Conference of the Learning Sciences (ICLS) , Chicago, IL.

Schwarz, B. B., Asterhan, C. S. C., Wang, C., Chiu, M. M., Ching, C. C., Walker, E., Koedinger, K.R., K., Rummel, N., & Baker, M. (2010). Adaptive human guidance of computer-mediated group work. Proceedings of the 2010 International Conference of the Learning Sciences – ICLS 2010

Schwonke, R., Ertelt, A., Renkl, A. & Aleven, V. (2009). The role of procedural metacognitive support for an effective use of multiple representation and multiple information sources in tutored problem solving. Presented as part of "Examining Metacognitive Tools for Supporting Effective Computer-Based Learning Environments" Symposium at EARLI 2009.

Schwonke, R., Ertelt, A., Renkl, A., Aleven, V. & Salden, R. (2009). Reducing extraneous demands in learning from tutored problem solving and embedded worked examples. Proceedings of the 3rd International Cognitive Load Theory Conference (CLT-09). Heerlen, the Netherlands, March 2-4, 2009.

Schwonke, R., Witter, J., Aleven, V., Salden, R., Krieg, C. & Renkl, A. (2007). Can tutored problem solving benefit from faded worked-out examples? Proceedings of The European Cognitive Science Conference, Delphi, Greece, May, 2007, (pp.59-64).

Shih, B., Koedinger, K.R. & Scheines, R. (2008). A Response time model for bottom-out hints as worked examples. 1st International Conference on Educational Data Mining, 2008. [full paper].

Shih, B., Koedinger, K.R., K., & Scheines, R. (2010). Unsupervised Discovery of Student Learning Tactics. Proceedings of the 3rd International Conference on Educational Data Mining, 201-210.

Siler, S. A. & VanLehn, K. (2013). The effect of shared experience on learning outcomes in one-to-one human tutoring. Paper to be presented at the 2013 European Association for Research on Learning and Instruction (EARLI) conference. Munich, Germany.

Siler, S. A., Klahr, D., Magaro, C., & Willows, K. (2012). The effect of instructional framing on learning and transfer of experimental design skills. Paper presented at the 2012 National Association for Research in Science Teaching (NARST) Annual International Conference. Indianapolis, IN.

Siler, S.A., Klahr, D., Magaro, C., & Willows, K. (2012). The effect of instructional framing on learning and transfer of experimental design skills. Paper presented at the 2012 National Science Teachers Association (NSTA) Conference. Phoenix, AZ.

Siler, S.A., Klahr, D., Magaro, C., & Willows, K. (2012). Investigation of causes of goal misinterpretations during a lesson on experimental design. Paper presented at the 8th International Conference on Conceptual Change. Trier, Germany.

Siler, S.A., Klahr, D., Magaro, C., Willows, K., & Mowery, D. (2010). Predictors of transfer of experimental design skills in elementary and middle school children. Proceedings of the 10th ITS 2010 Conference. Lecture Notes in Computer Science, 6095, 198-208.

Siler, S.A., Klahr, D. & Strand-Cary, M. (2009). Adapting an effective lesson plan into a computer-based tutor. Presented as part of "Supporting Science Education by Computer-Based Learning Environments" Symposium, EARLI 2009.

Siler, S.A., Klahr, D., Willows, K, & Magaro, C (2012). The effect of scaffolded causal identification in the transfer of experimental design skills. Paper presented at the Fall 2011 conference for the Society for Research on Educational Effectiveness (SREE). Washington, D.C.

Siler, S.A., Klahr, D., Willows, K., & Magaro, C. (2013). The effect of instructional framing on learning and transfer of experimental design skills. Poster presented at the Annual Meeting of the American Educational Research Association, 2013, San Francisco, California.

Siler, S.A., Klahr, D., Willows, K., & Magaro, C. (2013). The effect of example concreteness on sixth-and seventh-grade students’ learning of experimental design. Paper to be presented at the 2013 European Association for Research on Learning and Instruction (EARLI) conference. Munich, Germany.

Siler, S.A., Klahr, D., Willows, K., & Magaro, C. (2013). The effects of figure abstraction and feature relevance on sixth- through eighth-grade students’ learning and transfer to a math domain. Paper presented at the Spring 2013 conference for the Society for Research on Educational Effectiveness (SREE). Washington, D.C.

Siler, S.A., Mowery, D., Magaro, C., Willows, K., & Klahr, D. (2010). Comparison of a computer-based to a hands-on lesson in experimental design. Proceedings of the 10th ITS 2010 Conference. Lecture Notes in Computer Science, 6095, 408-410.

Singh, A.P. & Gordon, G. (2008). Relational learning via collective matrix factorization. In Proceedings of the 14th Intl. Conf. on Knowledge Discovery and Data Mining (KDD), 2008.

Siskin, C.B. (2006). Revolution Templates for Language Learning (Courseware Showcase) CALICO Symposium, Honolulu.

Siskin, C.B. (2006). Revolution for Non-Programmers, or Yes, There Is Life After HyperCard! NEALLT Conference, Philadelphia.

Siskin, C.B. (2007). Revolution for low-cost data collection in CALL. Paper presented at the Computer Assisted Language Instruction Consortium Conference (CALICO).

Siskin, C.B. (2006). Misconceptions, myths, and metaphors in CALL research. TESOL: CALL IS Acadmeic Session.

Siskin, C.B. & Asay D. (2006). Webware: Creation of Internet-based Multimedia Applications Without Web Browser Hassles. Presented at CALICO 2006, University of Hawaii.

Siskin, C.B. & Asay D. (2006). Rapid Creation of Internet-based Multimedia Applications without Brower Hassles. CALICO Symposium, Honolulu.

Stamper, J. (2011). Paper presented at dataTEL - Datasets for Technology Enhanced Learning Workshop. 2nd STELLAR Alpine Rendez-Vous, March 2011.

Stamper, J., Barnes, T., & Croy, M. (2010). Enhancing the Automatic Generation of Hints with Expert Seeding. Intelligent Tutoring Systmes: Lecture Notes in Compouter Science, 2010, Vol. 6095/2010, 31-40.

Stamper, J., Barnes, T., & Croy, M. (2010). Using a Bayesian Knowledge Base for Hint Selection on Domain Specific Problems. Proceedings of the 3rd International Conference on Educational Data Mining (EDM 2010), 327-8.

Stamper, J., Koedinger, K. & McLaughlin (2013). A Comparison of Model Selection Metrics in DataShop. Proceedings of EDM 2013, 284-287.

Stamper, J., Lomas, D., Ching, D., Ritter, S., Koedinger, K.R. & Steinhart, J. (2012). The Rise of the Super Experiment. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012).

Stamper, J.C. & Koedinger, K.R. (2011). Human-Machine student model discovery and improvement using DataShop. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 353-360. Springer-Verlag Berlin Heidelberg.

Stamper, J.C., Koedinger, K.R., Baker, R.S.J.d., Skogsholm, A., Leber, B., Demi, S., Yu, S., & Spencer, D. (2011). Managing the educational datset lifecycle with DataShop. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 557-559. Springer-Verlag Berlin Heidelberg.

Stampfer, E. & Koedinger, K.R. (2013). When seeing isn't believing: Influences of prior conceptions and misconceptions. In M. Knauff, N. Sebanz, M. Pauen & I wachsmuth (Eds.). Proceedings of the 35th Annual Conference of the Cognitive Science Society. Cognitive Science Society: Austin, TX., 1384-1389.

Stampfer, E., Long, Y., Aleven, V. & Koedinger, K.R. (2011). Eliciting intelligent novice behaviors with grounded feedback in a fraction addition tutor. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 560-562. Springer-Verlag Berlin Heidelberg.

Sudol-DeLyser, L. & Steinhart, J. (2011). Factors impacting novice code comprehension in a tutor for introductory computer science. In M. Pechenizky, T. Calders, C. Conati, S. Ventura, C. Romero & J.C. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Suthers, D., Lund., K., Rosé, C. P., Dyke, G., et al. (2011). Towards Productive Multivocality in the Analysis of Collaborative Learning, in Proceedings of Computer Supported Collaborative Learning

Tsovaltzi, D., McLaren, B., Rummel, N., Scheuer, O., Harrer, A., Pinkwart, N. & Braun, I. (2008). CoChemEx: Supporting conceptual chemistry learning via computer-mediated collaboration scripts. In P. Dillenbourg and M. Specht (Eds.), Proceedings of the Third European Conference on Technology Enhanced Learning (EC-TEL 2008), Lecture Notes in Computer Science 5192 (pp. 437-448). Berlin: Springer.

Tsovaltzi, D., McLaren, B., Rummel, N., Scheuer, O., Harrer, A., Pinkwart, N. & Braun, I. (2008). Using an Adaptive Collaboration Script to Promote Conceptual Chemistry Learning. In B. Woolf, E. Aimeur, R. Nkambou, S. Lajoie (Eds), Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS-08), Lecture Notes in Computer Science, 5091 (pp. 709-711). Berlin: Springer.

Tsovaltzi, D., McLaren, B.M., Melis, E., Meyer, A-K., Dietrich, M., & Goguadze, G. (2010). Learning from Erroneous Examples. Proceedings of Intelligent Tutoring Systems (ITS), 420-422.

Tunc-Pekkan, Z., Rau, M., Aleven, V. & Rummel, N. (2010). External Representations and Fractional Knowledge. Third Annual inter-Science of Learning Center (iSLC) Conference For Students and Postdoctoral Fellows at the Science of Learning Centers, Boston, MA

Tunç-Pekkan, Z., Zeylikman, L., Aleven. V. & Rummel, N. (2010). Fifth Graders’ Conception of Fractions on Numberline Representations. Annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education, Columbus, Ohio.

van de Sande, B. (2013). Applying three models of learning to individual student log data. Proceedings of EDM 2013, 193-199.

van de Sande, B. (2013). Measuring the moment of learning with an information-theoretic approach. Proceedings of EDM 2013, 288-291.

van de Sande, B. & Hausmann, R.G.M. (2007). An Analysis of Student Learning Using the Andes Homework System. Paper presented at the AAPT Summer Meeting, Greensboro, NC, July 2007.

van de Sande, B. & Hausmann, R.G.M. (2008). Does an intelligent tutor homework system encourage beneficial collaboration? Paper presented at the joint Spring Meeting of the Ohio Section of the American Physical Society (OS/APS) and the Western Pennsylvania American Association of Physics Teachers (WPA/AAPT), March 2008, Youngstown State University, Ohio.

van de Sande, B. & Hausmann, R.G.M. (2008). Does an intelligent tutor homework system encourage beneficial collaboration? Paper presented at the Central Pennsylvania Section of the American Association of Physics Teachers (CPS/AAPT), April, 2008, Lock Haven University of Pennsylvania, Lock Haven, PA.

van de Sande, B. & Hausmann, R.G.M. (2008). Does an intelligent tutor homework system encourage beneficial collaboration? Paper presented at the winter meeting of the American Association of Physics Teachers (AAPT), Baltimore, MD

van de Sande, B., Shelby, R., Treacy, D., VanLehn, K. & Wintersgill, M. (2006). Andes: An Intelligent Tutor for Introductory Physics Homework. AAPT Summer Meeting, Syracuse NY.

van de Sande, B., VanLehn, K., Hausmann, R.G.M., Treacy, D. & Shelby, R. (2007). Andes: An Intelligent Homework System for Introductory Physics. Paper presented at the winter meeting of the American Association of Physics Teachers, Seattle, WA.

VanLehn, K. (2008). Explaining the assistance/load/difficulty duality in terms of meta-cognitive learning strategies. Abstract in Symposium: Confronting the Asssistance Dilemma: Is it Better to Give Than Receive? (AERA 2008).

VanLehn, K. (2009). Toward a practical learning theory for step-based tutoring systems. ARI Workshop on Adaptive Training Technologies. Charleston, SC, March 3-5, 2009.

VanLehn, K. (2012). Toward socially intelligent tutoring systems: Of the crowd, for the crowd. Paper presented at Microsoft Research at University of Washington (MSR/UW) Summer Institute on Crowdsourcing Personalized Online Education, July 2012

VanLehn, K. & Burleson, W., Chavez Echeagaray, M-E., Christopherson, R., Gonzalez Sanchez, J., Hastings, J., Hidalgo Pontet, Y. & Zhang, L. (2011). The affective meta-tutoring project: How to motivate students to use effective meta-cognitive strategies. In T. Hirashima et al. (Eds.) Proceedings of the 19th International Conference on Computers in Education. Chiang Mai, Thailand: Asia-Pacific Society for Computers in Education.

VanLehn, K. & Jordan, P. (2008). When is tutorial dialogue more effective than less interactive instruction? Abstract in Symposium: Intelligent Tutoring Systems: What Do We Do Next? (AERA, 2008).

VanLehn, K., Bhembe, D., Chi, M., Lynch, C., Schulze, K., Shelby, R., Taylor, L., Treacy, D., Weinstein, A. & Wintersgill, M. (2004). Implicit versus explicit learning of strategies in a non-procedural cognitive skill. In J. C. Lester, R. M. Vicari, & F. Paraguacu, (Eds.), Intelligent Tutoring Systems: 7th International Conference (pp. 521-530). Berlin: Springer-Verlag Berlin & Heidelberg GmbH & Co. K.

VanLehn, K., Burleson, W., Chavez Echeangary, H., Christopherson, R., Gonzales Sanchez J., Hidalgo Pontet, Y., Muldner, K., & Zhang, L. (2011). The Level Up Procedure: How to Measure Learning Gains Without Pre- and Post-testing. In T. Hirashima et al. (Eds), Proceedings of the 19th International Conference on Computers in Education. Chiang Mai, Thailand: Asia-Pacific Society for Computers in Education.

VanLehn, K., Hausmann, R.G.M. & Craig, S. (2007). Is the “self” of self-explanation important? In vivo experiments. Symposium at the 2007 meeting of the American Educational Research Association, Chicago, IL.

VanLehn, K., Hausmann, R.G.M. & Craig, S. (2007). The role of the self in self-explanation. Symposium at the 12th Biennial Conference for Research on Learning and Instruction, Budapest, Hungary, 2007.

VanLehn, K., Jordan, P. & Litman, D.J. (2007). Developing pedagogically effective tutorial dialogue tactics: Experiments and a testbed. Paper presented at the SLaTE2007 Workshop on Speech and Language Technology in Education, Farmington, PA (October 2007).

VanLehn, K., Lynch, C., Schulze, K., Shapiro, J.A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A. & Wintersgill, M. (2005). The Andes physics tutoring system: Five years of evaluations. In G. McCalla, C. K. Looi, B. Bredeweg & J. Breuker (Eds.), Artificial Intelligence in Education. (pp. 678-685) Amsterdam, Netherlands: IOS Press. Winner of a Best Paper Award.

Waalkens, M., Aleven, V., & Taatgen, N. (2011). Does supporting multiple student strategies in intelligent tutoring systems lead to better learning? In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 572-574. Springer-Verlag Berlin Heidelberg.

Walker, E. (2005). Mutual Peer Tutoring: A Collaborative Addition to the Cognitive Tutor: Algebra-1. In C-K. Looi et al. (Eds.). Proceedings of the 12th International Conference on Artificial Intelligence in Education, p. 979. IOS Press, 2005

Walker, E. (2013). Applying Intelligent Tutoring Principles to a Teachable Robotic Agent for Middle School Mathematics. Paper presented as part of the Beyond Problem Solving: Applying Lessons From Intelligent Tutoring to New Contexts, Domains, and Platforms Roundtable. Proceedings of AERA 2013.

Walker, E. & Ogan, A. (2007). Peer Moderation in Cultural Discussion Forums. Paper presented at European Computer Assisted Language Learning (EuroCALL 2007) Ulster, Northern Ireland, September 2007.

Walker, E. Rummel, N., & Koedinger, K.R. (2011). Adaptive support for CSCL: Is it feedback relevance or increased student accountability that matters? Proceedings of the 9th International Conference on Computer-Supported Collaborative Learning (CSCL 2011), Hong Kong, China.

Walker, E., Koedinger, K.R., McLaren, B. & Rummel, N. (2006). Cognitive Tutors as Research Platforms: Extending an Established Tutoring System for Collaborative and Metacognitive Experimentation. In M. Ikeda, K.D. Ashley, & T-W. Chan (Eds), Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Lecture Notes in Computer Science, 4053 (pp. 207-216). Berlin: Springer.

Walker, E., Leah, L., Ayers, E., Schwartz, R. A. (2010). Assessing a Multidimensional Learning Progression: Psychometric Modeling and Brokering Professional Development. Paper presented in "Coordinated Progress in Conceptual Understanding and Representational Competence" symposium at AERA 2010.

Walker, E., McLaren, B., Rummel, N. & Koedinger, K.R. (2007). Who says three's a crowd? Using a cognitive tutor to support peer tutoring. In R. Luckin, K.R. Koedinger, K.R., & J. Greer (Eds), Proceedings of the 13th International Conference on Artificial Intelligence and Education. 2007. IOS Press. (pp. 399-406).

Walker, E., Ogan, A. & Wylie, R. (2006). A Tense Situation: Applying Cognitive Tutor Methodology to Ill-Defined Domains. Paper presented at European Computer Assisted Language Learning (EuroCALL 2006) Granada, Spain, September 2006.

Walker, E., Ogan, A., Baker, R.S.J.d., de Carvalho, A.M.J.A., Laurentino, T., Rebolledo-Mendez, G., & Jimenez-Castro, M. (2011). Observations of collaboration in Cognitive Tutor use in Latin America. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 575-577. Springer-Verlag Berlin Heidelberg.

Walker, E., Ogan, A., Jones, C., Aleven, V. (2008). Two Approaches for Providing Adaptive Support in an Ill-Defined Domain. Proceedings of the "Intelligent Tutoring Systems for Ill-Defined Domains: Assessment and Feedback in Ill-Defined Domains" Workshop. 9th International Conference on Intelligent Tutoring Systems (ITS) 2008.

Walker, E., Rummel, N, & Koedinger, K. R. (2011). Designing automated adaptive support to improve student helping behaviors in a peer tutoring activity; International Journal of Computer-Supported Collaborative Learning; International Socieity of the Learning Sciences, In.: Springer Science + Business Media, LLC 2011, 10.1007/s11412-011-9111-2

Walker, E., Rummel, N, & Koedinger, K.R. (2011). Using automated dialog analysis to assess peer tutoring and trigger effective support. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 385-393. Springer-Verlag Berlin Heidelberg.

Walker, E., Rummel, N. & Koedinger, K.R. (2008). To tutor the tutor: Adaptive domain support for peer tutoring. In B.P. Woolf, E. Aimeur, R Nkambou, and S.P. Lajoie, (Eds.), Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008), Springer Lecture Notes in Computer Science, 626-635.

Walker, E., Rummel, N. & Koedinger, K.R. (2008). Adaptive Domain Support for Computer-Mediated Peer Tutoring. Appeared in ICLS 2008 as part of the symposium New Challenges in CSCL: Towards adaptive script support, edited by Nikol Rummel, N. and Armin Weinberger.

Walker, E., Rummel, N. & Koedinger, K.R. (2009). Modeling Helping Behavior in an Intelligent Tutor for Peer Tutoring. In V. Dimitrova, R. Mizoguchi, B. du Boulay, & A. Graesser (Eds.). Artificial intelligence in education: Building learning systems that care: From knowledge representation to affective modelling. Frontiers in Artificial Intelligence and Applications, Vol 200 (pp. 341-349). Amsterdam: IOS Press.

Walker, E., Rummel, N. & Koedinger, K.R. (2009). Beyond Explicit Feedback: New Directions in Adaptive Collaborative Learning Support. Proceedings of the 9th International Conference on Computer Supported Collaborative Learning (CSCL-09), 552-556.

Walker, E., Rummel, N. & Koedinger, K.R. (2009). The influence of correct and erroneous worked examples on learning from peer tutoring. As part of the Symposium "In Vivo experimentation on worked examples across domains", EARLI 2009.

Walker, E., Rummel, N., Koedinger, K.R. (2010). Assessing, Modeling, and Supporting Helping Behaviors in Computer-Mediated Peer Tutoring. Proceedings of the “Opportunities for intelligent and adaptive behavior in collaborative learning systems” Workshop, Intelligent Tutoring Systems (ITS) 2010 Conference. Pittsburgh, PA, 25-28.

Walker, E., Rummel, N., Koedinger, K.R. (2010). Automated Adaptive Support for Peer Tutoring in High School Mathematics. Presented as part of Symposium “Human Adaptive Guidance for Group Work” for ICLS.

Walker, E., Rummel, N., McLaren, B. & Koedinger, K.R. (2007). The student becomes the master: Integrating peer tutoring with cognitive tutoring. In C.A. Chinn, G. Erkens & S. Puntambekar (Eds.) Proceedings of the Conference on Computer Supported Collaborative Learning (CSCL-07), Vol. 8, pp. 750-752. International Society of the Learning Sciences, Inc. ISSN 1819-0146.

Walker, E., Rummel, N., Walker, S. & Koedinger, K.R. (2012). Noticing Relevant Feedback Improves Learning in an Intelligent Tutoring System for Peer Tutoring. In S.A. Cerri, W. J. Clancey, G. Papadourakis, K. Panourgia (Eds): Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, Volume 7315/2012, Springer 2012, 222-232.

Walker, E., Walker, S., Rummel, N., Koedinger, K.R.. (2010). Using Problem-Solving Context to Assess Help Quality in Computer-Mediated Peer Tutoring. Intelligent Tutoring Systems: Lecture Notes in Computer Science 2010, Volume 6094/2010, 145-155.

Walkington, C. (2012). Context Personalization in Algebra: Supporting Connections Between Relevant Stories and Symbolic Representations. Presented at "Intervening in Algebra" Roundtable at AERA 2012.

Walkington, C. & Sherman, M. (2012). Using Adaptive Learning Technologies to Personalize Instruction: The Impact of Interest‐Based Scenarios on Performance in Algebra. Proceedings of ICLS2012, Vol 1, 80-87.

Walkington, C., Petrosino, A. & Sherman, M. (2011). The Impact of Personalization on Problem-Solving in Algebra. Paper presented at AERA 2011. [Winner of Graduate Student Research Award.]

Walkington, C., Srisurichan, R., Nathan, M.J., Williams, C.C. & Alibali, M.W. (2012). Grounding Geometry Justifications in Concrete Embodied Experience: The Link Between Action and Cognition. Paper presented at AERA 2012.

Walkington, C.A. & Maull, K. (2010). Exploring the Assistance Dilemma: The Case of Context Personalization. Proceedings of CogSci 2011, 90-95.

Walonski, J.A. & Heffernan, N. (2006). Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems; Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006

Wang, H.C. & Rosé, C.P. (2007). A Process analysis of idea generation and failure. In D.S. McNamara & G. Trafton. Proceedings of the 29th Annual Meeting of the Cognitive Science Society, (1629-1634). Austin TX: Cognitive Science Society.

Wang, H.C., Kumar, R., Rosé, C.P., Li, T.Y. & Chang, C.Y. (2007). A Hybrid Ontology Directed Feedback Generation Algorithm for Supporting Creative Problem Solving Dialogues. Proceedings of the International Joint Conference on Artificial Intelligence.

Wang, H.C., Rosé, C.P., Cui, Y., Chang, C.Y., Huang, C.C. & Li, T.Y. (2007). Thinking Hard Together: the Long and Short of Collaborative Idea Generation in Scientific Inquiry. Proceedings of the Conference on Computer Supported Collaborative Learning (CSCL-07). Rutgers University

Wang, H.C., Rosé, C.P., Li, T. & Chang, C. (2006). Providing Support for Creative Group Brainstorming: Taxonomy and Technologies. Workshop Proceedings on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems, 2006, pp 74-82.

Wang, J. & Juffs, A. (2010). A Bidirectional Corpus Study of Semantics-Syntax Correspondences. Second Language Research Forum, October 2010. University of Maryland.

Wang, Z. (2012). An investigation of additional processing time on-line during L2 speech production. Paper presented at the 31st Second Language Research Forum (SLRF 2012). Pittsburgh, PA.

Warren, M. (2011). The role of repeated grammatical structures in second language fluency. Paper presented at McGill's Canadian Conference for Linguistics Undergraduates, Montreal, QC, March 2011.

Witter, J. & Renkl, A. (2009). Do instructional explanations foster learning from worked-out examples? A cognitive load perspective. Proceedings of the 3rd International Cognitive Load Theory Conference (CLT-09). Heerlen, the Netherlands, March 2-4, 2009.

Wu, S. (2005). "Chinese Online Module: A Cognitive Language Learning Infrastructure". The Annual Meeting of Chinese Language Teachers Association (CLTA/ ACTFL), November 18-20, 2005, Baltimore, Maryland

Wu, S. (2006). Language Online: The Evolution of Web-Delivered Instruction. Paper presented at ACTF, Nashville, Tennessee.

Wu, S. (2006). Interdisciplinary Collaboration for Chinese as a Foreign Language: Running In-Vivo Learning Experiments in Chinese Language Courses. Paper presented at CLTA/ACTFL, Nashville, Tennessee.

Wu, S. (2006). Chinese Cognitive CALL Environment Design: Content and Exercises. Fourth International Conference and Workshops on Technology and Chinese Language Teaching (TCLT4). University of Southern California, Los Angeles.

Wu, S. (2008). Chinese Online: A Hybrid Experience. Proceedings of the 5th International Conference and Workshops on Technology and Chinese Teaching in the 21st Century (TCLT5). pp. 296-302. Macau: University of Macau.

Wu, S. & Haney, M. (2005). Robust Chinese E-learning: Integrating the 5 Cs Principles with Content and Technology. Paper presented at the 4th International Conference on Internet Chinese Education. 2005.

Wu, S. & Haney, M. (2006). Empowering Online Language Learning: The Chinese LearnLab in the Pittsburgh Science of Learning Center. Annual Symposium of Computer Assisted Language Instruction Consortium (CALICO 2006). University of Hawaii.

Wylie, R. (2007). Are we asking the right questions? Understanding which tasks lead to the robust learning of English grammar. Young Researchers Track paper at the 13th International Conference on Artificial Intelligence in Education (2007).

Wylie, R. (2013). Comprehension SEEDING: Using Technology to Enhance Self-Explanation, Classroom Discussion, and Question Generation. Proceedings of AERA 2013.

Wylie, R., Koedinger, K.R. & Mitamura, T. (2009). Practice makes Perfect? Structuring Practice Opportunities for Learning in an ESL Grammar Tutor. Computer Assisted Language Instruction Consortium (CALICO). March 10-14, 2009.

Wylie, R., Koedinger, K.R. & Mitamura, T. (2009). Is Self-Explanation Always Better? The Effects of Adding Self-Explanation Prompts to an English Grammar Tutor. Proceedings of Cognitive Science Society, 2009, 1300-1305.

Wylie, R., Koedinger, K.R. & Mitamura, T. (2009). Self-Explaining Language: Effects of Adding Self-Explanation Prompts to an ESL Grammar Tutor. Presented as part of "In Vivo Experimentation on Self-Explanations Across Domains" Symposium at European Association for Research on Learning and Instruction (EARLI), August 25-29, 2009.

Wylie, R., Koedinger, K.R. & Mitamura, T. (2010). Analogies, Explanation, and Practice: Examining how task type affects second language grammar learning. Intelligent Tutoring Systems: Lecture Notes in Computer Science 2010, Volume 6094/2010, 214-223

Wylie, R., Koedinger, K.R. & Mitamura, T. (2010). Extending the Self-Explanation Effect to Second Language Grammar Learning. In K. Gomez, L. Lyons, & J. Radinsky, (Eds.). ICLS '10: Proceedings of the 9th International Conference of the Learning Sciences, Vol. 1, 57-64. ACM Digital Library.

Wylie, R., Mitamura, T., Rankin, J. & Koedinger, K.R.. (2007). Developing Tutoring Systems for Classroom and Research Use: A Look at Two English Article Tutors. Paper presented at the Computer Assisted Language Instruction Consortium Conference (CALICO).

Wylie, R., Mitamura, T., Rankin, J. & Koedinger, K.R.. (2007). Doing more than Teaching Students: Opportunities for CALL in the Learning Sciences. Proceedings of SLaTE Workshop on Speech and Language Technology in Education. Farmington, Pennsylvania. October 1-3, 2007

Wylie, R., Sheng, M., Mitamura, T. & Koedinger, K. (2011). Effects of adaptive prompted self-explanation on robust learning of second language grammar. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.). Artificial Intelligence in Education (AIED 2011), Lecture Notes in Computer Science, Vol 6738, 588-590. Springer-Verlag Berlin Heidelberg.

Xu, Y. & Mostow, J. (2012). A Dynamic Higher-Order DINA Model To Trace Multiple Skills. In NIPS 2012 Workshop on Personalizing Education With Machine Learning, Lake Tahoe, California.

Xu, Y. & Mostow, J. (2013). Using Item Response Theory to Refine Knowledge Tracing. International Educational Data Mining Society: 356-357, Memphis, TN.

Xu, Y., & Mostow, J. (2012). Comparison of methods to trace multiple subskills: Is LR-DBN best? [Best Student Paper Award]. Proceedings of the Fifth International Conference on Educational Data Mining (EDM 2012), Chania, Crete, Greece.

Yang, C. & Perfetti, C. (2006). Reading skill and the acquisition of high quality representations for new words. Thirteenth Annual Meeting Society for the Scientific Study of Reading, Vanncouver, Canada.

Yannier, N., Koedinger, K.R. & Hudson, S. (2013). Learning with a Mixed-Reality Game: EarthShake. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 131-140. Springer-Verlag Berlin Heidelberg.

Yaron, D. (2006). The ChemCollective: Virtual Labs and Scenario-Based Learning for Introductory Chemistry . Nineteenth Biennial Conference on Chemical Education in West Lafayette, Indiana, p 621.

Yaron, D. (may be other authors--not listed on website) (2008). Digital libraries to support problem solving and conceptual learning in introductory chemistry. Gordon, G. Conference for Physics Research and Education, June, 2008, Smithfield, RI.

Yaron, D., Cuadros, J. & Karabinos, M. (2005). “Virtual Laboratories and Scenes to Support Chemistry Instruction”, in About Invention and Impact: Building Excellence in Undergraduate STEM (Science, Technology, Engineering, and Mathematics) Education, Proceedings from National Science Foundation Course, Curriculum, and Laboratory Improvement (NSF-CCLI) program conference, Arlington, Virginia, 2004, edited and prepared by NSF.

Yaron, D., Davenport, J., Karabinos, M., Leinhardt, G., Bartolo, Portman, Sadoway, Carter, Ashe (2008). Cross-disciplinary molecular science education in introductory science courses: An NSDL MatDL Collection. Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries, Pittsburgh, PA USA. Association for Computing Machinery, Inc. (ACM).

Yaron, D., Evans, K.L., Leinhardt, G., Karabinos, M. et al (2005). “Using the field of chemistry to guide in the development of an on-line stoichiometry course”, American Chemical Society National Meeting, Washington DC, August 2005.

Yaron, D., Karabinos, M., Davenport, J. & Leinhardt, G. (2006). Virtual lab activities for introductory chemistry. Paper presented at the Biennial Conference on Chemical Education, Purdue University, West Layefette, IN.

Yaron, D., Karabinos, M. & Leinhardt, G. (2005). “Using digital libraries to build educational communities: The ChemCollective”, American Chemistry Society National Meeting, San Diego, March 2005.

Yaron, D., Karabinos, M., Leinhardt, G., Davenport, J. & Greeno, J. (2007). Making the implicit explicit in the teaching of chemical equilibrium. Gordon, G. Conference on Chemical Education Research and Practice, invited paper.

Yaron, D., Leinhardt, G., Evans, K.L., Cuadros, J., Karabinos, M., McCue, W. & Dennis, D. (2006). Creation of an online stoichiometry course that melds scenario based learning with virtual labs and problem-solving tutors. Paper Presented on CONFCHEM. Online Conference, Spring 2006.

Youngs, B. (2007). Ruminations of a hybrid course instructor. Paper presented at the Computer Assisted Language Instruction Consortium Conference (CALICO), San Macos, TX.

Yudelson, M. & Brunskill, M. (2012). Policy Building -- An Extension To User Modeling. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012).

Yudelson, M. & Koedinger, K. (2013). Estimating the benefits of student model improvements on a substantive scale. Proceedings of EDM 2013, 358-359.

Yudelson, M. V., Koedinger, K.R. & Gordon, G.J. (2013). Individualized Bayesian Knowledge Tracing Models. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 161-170. Springer-Verlag Berlin Heidelberg.

Yudelson, M., Pavlik, P. & Koedinger, K. (2011). Towards better understanding of transfer in cognitive models of practice. In M. Pechenizky, T. Calders, C. Conati, S. Ventura, C. Romero & J.C. Stamper (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011).

Yudelson, M., Pavlik, P. & Koedinger, K. (2011). User Modeling – a Notoriously Black Art. In J.A. Konstan, R. Conejo, J.L. Marzo, & N. Oliver (Eds.). Proceedings of User Modeling, Adaptation and Personalization Conference (UMAP 2011), Lecture Notes in Computer Science, Vol. 6786/2011, 317-328.

Zawadzki, E., Gordon, G. & Platzer, A. (2011). An Instantiation-Based Theorem Prover for First-Order Programming. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS) 2011, Fort Lauderdale, FL. Volume 15 of JMLR: W&CP 15.

Zhang, Mostow, J., Beck, J. (2008). A Comparison of three methods to evaluate tutorial behaviors. Paper presented at the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008).

Zhang, X., Mostow, J. & Beck, J.E. (2007). All in the (word) family: Using learning decomposition to estimate transfer between skills in a reading tutor that listens. Proceedings of Workshop on Educational Data Mining (AIED 2007).

Zhang, Y., Li, X., Zhang, D. & Li, L. (2007). SLA research for empirically-driven innovations in CSL studies. Paper presented at the American Council on the Teaching of Foreign Languages (ACTFL) Annual Meeting, 2007. <http://citation.allacademic.com/meta/p182336_index.html>

Zhao, Y. & MacWhinney, B. (2010). Competing cues: A corpus-based study of English tense-aspect acquisition. BUCLD Proceedings 34: 503-514.

Zhao, Y., Koedinger, K.R. & Kowalski, J. (2013). Knowledge tracing and cue contrast: Second language English grammar instruction. In In M. Knauff, N. Sebanz, M. Pauen & I wachsmuth (Eds.). Proceedings of the 35th Annual Conference of the Cognitive Science Society. Cognitive Science Society: Austin, TX., 1653-1658.

== Poster Presentations ==

Adams, D.M., McLaren, B.M., Mayer, R. E., Goguadze, G. & Isotani, S. (2013). Erroneous Examples as Desirable Difficulty: A Study Showing a Delayed Learning Effect. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 2013, 803-806. Springer-Verlag Berlin Heidelberg.

Aleahmad, T., Koedinger, K., & Zimmerman, J. (2011). Design-based research from the start: A process for innovation at the convergence of learning theory and contextual observation. Computer Supported Collaborative Learning. July 4, 2011. The University of Hong Kong, Hong Kong, China. [poster]

Anthony, L. (2006). Exploration of the Effects of Handwriting on Learning in Algebra Equation Solving. Science of Learning Centers Symposium, Atlanta, Georgia.

Anthony, L. (2006). Exploration of the Effects of Handwriting on Learning in Algebra Equation Solving. Human-Computer Interaction Institute 12th Anniversary, Carnegie Mellon University.

Asterhan, C. S. C., & Schwarz, B. B. (2010). Assisting the facilitator: Striking a balance between intelligent and human support of computer-mediated discussions. Proceedings of the the “Opportunities for intelligent and adaptive behavior in collaborative learning systems” Workshop, Intelligent Tutoring Systems (ITS) 2010 Conference. Pittsburgh, PA

Ayers, Nugent, Dean (2008). Skill set profile clustering based on weighted stuent responses. 1st International Conference on Educational Data Mining, 2008. [poster-young researchers' track].

Belenky, D. M. & Nokes, T. J. (2009). How achievement goals and instructional activities interact to promote or hinder transfer of knowledge. Poster presented at the 50th Annual Meeting of the Psychonomic Society: Boston, MA.

Belenky, D., Gadgil, S., Richey, E., Nokes-Malach, T. & Levine, J. (2011). The Role of engagement in learning form dialectical interaction. Poster presented at Socializing Intelligence Through Academic Talk and Dialogue: Invitational AERA Research Conference. University of Pittsburgh, September 22-25, 2011.

Belenky, D., Nokes, T. & Bernacki, M. (2011). Achievement goals and learning in a lecture course: Moving towards mastery goals predicts deeper learning. Proceedings of CogSci 2011, 755.

Booth, J. & Olsen, J.K. (2009). Encoding of equation features relates to conceptual and procedural knowledge of algebra. Poster presented at the meeting of the Society for Research in Child Development, Denver, CO.

Booth, J., Koedinger, K.R. & Siegler, R. (2007). The effect of corrective and typical self-explanation on algebraic problem solving. Poster presented at the Science of Learning Centers Awardee’s Meeting in Washington, DC, October, 2007.

Booth, J., Koedinger, K.R. & Siegler, R. (2008). Using self-explanation to improve algebra learning. In B.C. Love, K. McRae, & V.M. Sloutsky (Eds.), Proceedings of the 30th Annual Cognitive Science Society, p. 2395. Jaustin, TX: Cognitive Science Society. [abstract].

Brown, J. & Eskenazi, M. (2004). Retrieval of Authentic Documents for Reader-Specific Lexical Practice. Proceedings of InSTIL/ICALL Symposium. 2004.

Butcher, K. & Aleven, V. (2007). Visual-verbal coordination: Diagram interaction promotes robust learning in geometry. Poster presented at the Science of Learning Centers Annual Meeting, Arlington, VA.

Butcher, K. & Bhushan, S. (2005). Using strand maps to engage digital library users with science content (Poster presentation). 5th ACM/IEEE-CS joint conference on Digital libraries, p. 371. New York: Association for Computing Machinery.

Butcher, K. & Bhushan, S. (2005). Learning with scientific visualizations: Effects of background knowledge and interactivity. Poster presentation. American Educational Research Association 2005.

Butcher, K., Aleven, V. (2008). Concept training and deep knowledge assessment: Using CTAT in the classroom. Poster presented at the Open Learning Interplay Symposium 2008, Carnegie Mellon University, Pittsburgh, PA.

Cao, F., Vu, M., Chan, H., Lawrence, J., Harris, L., Guan, Q., Xu, Y., & Perfetti, C. A. (2010). Neural correlates of writing training in learning Chinese. Poster session presented at the 40th Society of Neuroscience Annual Meeting, San Diego, CA.

Catz, K.N., Crowell, A., Burmester, K.O., Schunn, C.D. & Dorph, R. (2012). Scientific Sense Making in Context. Poster presented at "Activating Young Science Learners: Igniting Persistent Engagement in Science Learning and Inquiry" Structured Poster Session at AERA 2012.

Chang, A., Strohm, E., Nokes, T. J. & Schunn, C. D. (2009). Using cognitive science to improve middle school science learning. Poster presented to the 50th Annual Meeting of the Psychonomic Society: Boston, MA.

Chen G., Resnick, L. B. & Michaels, S. (2012). Comparing human and machine coding of teacher accountable talk. Poster presented at the annual Inter-Science of Learning Center meeting, San Diego, CA, USA.

Chen G., Resnick, L. B., Michaels, S. & O’Connor, C. (2011). A graphical representation of teacher-led classroom talk. Poster presented at the AERA Research Conference (Socializing Intelligence Through Academic Talk and Dialogue). Pittsburgh, PA, USA.

Chen G., Resnick, L. B., Michaels, S. & O’Connor, C. (2012). A visual display of teacher-led talk in a science class. Poster presented at the 4th Biennial Conference of the International Society for the Psychology of Science and Technology (ISPST). Pittsburgh, PA, USA.

Chen, G., Gaowei, Resnick, L., Michaels, S., & O'Connor, M.C. (2011). A New method for analyzing teacher-led classroom talks. Poster presented at Socializing Intelligence Through Academic Talk and Dialogue: Invitational AERA Research Conference. University of Pittsburgh, September 22-25, 2011.

Chen, G., Jain, M., Gweon, G., & Mayfield, E. (2012). Automatic analysis of discussion for learning. Poster presented at the Pittsburgh Science of Learning Center’s Board of Visitors Meeting, Pittsburgh, PA, USA.

Chi, M. & Jordan, P., VanLehn, K., Hall (2008). Reinforcement learning-based feature selection for developing pedagogically effective tutorial dialogue tactics. 1st International Conference on Educational Data Mining, 2008. [best poster-young researchers' track award].

Clarke, S. (2011). Entering the discussion: Understanding student engagement in class discussions. Poster presented at Socializing Intelligence Through Academic Talk and Dialogue: Invitational AERA Research Conference. University of Pittsburgh, September 22-25, 2011.

Corbett, A., Wagner, A., Chao, C., Lesgold, S., Stevens, S. & Ulrich, H. (2005). Student Question-Asking Behavior in a Classroom Evaluation of the ALPS Learning Environment. 12th Annual Conference on Artificial Intelligence in Education. 2005. Poster.

Cuadros, J., Yaron, D., Karabinos, M. & Leinhardt, G. (2006). find this

Davenport, J., Klahr, D. & Koedinger, K.R. (2006). The influence of external representations on chemistry problem solving. Poster presented at the Forty-seventh Annual Meeting of the Psychonomic Society in Houston, Texas. November 2006.

Davenport, J., Yaron, D., Klahr, D., Koedinger, K.R. (2008). Coordinating chemistry concepts with problem solving to enhance learning. Poster presented at the Open Learning Interplay Symposium in Pittsburgh, PA, March 2008.

Davenport, J., Yaron, D., Koedinger, K.R., Klahr, D. (2008). Development of Conceptual Understanding and Problem Solving Expertise in Chemistry. Proceedings of the 30th Annual Meeting of the Cognitive Science Society, July 2008 [poster].

Diziol, D., Rummel, N., Spada, H. (2008). Introducing collaboration to the Algebra Cognitive Tutor: Differential effects on three robust learning measures. Poster presented at the 1st Inter-Science of Learning Center Student and Post-Doc (iSLC) 2009. Pittsburgh, PA, USA.

Diziol, D., Rummel, N., Spada, H. (2008). Introducing collaboration to the Algebra Cognitive Tutor: Differential effects on three robust learning measures. Poster presented at the 1st Inter-Science of Learning Center Student and Post-Doc (iSLC) 2009. Pittsburgh, PA, USA.

Dunlap, S. (2010). Spelling in English as a second language: Do students make different types of errors on different types of tasks? Poster presented at the 3rd annual meeting of the iSLC, Boston, Massachusetts.

Dunlap, S., Friedline, B., Juffs, A. & Perfetti, C. (2009). Lexical quality of English second language learners: Effects of focused training on orthographic encoding skill. Poster presented at the 2nd annual meeting of the iSLC, Seattle, Washington, February, 2009.

Dunlap, S., Liu, Y., Chen & Perfetti, C. (2005). Classroom learners of Chinese as a second language: Testing online study methods. Poster presented at the Pitt-CMU Conference, Pittsburgh Pennsylvania.

Dunlap, S. & Perfetti, C. (2009). Effects of explicit instruction on Chinese character learning. Poster presented at the Georgetown University Round Table on Languages and Linguistics, Washington, D.C., March 2009.

Dunlap, S., Perfetti, C. Liu, Y. & Wu, S. (2007). Rules and exceptions: Strategies for learning vocabulary in Chinese as a second language. Poster presented at the meeting of the American Educational Research Association, Chicago, IL., 2007.

Feng, M., Heffernan, N., Beck, J., Koedinger, K.R. (2008). Can we predict which groups of questions students will learn from? 1st International Conference on Educational Data Mining, 2008. [poster-young researchers' track].

Gadgil, S., Richey, J.E., Belenky, D., Nokes-Malach, T. & Levine, L. (2011). Using convergent methodologies to understand student engagement and learning in a debate. Poster presented at Socializing Intelligence Through Academic Talk and Dialogue: Invitational AERA Research Conference. University of Pittsburgh, September 22-25, 2011.

Gobert, J.D. & Koedinger, K.R. (2012). Using Model-tracing to Conduct Performance Assessment of StudentsScience Inquiry Skills Within a Microworld. Poster presented at AERA 2012.

Greeno, J., MacWhinney, B. (2006). Learning as perspective taking: Conceptual alignment in the classroom. Proceedings of the 7th International Conference of the Learning Sciences, Bloomington, IN. [poster], 930-931.

Greeno, J., MacWhinney, B. (2006). Perspectives in reasoning about quantities. Proceedings of the annual meeting of the Cognitive Science Society, Vancouver, BC. [poster], page 2495.

Hausmann, R.G.M. (2007). The effect of generation on robust learning. Poster presented at the annual meeting of the Science of Learning Centers, Washington, D.C.

Hausmann, R.G.M. & VanLehn, K. (2007). A test of the interaction hypothesis: Joint-explaining vs. self-explaining. Poster presented at the Physics Education Research Conference, Greensboro, NC.

Hausmann, R.G.M. & VanLehn, K. (2007). Self-explaining in the classroom: Learning curve evidence. Poster presented at the Physics Education Research Conference, Greensboro, NC.

Hausmann, R.G.M. & VanLehn, K. (2007). A test of the interaction hypothesis: Joint-explaining vs. self-explaining. In D. McNamara & G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum, 1770.

Hausmann, R.G.M., Nokes, T.J., VanLehn, K. & van de Sande, B. (2009). Collaborative dialog while studying worked-out examples. Proceedings of the International Conference on Artificial Intelligence in Education (AIED 2009), Brighton, England.

Hausmann, R.G.M., van de Sande, B. & VanLehn, K. (2008). The content of self-explanations while studying incomplete worked-out examples. Poster presented at the 30th meeting of the Cognitive Science Society, Washington, DC., July 2008.

Heilman, M., Eskenazi, M. (2008). Self-assessment in vocabulary tutoring. Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008), 656-658. Springer Berlin/Heidelberg.

Heilman, M., M.J. (2010). Advancing Educational Technologies with Statistical Models of Sentence Structure Transformations. Poster presented at AERA.

Heilman, M., Zhao, Pino, J., Collins-Thompson, K., Callan, J., Eskenazi, M., Perfetti, C. & Juffs, A. (2008). Providing Appropriate Texts for Language Learners. Poster presented at the IES Research Conference (IES), 2008.

Hershkovitz, A., Baker, R.S.J.d., Moore, G.R., Rossi, L.M. & van Velsen, M. (2013). The Interplay between Affect and Engagement in Classrooms Using AIED Software. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 2013, 587-590. Springer-Verlag Berlin Heidelberg.

Howley, I., Adamson, D., Kumar, R., Dyke, G. & Rosé, C. (2011). Promoting Accountable Talk in group discussion with intelligent dialogue tutors. Poster presented at Socializing Intelligence Through Academic Talk and Dialogue: Invitational AERA Research Conference. University of Pittsburgh, September 22-25, 2011.

Juffs, A., Wilson, L., Eskenazi, M., Callan, J., Brown, J., Collins-Thompson, K., Heilman, M., Pelletreau, T. & Sanders, J. (2006). Robust learning of vocabulary: investigating the relationship between learner behavior and the acquisition of vocabulary. Poster presented at the 40th Annual TESOL International Conference, 2006.

Kallai, A. Y., Ponting, A., Schunn, C. D., & Fiez J. A. (2011). Critical constituents of reward-based learning in an arithmetic training. Poster presented at The 18th Annual Meeting of the Cognitive Neuroscience Society (CNS), San-Francisco, California

Kallai, A. Y., Schunn, C. D., & Fiez, J. A. (2011). An fMRI study of Arithmetic training: different activation patterns of basal ganglia due to differences in training procedures. Poster presented at The 52st Annual Meeting of the Psychonomic Society, Seattle, Washington

Kallai, A. Y., Schunn, C. D., & Fiez, J. A. (2012). Automaticity in Processing of Numbers That Were Never Presented: An fMRI Study. Poster presented at The 19th Annual Meeting of the Cognitive Neuroscience Society (CNS), Chicago, Illinois

Kasman, E., Retterer-Moore, J., Xia, T., Nelson, J., Chang, K.-m., & Mostow, J. (2012). (2012). How could brainwave information be useful to an automated reading tutor? [Poster]. Paper presented at the PSLC Summer Intern Poster Session, Carnegie Mellon University.

Katz, S. (2006). A Comparison of three modes of reflective dialogue. Poster presented at American Association of Physics Teachers (AAPT) meeting, 2006.

Katz, S., Connelly, J. & Wilson, C. (2007). Out of the lab and into the classroom: An Evaluation of Reflective Dialogue in Andes. Poster presented at the Physics Education Research Conference (PERC 2007), Greensboro, NC.

Katz, S., Connelly, J., Wilson, C. & Goedde (2006). Post-Practice Dialogues in an Intelligent Tutoring System for College-Level Physics. AAPT 2006. Poster.

Lange, K.E., Booth, J.L. & Koedinger, K.R. (2012). Differentiating Between Correct and Incorrect Examples for ImprovingStudent Learning in Algebra. Poster presented at AERA 2012.

Li, N. & Latecki, L.J.J. (2012). Clustering Aggregation as Maximum-Weight Independent Set. Neural Information Processing Systems Foundation (NIPS) 2012.

Liu, Y., Guan, C., Chan, D., Wu, S. & Perfetti, C. (2009). Writing to foster reading in Chinese. Poster presented at the Second Annual Inter-Science of Learning Center Student and Post-Doc Conference. University of Washington, Seattle, WA. February 5-7.

Liu, Y., Guan, Chan, Wu, Perfetti, C. (2008). The Effects of Character-writing on Reading Skill Development: An Experiment in Chinese. Poster presented at the Third International Conference on Cognitive Science, Moscow, Russia, June 20-26, 2008

Lovett, M., Meyer, O., & Thille, C. (2009). "Measuring the Effectiveness of the OLI Statistics Course in Accelerating Student Learning” Poster presented at the National Center for Academic Transformation Conference. March 22-24, Orlando FL.

Maass, J.K. & Pavlik, P.I. (2013). Utilizing Concept Mapping in Intelligent Tutoring Systems. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 2013, 880-883. Springer-Verlag Berlin Heidelberg

Martin, K. I., & Juffs, A. (2012). L1 Affects Eye-Movements and Sensitivity to Vowels in L2: Evidence from Eye-Tracking. Poster presented at the Fifth Annual inter-Science of Learning Center Student and Post-Doc Conference, Temporal Dynamics of Learning Center (TDLC), San Diego, CA.

Martin, K. I., & Juffs, A. (2012). Reading in English: A Comparison of Native Arabic, Native Chinese, and Native English Speakers. Poster presented at International Symposium on Bilingualism 8, University of Oslo, Oslo, Norway.

Mayfield, E., Dyke, G., Gweon, Gahgene, Howley, I., & Rosé, C. (2011). Automating sociolinguistic analysis of group interaction. Poster presented at Socializing Intelligence Through Academic Talk and Dialogue: Invitational AERA Research Conference. University of Pittsburgh, September 22-25, 2011.

McLaren, B., Rummel, N. et al (2005). Improving algebra learning and collaboration through collaborative extensions to the Algebra Cognitive Tutor. Poster presented at the Conference on Computer Supported Collaborative Learning (CSCL-05), May 2005, Taipei, Taiwan.

Mostow, J. & Beck, J. (2009). What, How, and Why should Tutors Log? Proceedings of the 2nd International Conference on Educational Data Mining (EDM 2009), 269-278.

Muldner, K., Burleson, W., van de Sande, B. & VanLehn, K. (2010). Fun and Gaming with Andes. Poster presented at the AAPT Summer meeting, Portland Oregon, July 2010.

Nokes, T. J., Mestre, J. P., Ross, B. H., Richey, J. E. (2010). Conceptual analysis and student learning in physics. Poster presented at the 2010 Institute for Education Sciences Research Conference: Washington, DC.

Nokes, T.J., Ross, B.H., Mestre, J.P., Strohm, E., Brookes, D.T., & Feil, A. (2009). Conceptual analysis facilitates learning and transfer in both laboratory and classroom settings. Poster presented to the 50th Annual Meeting of the Psychonomic Society: Boston, MA.

Nokes, T.J., VanLehn, K. (2008). Bridging principles and examples through analogy and explanation. In P. A. Kirschner, F. Prins, V. Jonker, G. Kanselaar, G. (Eds.), Proceedings of the Eighth International Conference for the Learning Sciences, ICLS 2008. Vol. 3, 100-102. ISLS, The Netherlands.

Nokes, T.J., VanLehn, K. & Belenky, D.M. (2008). Coordinating principles and examples through analogy and explanation. Poster presented at the Thirtieth Annual Conference of the Cognitive Science Society: Washington, DC.

Olsen, J., Belenky, D., Aleven, V. & Rummel, N. (2013). Intelligent Tutoring Systems for Collaborative Learning: Enhancements to Authoring Tools. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 900-903. Springer-Verlag Berlin Heidelberg.

Oyer, M.H., Booth, J.L. & Elliot, A. J. (2012). Investigating Motivational Predictors of Traditional and Example-Based Algebra Learning. Poster presented at AERA 2012.

Pavlik, P. (2008). Classroom Testing of a Discrete Trial Practice System. Poster presented at the 34th Annual Meeting of the Association for Behavior Analysis, Chicago, IL, (May, 2008).

Pavlik, P. & Koedinger, K.R. (2009). Understanding the Advantages of Retrieval for Long-term Retention Using Modeling. Poster presented at the 50th Annual Meeting of the Psychonomic Society, Boston, MA.

Pavlik, P., Cen, H., Wu, S. & Koedinger, K.R. (2008). Automatic determination of skill models from existing tutor data. Poster presented at the Institute of Education Science Research Conference (IES), Washington, D.C.

Presson, N. & Heilman, M. (2010). An enactive, computerized practice interface for using Spanish prepositions. Poster presentation at the annual meeting of the Institute of Education Sciences, Washington DC. [poster].

Presson, N. & MacWhinney, B. (2010). The Influence of time pressure on the effects of rule instruction and highlighting relevant cues. Second Language Research Forum (SLRF 2010). Poster.

Presson, N., MacWhinney, B, & Heilman, M. (2010). An embodied interface for practicing second-language prepositions. Joint Meeting of Conceptual Structure, Discourse and Language (CSDL) and Embodied and Situated Language Processing (ESLP), [poster].

Presson, N., MacWhinney, B. (2008). Contrasting explicit and implicit instruction for grammatical categorization. Poster presented at the IES Research Conference (IES), 2008.

Presson, N., MacWhinney, B. (2008). An adaptive tutor for explicit instruction of French grammatical gender cues. Poster presentation at the annual meeting of the Institute of Education Sciences, Washington DC.

Rau, M., Aleven, V. & Rummel, N. (2013). How to use multiple graphical representations to support conceptual learning? Research-based principles in the Fractions Tutor. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 2013, 587-590. Springer-Verlag Berlin Heidelberg.

Richey, J.E., Chang, A, Nokes, T.J., Schunn, C. (2010). Using analogical learning in science curricula to improve conceptual understanding. In S. Ohlsson & R. Catrambone (Eds.). Proceedings of the 32nd Annual Conference of the Cognitive Science Society, 662. Austin, TX: Cognitive Science Society.

Salden, R., Aleven, V. & Renkl, A. (2007). Can tutored problem solving be improved by learning from examples? Proceedings of the 29th Annual Meeting of the Cognitive Science Society. (p. 1847). (CogSci 2007).

Salden, R., Aleven, V., Schwonke, R. & Renkl, A. (2008). Are worked examples and tutored problem solving synergistic forms of support? Proceedings of the 8th International Conference of the Learning Sciences (ICLS), June 2008.

Salden, R., Aleven, V., Schwonke, R. & Renkl, A. (2009). Exploring worked examples in tutored problem solving. Proceedings of the 31st meeting of the Cognitive Science Society, 950.

Salden, R., Aleven, V., Renkl, A., Schwonke, R. & Witter, J. (2007). Does Learning from Examples Improve Tutored Problem Solving? Proceedings of the 28th Annual Meeting of the Cognitive Science Society, p. 2602. Poster.

Sha, L., Schunn, C.D. & Bathgate,M. (2012). Activated Science Learners as Self-Regulation Agents. Poster presented at "Activating Young Science Learners: Igniting Persistent Engagement in Science Learning and Inquiry" Structured Poster Session at AERA 2012.

Siler, S. A., Klahr, D., Willows, K., Magaro, C., & Mowery, D. (2011). The effect of prompted causal identification in transfer of experimental design skills. Proceedings of the 33rd Annual Meeting of the Cognitive Science Society (CogSci 2011), Boston, MA, 2242. Poster.

Stampfer, E., & Koedinger, K.R. (2013). Conceptual Scaffolding to Check One’s Procedures. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.). Proceedings of AIED 2013, LNAI 7926, 916-919. Springer-Verlag Berlin Heidelberg.

Torres Olague, D., Yuan, Y., Chang, K.M., & Mostow, J. (2013). Can EEG detect when a student needs help? PSLC Summer Intern Poster Session, Carnegie Mellon University.

van de Sande, B., Shelby, R., Treacy, D. & VanLehn, K. (2007). Changing Student Attitudes using Andes, An Intelligent Homework System. Poster presented at the AAPT Winter Meeting, Seattle WA, January 2007.

van de Sande, B., Shelby, R., Treacy, D., VanLehn, K. & Wintersgill, M. (2007). Andes: An Intelligent Tutor Homework System. Poster presented at the AAPT Summer Meeting, Greensboro, NC, July 2007.

VanLehn, K., Koedinger, K.R., Skogsholm, Nwaigwe, A., Hausmann, R.G.M., Weinstein, Billings (2007). What’s in a step? Toward general, abstract representations of tutoring system log data. In C. Conati & K. McCoy (Eds). Proceedings of User Modelling 2007.

Vercellotti, M. & De Jong, N. (2009). “I prefer go”: English L2 Verb Complement Errors. Poster presented at the Georgetown University Round Table, Washington, D.C., March 2009

Vercellotti, M. & De Jong, N. (2009). “I always dessert cake to diet”: Elicited Imitation as an L2 task. Poster presented at the Second Annual Inter-Science of Learning Center Conference, Seattle, WA, February 2009.

Vercellotti, M., De Jong, N. (2010). How does fluency training in the ESL classroom affect language complexity? Poster presented at the iSLC conference, May 2010.

Walkington, C. (2010). American Educational Research Association Annual Meeting Poster Presentation (April 2010): “Playing the Game” of Story Problems: Situated Cognition in Algebra Problem Solving.

Walkington, C. (2011). Cognitive Science Society Poster Presentation (July, 2011): Adolescent Reasoning in Mathematics: Exploring Middle School Students’ Strategic Approaches in Empirical Justification.

Walkington, C., Sherman, M., Petrosino, A. (2010). Playing the Game of Story Problems: Situated Cognition in Algebra Problem Solving. Paper presented at AERA 2010.

Wang, Y.C., Joshi, M. & Rosé, C.P. (2007). A Feature Based Approach for Leveraging Context for Classifying Newsgroup Style Discussion Segments, Proceedings of the Association for Computational Linguistics (poster).

Wang, Y.C., Joshi, M., Rosé, C.P., Fischer, F., Weinberger, A. & Stegmann, K. (2007). Context Based Classification for Automatic Collaborative Learning Process Analysis [poster]. In Proceedings of AIED 2007.

Wang, Y.C. & Rosé, C.P. (2007). Supporting collaborative idea generation: A closer look using statistical process analysis techniques. Proceedings of AIED 2007 (poster).

Wang, Z., de Jong, N. & Perfetti, C. (2013). Robustness in learning L2 speaking through repetition: Evidence from speech fluency, complexity, and accuracy. Poster presented at the Sixth Annual inter-Science of Learning Center Student and Post-Doc Conference (iSLC), NSF Science of Learning Centers (Philadelphia).

Wylie, R. (2007). Small words, big challenges: Identifying the difficulties in learning the English article system. Poster presented at the IES Research Conference, Washington, DC, june, 2007 [pre-doctoral student poster].

Wylie, R. (2008). Making a priori predictions about English as a Second Language grammar learning. Poster presented at the IES Research Conference, Washington, DC, June 2008. [pre-doctoral student poster].

Zepeda, C., Richey, J. E., Ronevich, P. & Nokes-Malach, T. J. (2012). Explicit instruction of metacognition and its benefits to motivation and science learning. Poster presented at the 2012 Annual Meeting of the Advancing Hispanics/Chicanos & Native Americans in Science, Seattle, WA.

Zhang, X., Mostow, J., Duke, N., Trotochaud, C., Valeri, J. & Corbett, A. (2008). Mining free-form spoken responses to tutor prompts. 1st International Conference on Educational Data Mining, 2008. [poster-young researchers' track].

== Technical Reports ==

Anthony, L., Yang, J. & Koedinger, K.R. (2006). Entering Mathematical Equations Multimodally: Results on Usability and Interaction Design. Technical Report CMU-HCII-06-101, 15 Mar 2006.

Anthony, L., Yang, J. & Koedinger, K.R. (2006). Entering Mathematical Equations Multimodally: Results on Usability and Interaction Patterns, Technical Report CMU-HCII-06-101, 15 Mar 2006.

Anthony, L., Yang, J. & Koedinger, K.R. (2008). How Handwritten Input Helps Students Learning Algebra Equation Solving. Technical Report CMU-HCII-08-100, 1 Mar 2008.

Koedinger, K.R., Corbett, A. & Perfetti, C. (2010). The Knowledge-Learning-Instruction (KLI) Framework: Toward Bridging the Science-Practice Chasm to Enhance Robust Student Learning. Technical Report CMU-HCII-10-102, Human Computer Interaction Institute, Carnegie Mellon University. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html.

Matsuda, N., Cohen, W., Sewall, J., Koedinger, K.R. (2006). Applying Machine Learning to Cognitive Modeling for Cognitive Tutors, Technical Report CMU-ML-06-105, School of Computer Science, Carnegie Mellon University

Matsuda, N., Cohen, W., Sewall, J., Koedinger, K.R. (2006). What characterizes a better demonstration for cognitive modeling by demonstration? Technical Report CMU-ML-06-106, School of Computer Science, Carnegie Mellon University.

McLaren, B. (2005). Lessons in Machine Ethics from the Perspective of Two Computational Models of Ethical Reasoning; AAAI Fall 2005 Symposium, Washington, D. C. In "Papers from the AAAI Fall Symposium," Technical Report FS-05-06, pp. 70-77.

Singh, A.P. & Gordon, G. (2008). Relational Learning via Collective Matrix Factorization. Technical Report CMU-ML-08-109.

== Theses ==
Aleahmad, T. (2012). Improving Students’ Study Practices Through the Principled Design of Research Probes, Thesis defense, CMU, April 27, 2012.

Anthony, L. (2008). Developing Handwriting-based Intelligent Tutors to Enhance Mathematics Learning. Ph.D. Thesis, Human-Computer Interaction Institute, School of Computer Science, Carnegie Mellon University. CMU-HCI-08-105.

Belenky, D.M. (2012). The Effect of Achievement Goals on Self-Explanation and Transfer: Investigating the Role of Motivation on Learning. PhD Thesis. Human Computer Interaction Institute, Carnegie Mellon University.

Cen, H. (2009). Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning. Doctoral thesis: CMU-ML-09-102.

Diziol, D. (2006). Development of a collaboration script to improve students` algebra learning when solving problems with the Algebra I, Cognitive Tutor. Diploma Thesis. Albert-Ludwigs-Universität Freiburg, Germany: Institute of Psychology, June 2006.

Easterday, M. (2010). A Cognitive Game for Teaching Policy Argument, or, The Young Citizen's Illustrated Primer, PhD Thesis: Human Computer Interaction Institute, Carnegie Mellon University.

Galyardt, A. (2012). Mixed Membership Distributions with Applications to Modeling Multiple Strategy Usage. July 17, 2012

Golden, E. (2010). Early-stage Software Design for Usability, PhD Thesis: Human Computer Interaction Institute, Carnegie Mellon University.

Goldin, I. (2011). A Focus on Content: The Use of Rubrics in Peer Review to Guide Students and Instructors. Friday, April 29, 2011. Intelligent Systems Program, University of Pittsburgh.

González-Brenes, J. P. (2013). What and When Do Students Learn? Methods For Knowledge Tracing With Data-Driven Mapping of Items to Skills. PhD Thesis, Language Technologies Institute (LTI), Carnegie Mellon University, August 2, 2013.

Gweon, G. (2012). Assessment and support of the idea co-construction process that influence collaboration. Human Computer Interaction Institute, Carnegie Mellon University. Thesis Defense. April 2012.

Hausmann, R.G.M. (2005). Elaborative and Critical Dialog: Two Potentially Effective Problem-Solving and Learning Interactions. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, University of Pittsburgh, 2005.

Heilman, M.J. (2011). Automatic Factual Question Generation for Reading Assessment. PhD Thesis, Carnegie Mellon University.

Kao (2009). Contributions of Spatial Skills to Geometry Achievement: Training and Transfer

Kumar, R. (2011). Socially Capable Conversational Agents in Multi-Party Interactive Situations. PhD Thesis, August 2011: Language Technologies Institute, Carnegie Mellon University.

Nelson, J.R. (2010). Reading skill and components of word knowledge affect eye movements during reading. Submitted to the Graduate Faculty of the Department of Psychology in partial fulfillment of the requirements for the degree of Doctor of Philosophy, University of Pittsburgh.

Ogan, A. (2011). Supporting Learner Social Relationships with Enculturated Pedagogical Agents , PhD Thesis: Human Computer Interaction Institute, Carnegie Mellon University.

Rau, M. (2013). Conceptual learning with multiple graphical representations: Intelligent tutoring systems support for sense-making and fluency-building awareness. PhD Thesis, Human Computer Interaction Institute, Carnegie Mellon University.

Ringenberg, M. (2006). Scaffolding Problem Solving With Embedded Examples to Promote Deep Learning. Submitted in partial fulfillment of the requirments for the degree of Master of Sciences, University of Pittsburgh, 2006.

Roll, I. (2009). Structured Invention Tasks to Prepare Students for Future Learning: Means, Mechanisms and Cognitive Processes. PhD thesis available from the Human Computer Interaction Institute Technical Reports database, CMU-HCII-09-105.

Shih, B. (2011). Target Sequence Clustering. PhD Thesis, CMU, 2012.

Studer, C. (2012). Incorporating Learning Over Time into the Cognitive Assessment Framework. May 25, 2012.

Sweet, T. (2012). Statistical Network Models for Replications and Experimental Interventions. July 17,2012.

Vercellotti, M.L. (2012). Complexity, Accuracy, and Fluency as Properties of Language Performance: The Development of the Multiple Subsystems over Time and in Relation to Each Other. Ph.D. Dissertation. University of Pittsburgh, Pittsburgh, PA.

Walker, E. (2010). Automated Adaptive Support for Peer Tutoring. PhD Thesis: Human Computer Interaction Institute, Carnegie Mellon University.

Walkington, C. (2010). “Playing the game” of story problems: Situated cognition in algebra problem solving (Doctoral dissertation). University of Texas, Austin, TX.

Ward, A. (2010). Reflection and Learning Robustness in a Natural Language Conceptual Physics Tutoring System. PhD Thesis: Intelligent Systems, University of Pittsburgh

Wylie, R. (2011). Examining the Generality of Prompted Self-Explanation. PhD Thesis, August 8, 2011. Human Computer Interaction Institute, Carnegie Mellon University.

Zhao, Y. (2012). Explicitness, Cue Competition, And Knowledge Tracing: A Tutorial System For Second Language Learning Of English Article Usage, Thesis Defense, CMU, May 4, 2012.

== Thesis Proposals ==

Aleahmad, T. (2011). Integrating Effective Learning Principles into Student Study Practices. Tuesday, July 12, 2011. Human Computer Interaction Institute, Carnegie Mellon University.

Ayers, E. (2007). Predicting Performance and Creating Better Student Proficiency Models by Improving Skill Codings. CMU-PIER Thesis Proposal.

Balass, M. (2010). Thesis Proposal. Department of Psychology, University of Pittsburgh.

Cen, H. (2007). Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning. Thesis Proposal, CMU.

Gweon, G. (2010). Assessment and Support of the Knowledge Construction Process in Group Work. Thesis Proposal August 16, 2010. Human Computer Interaction Institute, Carnegie Mellon University.

Kumar, R. (2011). Conversational Agents in Multi-Party Interactive Situations. PhD Thesis Proposal.

Li, N. (2012). Integrating Representation Learning and Skill Learning in a Human-Like Intelligent Agent. May 21, 2012

Lomas, D. (2013). Optimizing Motivation and Learning in Educational Games: Crowdsourcing Design Decisions Using Large-Scale Design Experiments. CMU Human Interaction Institute Thesis Proposal.

Matlen, B. (2012). Comparison-based Instruction in Science Education. CMU Department of Psychology Thesis Proposal. July 2012.

Rau, M (2012). How can we promote understanding and fluency in learning from multiple representations? Intelligent Tutoring System support for connection making. Doctoral thesis proposal. Human Computer Interaction Institute, Carnegie Mellon University. August 2012.

Sudol, L. (2011). Deepening Students' Understanding of Algorithms: Effects of Problem Context and Feedback Regarding Algorithmic Abstraction. Tuesday June 28, 2011. Computer Science Department, Carnegie Mellon University.

Walker, E. (2009). Automated Adaptive Support for Peer Tutoring. PhD Thesis Proposal: Human Computer Interaction Institute, Carnegie Mellon University.

Wylie, R. (2010). Investigating the Effects of Self-Explanation on Second Language Grammar Learning. Thesis Proposal April 27, 2010. Human Computer Interaction Institute, Carnegie Mellon University.

== Tutorials ==

Aleven, V., McLaren, B. & Sewell, J. (2006). Tutorial on Rapid Development of Intelligent Tutors using the Cognitive Tutor Authoring Tools (CTAT). Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies, ICALT 2006, Kerkrade, The Netherlands.

Aleven, V., McLaren, B.& Koedinger, K.R. (2005). Tutorial: Rapid development of computer-based tutors with the Cognitive Tutor Authoring Tools (CTAT). In C-K Looi, G.I. McCalla, B. Bredeweg, & J. Breuker, (Eds.). Proceedings of the 12th International Conference on Artificial Intelligence in Education, July 2005. AIED, Vol 125 IOS Press (2005), p. 990.

Aleven, V., McLaren, B., Sewall, J. & Koedinger, K.R. (2006). Tutorial: Building Example-Tracing and Model-Tracing Tutors with the Cognitive Tutor Authoring Tools (CTAT). 8th International Conference on Intelligent Tutoring Systems. 2006.

Baker, R.S.J.d., Yacef, K., Beck, J. & Koedinger, K.R. (2009). Educational Data Mining (EDM). Tutorial conducted at AIED 2009.

Brunskill, E. & Gordon, G. (2013). Machine Learning for Student Learning. Invited tutorial conducted at Neural Information Processing Systems Foundation (NIPS) 2012.

Dyke, G. & Rosé, C. (2011). Leveraging tool support for the analysis of computer-mediated activities. Tutorial conducted at the 9th International Conference on Computer-Supported Collaborative Learning (CSCL 2011), Hong Kong, China.

Nixon, T., Baker, R.S.J.d., Yudelson, M. & Pardos, Z. (2012). Parameter fitting for learner models. Tutorial conducted at ITS 2012.

Rosé, C. P. (2013). Discourse Analytics. Invited Tutorial conducted at Learning Analytics Summer Institute (Co-Organized by the Society for Learning Analytic Research and Stanford University). July 2013, Stanford University.

Stamper, J. (2010). PSLC DataShop. Tutorial conducted at 10th International ITS Conference.

Stamper, J. (2011). Importing to DataShop. Tutorial conducted at AIED 2011.

Stamper, J. (2013). Learning Curve Analysis using DataShop. Third Conference on Learning Analytics and Knowledge (LAK 2013) in Leuven, Belgium April 8-12, 2013

== Workshops ==

Anthony, L. (2007). Exploration of the Effects of Handwriting on Learning in Algebra Equation Solving. ACM Multimedia EMME Workshop, Augsburg, Germany.

Asay, D., Siskin, C.B. & Siskin, M. (2008). Getting started with Revolution. Workshop presented at the Computer Assisted Language Instruction Consortium Conference (CALICO), San Francisco, CA, (March, 2008).

Asay, D. & Siskin, C.B. (2008). Moving ahead with Revolution. Workshop presented at the Computer Assisted Language Instruction Consortium Conference (CALICO), San Francisco, CA, (March, 2008).

Matsuda, N., Keiser, V., Raizada, R., Stylianides, G., Cohen, W. W., & Koedinger, K.R. (2011). Learning by Teaching SimStudent - Interactive Event at Artificial Intelligence in Education, 15th International Conference, AIED 2011, Auckland, New Zealand, 2011.

Rosé, C. (2012). Text Mining Workshop at Howard University.

Stamper, J.C., Koedinger, K.R., Baker, R.S.J.d., Skogsholm, A., Leber, B., Demi, S., Yu, S., & Spencer, D. (2011). DataShop: A Data repository and analysis service for the learning science community - Interactive Event at Artificial Intelligence in Education, 15th International Conference, AIED 2011, Auckland, New Zealand, 2011.

Turner, T., Macasek, M., Nuzzo-Jones, G., Heffernan, N. & Koedinger, K.R. (2005). The Assistment Builder: A Rapid Development Tool for ITS. 12th Annual Conference on Artificial Intelligence in Education 2005. Workshop on Adaptative Systems for Web Based Education: Tools and Reusability. 2005.

== Invited Talks ==

Aleven, V. (2009). CTAT: Efficiently building real-world intelligent tutoring systems through programming by demonstration. 22nd International FLAIRS Conference, May 29-21, 2009. Invited talk.

Aleven, V. (2011). Keynote talk at the First Workshop on Technology-Enhanced Learning for Mathematics and Science at EC-TEL'2011 (September 20-23, Palermo, Italy)

Aleven, V. (2011). Toward a Framework for the Analysis and Design of Educational Games. Carnegie Mellon University, Pittsburgh, PA. March, 2011.

Aleven, V. (2013). Games for Collaborative Science Inquiry for Grades K-3. Presented at Technology in Support of Learning segment of New Directions in Research on Learning and Education: A Symposium Celebrating 50 Years of LRDC, May 16, 2013. University of Pittsburgh.

Aleven, V., Evenson, S. & Butcher, K. (2006). Improved Interaction Design in a Cognitive Tutor for Geometry. Carnegie Mellon University: Human-Computer Interaction Institute 12th Anniversary Celebration. April 20, 2006.

Anthony, L. (2007). User Science and Experiences Research group seminar. IBM Almaden Research Center, San Jose, CA. Invited talk.

Ashley, K (2008). Some Thoughts on Using Computers to Teach Argumentation. Intelligent Tutoring Systems Invited Talk. 21st International FLAIRS Conference, May 15-17, 2008, Coconut Grove, Florida.

Asterhan, C. S. C. (2010). Between experimental designs, protocol data and individual gains: The case of argumentation to learn. Paper presented at the second Jerusalem Workshop on Interactive Learning “Multiple Perspectives in the Study of Learning in Interaction“, Hebrew University, Israel

Asterhan, C.S.C. (2010). Structured classroom dialogue and its role in student thinking and learning. Keynote presentation at the Segundo Congresso Nacional y Latino de Professoras y Professores de Ciencias de Education Basica, Chilean Ministry of Education, Santiago, Chile.

Baker, R.S.J.d. (2008). Towards Understanding Why Students Game the System. Department of Educational and Counseling Psychology, McGill University. June 18, 2008. (invited seminar)

Baker, R.S.J.d. (2008). Using Data Mining to Better Understand Learning and Learners: Key Challenges and Directions. Department of Computer Science, University of Sherbooke. June 17, 2008. (invited seminar)

Baker, R.S.J.d. (2008). Detecting and Responding to Gaming the System in Cognitive Tutors. Carnegie Learning, Inc., Pittsburgh, PA. April 3, 2008. (invited seminar)

Baker, R.S.J.d. (2009). Towards Understanding Why Students "Game the System" Within Educational Technology. University of Memphis. Mar 12, 2009. (invited seminar).

Baker, R.S.J.d. (2009). Educational Data Mining: A Revolution in Methods for Understanding Learners and Learning. Invited seminar. Science Colloquium Series, Colgate University. November 20, 2009.

Baker, R.S.J.d. (2009). Interface design, affect, and students’ choice to “game the system”. Invited seminar. BostonCHI: The New England area chapter of ACM SIGCHI. September 8, 2009.

Baker, R.S.J.d. (2009). Towards Understanding Why Students “Game the System” Within Educational Software. Invited seminar. Institute for Intelligent Systems, University of Memphis. March 12, 2009

Baker, R.S.J.d. (2010). Intelligent Tutoring Goes to School in the Big City… and the Suburbs… and the Countryside… and the Developing World Mega-City. Invited seminar. Computer Science Department, University of Massachusetts, Amherst. February 25, 2010

Baker, R.S.J.d. (2010). Educational Data Mining: A Revolution in Methods for Understanding Learners and Learning. University of Veracruz, Mexico (by videoconference). May 28, 2010.

Baker, R.S.J.d. (2010). Intelligent Tutoring Goes to School in the Big City… and the Suburbs… and the Countryside… and right here in Metro Manila! College of Computer Studies, De La Salle University – Manila, Philippines. April 8, 2010.

Baker, R.S.J.d. (2011). Towards Automatically Detecting the Robustness of Student Learning. Invited Talk, Intelligent Tutoring Systems Track. 24th Florida Artificial Intelligence Research Society Conference. Palm Beach, Florida. May 19, 2011.

Baker, R.S.J.d. (2012). Towards Complete and Concrete Models of Engagement in Learner-Computer Interaction

Baker, R.S.J.d. (2012). Modeling the Learning in 4-D. Keynote Address. 20th International Conference on User Modeling, Adaptation, and Personalization. July 18, 2012.

Baker, R.S.J.d. (2013). Affect, Collaboration, and Off-Task Behavior in the Chemistry Virtual Lab. Minerva University. April 30, 2013.

Baker, R.S.J.d. (2013). Educational Data Mining: Towards Long-Term and Actionable Prediction of Student Outcomes.College of Education and Human Development. University of Wisconsin. April 19, 2013.

Baker, R.S.J.d. (2013). Educational Data Mining: A Revolution in Methods for Understanding Learners and Learning. Electrical Engineering and Computer Science Department. University of Toledo. December 3, 2012.

Baker, R.S.J.d. (2013). Educational Data Mining: Predict the Future, Change the Future. Data Mining Possibilities Seminar Series. City University of New York Graduate Center. February 15, 2013.

Baker, R.S.J.d. (2013). Educational Data Mining: Predict the Future, Change the Future. Julius and Rosa Sachs Distinguished Lecture. Teachers College, Columbia University. November 5, 2012.

Baker, R.S.J.d. (2013). Modeling Student Learning, Moment by Moment. Reasoning Mind. February 7, 2013.

Baker, R.S.J.d. (2013). Modeling Student Learning, Moment-by-Moment. Center for Research and Evaluation of Advanced Technologies in Education. New York University. March 1, 2013.

Baker, R.S.J.d. (2013). Modeling Student Learning, Moment-by-Moment. Department of Computer Science, University of Colorado, Boulder. October 25, 2012.

Baker, R.S.J.d. (2013). Studying Student Disengagement with Educational Data Mining. Institute of Cognitive Science, University of Colorado, Boulder. October 26, 2012.

Baker, R.S.J.d. (2013). Towards Automatically Detecting the Robustness of Student Learning. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. August 31, 2012.

Baker, R.S.J.d. (2013). Using Educational Data Mining to Detect Disengagement. Reasoning Mind. October 23, 2012.

Balass, M., Bolger, D.J. & Perfetti, C. (2006). The Role of Definition and Sentence Context in Vocabulary Learning. Thirteenth Annual Meeting Society for the Scientific Study of Reading. July 5-8, 2006. Vancouver, Canada.

De Jong, N. (2007). Approaches to the study of second language acquisition. Guest lecture at the CUNY Graduate Center (invited by Prof. Den Dikken and Prof. Otheguy), December 2007

De Jong, N. (2007). Oral fluency development in ESL classrooms. Guest lecture at the CUNY Graduate Center (invited by Prof. Klein), November 2007

De Jong, N. (2008). Oral fluency development in a second language. Presentation given at the Cognitive Approaches to Second Language Acquisition research group at the University of Amsterdam, January 2008.

De Jong, N. (2008). The study of oral fluency development in ESL. Presentation given at the Colloquium on Teaching and Learning World Languages, March 2008, at Queens College of CUNY.

De Jong, N. (2009). Pre-training formulaic sequences and its effect on oral fluency. Talk given at the SLA lab meeting, CUNY Graduate Center, April 24, 2009.

De Jong, N. (2012). Oefenen met vloeiend spreken: wat, hoe en waarom? Paper presented at the BVNT2 Conferentie, June 2012, Hoeven. [invited speaker]

De Jong, N. (2009). Pre-training formulaic sequences and its effect on oral fluency. Talk given at the SLA lab meeting, CUNY Graduate Center, April 24, 2009.

De Jong, N. & Seman, J-M. (2012). Effects of immediate task repetition, prompt type, and time pressure on repeated retrieval of vocabulary. Presentation at the Second Language Research Forum, October 21, 2012, Pittsburgh, PA.

Dunlap, S., Friedline, B., Juffs, A. & Perfetti, C. (2010). Using CALL to direct processing focus on spelling and morphology. Invited colloquium at the American Association for Applied Linguistics, Atlanta, Georgia. (March 2010).

Han, N. (2011). Building ESL (English as a Second Language) Error Correction Models. Language Technologies Insititute at Carnegie Mellon University, Feb 2011.

Juffs, A. (2007). Vocabulary acquisition in English as a second language: Refining theory and practice in an Intensive English Program. Keynote address given at Second Language Acquisition and Teaching (SLAT) Roundtable, University of Arizona, March 2007.

Juffs, A. (2008). Opportunities and Challenges in Teaching Vocabulary Using CALL in an Intensive English Program. February 22, 2008. Ontario Institute for Studies in Education, University of Toronto, Canada. Invited talk.

Juffs, A. & Shirai, Y. (2012). Functional and Formal Approaches to SLA. Second Language Research Forum, October 2012. http://ml.hss.cmu.edu/slrf2012/schedule.html. Invited plenary speaker.

Juffs,A. (2012). Learning Second Language Derivational Morphology. Taiwan National Science Foundation. June 22, 2012

Juffs,A. (2012). Problems and Interventions in Second Language Morphological Processing. Second Language Studies Symposium, Michigan State University. February 24, 2012. Invited Keynote

Juffs,A. (2012). Working memory and sentence processing. Language Learning International Round Table. Invited Speaker. http://lc.ust.hk/~center/conf2012/. June 11, 2012

Klahr, D. (2007). Cognitive Science & Early Science Education. Invited Presentation at Seminar Series on Developmental Science and Early Schooling. Frank Porter Graham Child Development Institute. University of North Carolina, Chapel Hill, NC March 2007.

Klahr, D. (2007). Cognitive Science & Science Instruction: Pasteur's Quadrant in the Learning Sciences. Invited Master Lecture: SRCD 2007 Biennial Meeting. Boston, MA March 2007

Klahr, D. (2011). First of Two invited talks at a conference on science education, sponsored by the Francisco Manuel dos Santos Foundation at two universities in Lisbon and Oporto Portugal.

Klahr, D. (2011). Invited opening speaker, Annual Meeting of the Society for Research in Educational Effectiveness (SREE), Washington, DC., Sept 2011.

Klahr, D. (2011). Second of Two invited talks at a conference on science education, sponsored by the Francisco Manuel dos Santos Foundation at two universities in Lisbon and Oporto Portugal.

Klahr, D. (2012). What Do We Mean?: On the Importance of Not Abandoning Scientific Rigor When Talking about Science Education. The Science of Science Communication, National Academy of Sciences. May 21-22, 2012

Klahr, D. (2010). Biennial Conference of the International Society for the Psychology of Science and Technology, Keynote address. UC Berkeley.

Klahr, D. (2010). APA Science Leadership Conference, Washington, DC. Invited participant.

Klahr, D. (2010). Spencer Foundation Conference on “What Children Learn in School”, Chicago, IL. Invited participant.

Klahr, D. (2010). Purdue University Conference on the Psychology of Science: Implicit and Explicit Reasoning, Invited Speaker.

Koedinger, K. (2013). Using machine learning to create student models and improve educational decisions. Invited speaker for Machine Learning and the Social Sciences Seminar, Department of Machine Learning, Carnegie Mellon University.

Koedinger, K.R. (2006). Korean Academy of Science and Technology. Conference on Learning. Plenary speaker. Seoul, Korea, November, 2006.

Koedinger, K.R. (2006). Twenty-First National Conference on Artificial Intelligence. “Cognitive Tutors and Opportunities for Convergence of Human and Machine Learning Theory”. Plenary speaker. Boston, Massachusetts, July, 2006

Koedinger, K.R. (2007). Studying Robust Learning through Rigorous Experiments in Real Classrooms. Askwith Education Forum at the Harvard Graduate School of Education. Harvard University.

Koedinger, K.R. (2009). International Psychometric Society Meeting. Keynote Address

Koedinger, K.R. (2009). Presented the IES Practice Guide “Organizing Instruction and Study” at Regional Educational Laboratory Mid-Atlantic forum at Penn State University (April 24, 2009)

Koedinger, K.R. (2010). Why designing effective learning interactions is not easy and how we can do better: Part 1. Human Computer Interaction Institute Seminar, Carnegie Mellon University, Pittsburgh, PA, Feb 24, 2010.

Koedinger, K.R. (2011). Design-Deploy-Data-Discover: A Technology-Based Continuous Feedback Loop to Improve Learning Science and Education. Carnegie Mellon University, Pittsburgh, PA. March 2011.

Koedinger, K.R. (2012). Open Discussions on Formal Learning and Why Results From the Learning Sciences Have Little Impact in Schools, Keynote Address at American Psychological Association (APA) Conference, August 2, 2012

Koedinger, K.R. (2012). The KLI Dependency: How the domain-specific and domain-general interact in STEM learning. Presented at the Integrating Cognitive Science with Innovative Teaching in STEM Disciplines Meeting, September 27-28, 2012 at Washington University, St. Louis. Invited talk.

Litman, D.J. (2008). Detecting and Adapting to Student Uncertainty in a Spoken Tutorial Dialogue System. Invited Talk at Affective Language in Human and Machine Symposium, AISB Convention, Aberdeen, Scotland, (April, 2008).

Lovett, M. (2010). Accelerated Learning through Adaptive, Data-Driven Instructional Design. Plenary alk given at the Annual Meeting of the Cognitive Science Society.

MacWhinney, B. (2012). From Models to Methods: Linking L1 and L2 Acquisitional Theory. Talk given at the 31st Second Language Research Forum Conference (SLRF). Pittsburgh, PA. Plenary speaker.

Matsuda, N. (2007). Beyond Building Cognitive Tutors by Demonstration – When SimStudent helps building a bridge between technology and education. School of Education, Stanford University. June 2007, Palo Alto, CA

Matsuda, N. (2009). SimStudent for STEM Education: A synthetic student to explore theories of learning and build effective interventions (2009). School of Education, Public Policy and Civic Engagement, University of Massachusetts Dartmouth. Fairhaven, MA.

Matsuda, N. (2013). Simulated learners—Amplifying research beyond the simulation. AIED Workshop on Simulated Learners in conjunction with AIED 2013, July 9, 2013, Memphis, Tennessee. Invited talk.

Matsuda, N. (2008). SimStudent: Teaching a smart machine to learn how people learn. Human Computer Interaction Graduate Program, Iowa State University. April 2008, Ames, IA.

McLaren, B. (2006). Kaleidoscope Symposium, Oberhausen, Germany, July 2006. Title of talk: "The Pittsburgh Science of Learning Center: Learning Studies and Technology in Actual Classroom Settings."

Mostow, J. (2012). What Can We Learn from a Reading Tutor that Listens? TDLC Optimal Teaching Workshop at UCSD, May 4, 2012

Nokes, T. J. (2009). Robust Learning. Keynote speaker in the Learn-a-Palooza symposium at the 2009 Annual Meeting of the Advancing Hispanics/Chicanos & Native Americans in Science: Dallas, TX

Nokes, T.J. (2008). Taking cognitive science to school: How cognitive science can improve conceptual learning in physics classrooms. Learning Sciences and Policy Brown Bag Series, University of Pittsburgh: Pittsburgh, PA, December 2008. Invited talk.

Nokes, T.J. (2009). Taking cognitive science to school: Improving cognitive science and student learning. Invited speaker at the Research for Practice Conference. Learning Research and Development Center (LRDC), University of Pittsburgh, Pittsburgh, PA.

Nokes, T.J. (2009). Taking cognitive science to school: How cognitive science can improve student learning in physics classrooms. Paper presented to the annual meeting of the Eastern Psychological Association, March 2009, Pittsburgh, PA.

Nokes, T.J. (2009). Using cognitive science to improve student learning. Invited speaker at the Brain, Mind, and Learning: Research at the Science of Learning Centers at the 2009 Annual Meeting of the Advancing Hispanics/Chicanos & Native Americans in Science: Dallas, TX.

Nokes, T.J. (2009). Robust Learning. Keynote speaker in the Learn-a-Palooza symposium at the 2009 Annual Meeting of the Advancing Hispanics/Chicanos & Native Americans in Science: Dallas, TX.

Pavlik, P. (2009). Optimizing the Schedule of Practice. Invited talk at the University of Phoenix, National Research Center for Teaching and Learning

Perfetti, C. (2005). Brain Behavior Relations in Reading: Universal Constraints and Writing System Variations. Tagung experimentell arbeitender Psychologen (Congress of Experimental Psychology). 2005. Regensburg, Germany.

Perfetti, C. (2005). Reading word-by-word: Text integration processes exposed by Event Related Potentials. European Summer School on Reading. 2005. Edmond an Zee, Netherlands.

Perfetti, C. (2005). The accommodation of the brain’s reading network to writing system variation. Conference on Brain, Language, and Cognition. University of Minnesota, Center for Cognitive Sciences. October, 2005 .

Perfetti, C. (2005). Plenary address: How the mind meets the brain in literacy: New perspectives from reading science. National Reading Conference. 2005.. Miami, FL.

Perfetti, C. (2009). Reducing the complexities of reading comprehension: A Simplying framework. Presented at the Institute of ducation Sciences Research Conference, June 7-9, 2009, Washington DC.

Perfetti, C. (2010). Chinese reading and new universal science of reading. Invited keynote presentation at the Research in Reading Chinese Conference, Toronto, July 2010.

Perfetti, C. (2011). Issues in second language learning: How cognitive neuroscience contributes. Keynote address at Cognitive Neuroscience of Second Language Acquisition: Present Challenges and Future Potential Workshop, University of Maryland, College Park. November 9. 2011

Perfetti, C. (2011). Reading Ability and Reading Disability: The Emergence of Connections. Keynote lecture. Amsterdam Dyslexia Program, Amsterdam, Dec. 8, 2011.

Perfetti, C. (2011). Reading universals are modulated by language and writing system. Invited keynote lecture, preconference symposium of Society for Language development, Boston, November 3, 2011.

Perfetti, C. (2012). What does literacy have to do with language? Invited presentation to Workshop on Language Development in childhood and adolescence. Leiden, January 13, 2012.

Resnick, L. (2012). The SERP Partnership Model: Problem-Solving Researcher, Design, Development, and Implementation, Invited Panel Member. SREE 2012.

Ritter, S. (2010). Riding the Third Wave. Intelligent Tutoring Systems (ITS 2010). Invited talk.

Ritter, S. & Nixon, T. (2010). Cognitive Tutor: Modeling to improve mathematics education. Invited talk at KDD Cup 2010 Workshop held as part of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010).

Roll, I. (2007). Modeling and scaffolding general learning skills with intelligent tutoring systems. Department of Management Information Systems. Haifa University, December 2007. Invited talk.

Roll, I. (2007). Can Help-Seeking Be Taught Using Tutoring Systems? Searching For the Secret Sauce of Meta-cognitive Tutoring. Department of Education, Haifa University, December 2007. Invited talk.

Roll, I. (2007). Debugging the Learning Process: Can Tutoring Systems Teach General Learning Skills? Department of Computer Science, Worcester Polytechnic Institute. July 2007. Invited talk.

Rosé, C. (2011). Detecting Social Dynamics in Speech, IBM Delhi, Spoken Web group, December 14, 2011.

Rosé, C. (2011). Detecting Social Dynamics in Speech, Indo-US Workshop on Analytics, IISc, Bangalore, Dec 2011.

Rosé, C. (2011). Invited Discussant, Session on Dialogue in the Digital Age, Socializing Intelligence Through Academic Talk and Dialogue Conference, sponsored by the American Education Research Association, September 2011

Rosé, C. (2011). Invited panelist, Towards Monitoring Classroom Interactions Through Speech Processing, as part of the panel on Research on discursive teaching and learning: What have we learned and where are we heading, at the European Association for Research on Learning and Instruction 2011 Conference.

Rosé, C. (2011). Dialogue Systems that Support Group Work and Learning, at Young Researchers Round Table for Spoken Dialogue Systems 2011 (Academia Session). Invited Speaker and Panelist.

Rosé, C. (2011). Analysis of Social Positioning in Interaction, IBM Delhi, Spoken Web group, December 14, 2011. Invited Talk.

Rosé, C. (2011). Supporting Academically Productive Talk with Computer Agents,Invited Seminar Talk, Drexel Information School, Drexel University, February 2011

Rosé, C. (2011). Workshop Invited Talk, Analysis of Social Positioning in Interaction, Indo-US Workshop on Large Scale Data Analytics and Intelligent Services, IISc, Bangalore, Dec 18-20, 2011

Rosé, C. (2012). Institut Francais de l'Education 3rd International Learning Sciences seminar, Methodology Track, June 2012

Rosé, C. (2012). Invited talk, MIT Media Lab, part of a project planning summit jointly organized by the Media Lab and Linked In for developing a crisis response platform, September 2012

Rosé, C. (2012). Symposium Invited Talk, Robot Facilitation as Dynamic Support for Collaborative Learning, Symposium at the International Conference of the Learning Sciences, July 2012.

Rosé, C. (2012). Workshop Invited Talk, LightSIDE: Open Source Machine Learning for Text Accessible to Non-Experts, National Council on Measurements in Education Conference, Spring 2012, talk delivered by Elijah Mayfield

Rosé, C. (2012). Workshop Keynote, Institut Français de l'Education 3rd International Learning Sciences seminar, Methodology Track, Lyon, France, June 2012

Rosé, C. (2013). Invited Panel Talk, Invited Panel on CSCL Research Methodology, Computer Supported Collaborative Learning 2013.

Rosé, C. P. (2012). Supporting Discursive Instruction Online and In the Classroom with Intelligent Conversational Agents. Invited talk given at Worcester Polytechnic Institute (WPI), October 22, 2012

Rosé, C. P. (2013). Automated Approaches to Analyzing Data from Collaborative Learning Settings. Symposium on Trends in Support and Analysis of Collaborative Learning. Jointly organized by the Special Interest Groups on Instructional Design and Learning and Instruction with Computers, at the Biennial Meeting of the European Association for Research on Learning and Instruction, August 2013. Invited symposium talk.

Rosé, C. P. (2013). Discourse Analytics: Assessment of Collaborative Learning Discussions. 2013 Academy of the German Institute for International Education Research, Salzschlirf, Germany. June 2013.

Rosé, C. P. (2013). From Research Instruments to Classroom Assessments: A Call for Tools to Assist Teacher Assessment of Collaborative Learning,. Computer Supported Collaborative Learning conference, June 2013. Invited panel talk.

Rosé, C. P. (2013). How will Collaborative Problem Solving be assessed at international scale?, Workshop at the Computer Supported Collaborative Learning conference, June 2013. Invited panel talk, invited workshop.

Rosé, C. P. (2013). Measuring Engagement in Social Processes that Support Shared Cognition. Workshop on Developing Multi-Disciplinary Measurement Approaches for Shared Cognition, University of Central Florida. February 2013. Invited workshop talk.

Rosé, C. P. (2013). Panel on Translating collaborative project-based learning to online and blended environments at the Workshop on Multidisciplinary Research for Online Education (MunROE, http://www.cra.org/ccc/mroe). Sponsored by the Computing Community Consortium, Feb 11-12, 2013, Washington, DC. Invited talk.

Rosé, C. P. (2013). Zooming In and Out of Collaborative Process Analysis through Linguistically Informed Machine Learning Models. Invited talk as part of Plenary Panel: To see the world and a grain of sand: Multiple methods in CSCL research, Computer Supported Collaborative Learning conference, June 2013.

Rosé, C.P. & Clarke, S. (2013). Understanding Student Engagement in Classroom Dialogue. Symposium on Enablers and Barriers of Productive Learning Dialogues: Where social meets cognitive. Biennial Meeting of the European Association for Research on Learning and Instruction, August 2013. Invited symposium talk (presented by Sherice Clarke).

Salden, R. (2008). Life, the Universe, and Worked Examples in Cognitive Tutors. AI Seminar of the Intelligent Systems Program (ISP) at the University of Pittsburgh, USA, October 24, 2008.

Stamper, J. (2011). KDD Cup Competition Lessons Learned. Invited talk at EDM, July 2011.

Stamper, J. (2011). PSLC Datashop. Invited talk at 2nd STELLAR Alpine Rendez-Vous, March 2011.

Thille, C. (2010). Community College Online Teaching Conference, Keynote speaker, San Diego City College (San Diego CA)

Thille, C. (2010). Reinventing the American University. Invited speaker at the American Enterprise Institute for Public Policy Research (AEI). (Washington DC)

Thille, C. (2010). February 5: Reforming Electrical Energy Systems Curriculum with OER, Engineering Education Key Note Speaker (University of Minnesota).

Thille, C. (2010). January 11: Continuous Improvement in Teaching and Learning: Open Learning Initiative (OLI) and Open Learning Net (Olnet), Educause Learning Initiative (ELI) Webinar (internet)

Thille, C. (2010). January 13: Evidence Based Course Design - The Open Learning Initiative at Carnegie Mellon, American Mathematical Society (San Francisco, CA)

Thille, C. (2010). Thille, C. (2010, June). The National Conference on Student Assessment by the Council of Chief State School Officers, Plenary Session (Detroit MI).

Tokowicz, N. (2012). Translation Ambiguity in Language Learning, Processing, and Representation. Talk given at the 31st Second Language Research Forum Conference (SLRF). Pittsburgh, PA. Plenary speaker.

VanLehn, K. (2005). “The Andes Intelligent Tutoring System,” IADIS Virtual Multi Conference on Computer Science and Information Systems (MCCSIS 2005): eLearning. April 20, 2005.

VanLehn, K. (2006). “When is tutorial dialogue more effective than cheaper instruction?” Serious Games Workshop, Institute for Creative Technology, Marina del Rey, CA, August 2006.

VanLehn, K. (2006). “Representation and reasoning for deeper natural language understanding in a physics tutoring system.” FLAIRS, Melbourne Beach, FL, May, 2006.

VanLehn, K. (2006). “A natural language tutorial dialogue system for physics” FLAIRS, Melbourne Beach, FL, May 2006

VanLehn, K. (2006). “The Pittsburgh Science of Learning Center: Studying robust learning in LearnLab classrooms” International Conference on Cognition and Neural Science, Boston, MA, May 2006.

VanLehn, K. (2007). "Expertise in elementary physics, and how to acquire it.” The Development of Professional Performance: Approaches to Objective Measurement and Designed Learning Environments, Orlando, FL, March 2007

VanLehn, K. (2007). “What’s in a step? Toward general, abstract representations of tutoring system log data.” User Modelling Conference, Corfu, Greece, June 28, 2007.

VanLehn, K. (2007). “Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances” AI in Education Conference, Marian Del Rey, CA, July 13, 2007.

VanLehn, K. (2007). “Step-level assistance while solving complex physics problems can significantly improve semester-long learning” CRESMET, Arizona State University, Tempe, AZ, August 13, 2007

VanLehn, K. (2007). “Can natural language tutoring systems be as effective as human tutors?” School of Computing and Informatics, Arizona State University, Tempe, AZ, August 14, 2007.

VanLehn, K. (2007). “Cognitive Analysis of Student Learning Using LearnLab” Physics Education Research Conference, Greensboro, NC, August 2, 2007.

VanLehn, K. (2007). “Is the “self” of self-explanation important? In vivo experiments.” European Association of Research on Learning and Instruction (EARLI) conference, Budapest, Hungary, August 30, 2007.

VanLehn, K. (2007). “Can natural language tutoring systems be as effective as human tutors?” Stanford Research Institute, Menlo Park, CA, September 20, 2007.

VanLehn, K. (2007). “Why will you see so many null results for learning gains in these talks?” Speech and Language Technology in Education, Farmington, PA, October 2, 2007.

VanLehn, K. (2008). When Is Tutorial Dialogue More Effective Than Less Interactive Instruction? American Educational Research Association, New York, NY, March 28, 2008.

VanLehn, K. (2008). Intelligent Tutoring Systems: What Do We Do Next? Fordham University, New York, NY, March 27, 2008.

VanLehn, K. (2008). Designing for conceptual understanding: College physics. Open Learning Interplay 2008, Pittsburgh, PA, March 10, 2008.

VanLehn, K. (2008). The interaction plateau: Answer-based tutoring < Step-based tutoring = Natural tutoring. Keynote talk, Intelligent Tutoring Systems, July, 2008.

VanLehn, K. (2009). “Step-based tutoring systems emulate human tutors” TII-Vanguard Conference on Learning, Washington, DC, May 9-11 2009.

VanLehn, K. (2009). “Transfer of Meta-Strategies” AAAI Fall Symposium, Washington, DC, Novermber 10, 2009.

VanLehn, K. (2009). “Why are intelligent tutoring systems just as effective as expert human tutors?” CERI-PALM seminar series, ASU PolyTechnic, Mesa, AZ September 23, 2009.

VanLehn, K. (2009). Toward a practical learning theory for step-based tutoring systems” ARI Workshop on Adaptive Training Technologies, Charleston, SC, March 3-5, 2009.

VanLehn, K. (2010). “Why are step-based tutoring systems almost as effective as human tutors?” International Conference on Cognitive Modeling, Philadelphia, PA, August 6, 2010.

VanLehn, K. (2011). “Now that ITS are as effective as human tutors, how can they become even better?” International Conference on Computers in Education, Chiang Mai, Thailand, Nov. 30, 2011

VanLehn, K. (2011). “What granularity is best for tutoring? Implications for learning, assessment and classrooms” Educational Testing Service, Princeton, NJ, March 25, 2011

VanLehn, K. (2011). The relative effectiveness of human tutoring and 3 types of computer tutoring. Pearson Educational Products, Boston, MA, February 17, 2011

VanLehn, K. (2012). “Now that Intelligent Tutoring Systems are as effective as human tutors, how can they become even better?” Cognitive Science Institute, University of Colorado at Boulder, Feb. 17, 2012

VanLehn, K. (2012). “Now that Intelligent Tutoring Systems are as effective as human tutors, how can they become even better?” Optimal Teaching Workshop, University of California at San Diego, May 4, 2012

VanLehn, K. (2012). “Now that ITS are as effective as human tutors, how can they become even better?” Conversations on Quality: A Symposium on K-12 Online Learning, MIT, Jan. 24, 2012

Walkington, C. (2011). Measures of Effective Teaching: Capturing the UTeach Vision in Classroom Observation. Invited talk at University of Kansas (October 2011).

Wang, Z. (2013). Assessing speaking: The relevance of tasks and performance. Invited talk in Educational Testing Service (ETS), Princeton, NJ.

Yaron, D. (2011). Online materials that promote conceptual learning in introductory Chemistry. Invited talk at The 2011 Tripartite Symposium: Effective practices and current challenges in STEM education in western Pennsylvania. University of Pittsburgh, May 4, 2011.

== Talks-Other ==

Allen, H. & Jones, C. (2006). French Online and the Open Learning Initiative. Digital Stream Conference: Emerging Technologies in Teaching Languages and Culture, Monterey, California. March 2006.

Baker, R.S.J.d. (2011). Towards Automatically Detecting the Robustness of Student Learning. AAALab/LIFE Center, School of Education, Stanford University, September 30, 2011.

Baker, R.S.J.d. (2011). Using Educational Data Mining to Detect Disengagement and the Moment of Student Learning. Teachers College, Columbia University. March 23, 2011.

Baker, R.S.J.d. (2011). Using Educational Data Mining to Detect the Moment of Student Learning. 11th Philippine Computing Science Congress (PCSC2011). Naga, Bicol, Philippines. March 4, 2011.

Baker, R.S.J.d. (2011). Using Educational Data Mining to Detect the Moment of Student Learning. Department of Computer Science, University of the Philippines Diliman. February 28, 2011.

Baker, R.S.J.d. (2012). Educational Data Mining Methods for Modeling and Studying Gaming the System in Educational Software. ETS. April 24, 2012.

Baker, R.S.J.d. (2012). Studying Student Disengagement and the Robustness of Learning with Educational Data Mining. BBN Technologies, Cambridge, Massachusetts. February 3, 2012.

Belenky, D. M., Gadgil, S., Nokes, T. J., & Levine, J. (2010). Dialectical interaction, arousal, and learning. Third Annual Inter-Science of Learning Center Student and Post-Doc Conference. Boston, MA.

Butcher, K. (2008). Visual interaction and robust learning. Talk presented at the International Workshop on Spatial Cognition and Learning, University of Freiburg, Freiburg, Germany, September, 2008.

Butcher, K. & Aleven, V. (2008). Visual interaction in intelligent tutoring: Support for robust learning. Research presentation for visiting educators and officials from Singapore’s Ministry of Education, Carnegie Mellon University, Pittsburgh, PA

Chan, D. (2007). Learning a tonal language by attending to the tone: an in-vivo experiment. Talk given at the Pittsburgh Science of Learning Center Chinese Learnlab Symposium, Carnegie Mellon University, Oct 19, 2007.

De Jong, N. (2012). Short and longer term effects of time pressure on fluency in second language learners. Presentation at the Workshop Fluent Speech, November 13, 2012, Utrecht.

De Jong, N. (2006). Developing oral fluency with the 4/3/2 task. Presentation given at the Multimedia Showcase, University of Pittsburgh, September 2006

De Jong, N. & Halderman, L.K. (2010). Vocabulary and grammatical knowledge contribute differentially to second language oral fluency. Presented at the Inter-Science of Learning Center Conference, 3rd Annual Meeting, Boston, MA.

Dunlap, S. (2006). Learning L2 vocabulary from semantic cues: A PSLC LearnLab study of implicit versus explicit training. Presentation at the Pitt-CMU Conference, Pittsburgh, September 2006.

Dunlap, S. (2009). Lexical quality of English second language learners: Effects of focused training on orthographic encoding skill. Brown Bag Presentation for the Cognitive Psychology Program, University of Pittsburgh, February, 2009.

Dunlap, S. (2010). Spelling in English as a second language: Do students make different types of errors on different types of tasks? Talk presented at the 3rd annual Inter-Science of Learning Centers Conference, Boston, Massachusetts.

Dunlap, S. (2006). What are some effective ways to support learning of new vocabulary in L2?: Evidence from some LearnLab studies. Brown Bag Presentation for Cognitive Psychology Program, University of Pittsburgh, PA.

Dunlap, S. (2007). Rules and exceptions: Semantic cues for learning new vocabulary in Chinese as a second language. Presentation at PSLC Chinese LearnLab Symposium "Bridging Chinese Pedagogy, Research, and Technology," Carnegie Mellon University, Pittsburgh.

Dunlap, S., Friedline, B., Juffs, A. & Perfetti, C. (2009). Effects of a spelling intervention with learners of English as a second language. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009.

Frishkoff, G. & Schreiber, F. (2005). Research to Practice: A Bridge Worth Crossing. Talk presented at the Annual Meeting of the American Psychological Association (APA) Session: APA/IES Postdoctoral Education Research Training. Washington, D.C., August 15, 2005.

Gadgil, S. & Nokes, T.J. (2009). Analogical scaffolding in collaborative learning. Second Annual Inter-Science of Learning Center Student and Post-Doc Conference, Seattle, WA.

Hausmann, R.G.M. & Chi, M.T.H. (2005). The impact of constructive dialog on collaborative learning and problem solving performance. Presented at the Festschrift for Lauren Resnick entitled “Talk and Dialogue: How Discourse Patterns Support Learning.”

Hausmann, R.G.M. & Nokes, T.J. (2009). Evidence of transfer in a Physics 1 Course: An educational data-mining project. Second Annual Inter-Science of Learning Center Student and Post-Doc Conference. Seattle, WA.

Heilman, M. & Eskenazi, M. (2006). Authentic, Individualized Practice for English as a Second Language Vocabulary. Presented at Interfaces of Intelligent Computer-Assisted Language Learning Workshop at the Ohio State University, Columbus, OH.

Hensler B.S. & Beck, J. (2006). Are all questions created equal? Factors that influence cloze question difficulty. Thirteenth Annual Meeting Society for the Scientific Study of Reading. July 5-8, 2006. Vancouver, Canada.

Jones, C., Allen, H., Tardio T. & Wu, S. (2006). Language Online: The Evolution of Web-Delivered Instruction. Presentation at ACTFL (American Council on the Teaching of Foreign Languages), Nashville, November 2006.

Juffs, A. & Friedline, B. (2009). L1 Influence, morphological (in)sensitivity and L2 lexical development: Evidence form production data. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009.

Koedinger, K.R. (2009). “Is abstraction better than concreteness?” is the wrong question. Presented at the Meeting of the Society for Research in Child Development in Boston, MA.

Koedinger, K.R. (2011). Accounting for Socializing Intelligence with the Knowledge-Learning-Instruction Framework. Presentation made at the invitational AERA research conference: Socializing Intelligence Through Academic Talk and Dialogue. University of Pittsburgh, September 22-25, 2011.

MacWhinney, B. (2012). A Unified Model of First and Second Language Learning, presented at Georgia State, Utrecht, Jerusalem, Leuven, Penn State, AAAL, SLRF, Montréal

Matsuda, N. (2006). Building Cognitive Model for Cognitive Tutors by Demonstration (2006). Seminar series on e-Learning, Kumamoto University, May 2006, Kumamoto, Japan

Matsuda, N. (2006). Using Simulated Student to build Cognitive Tutors and beyond – Cognitive Modeling with Programming by Demonstration (2006). Department of Computer Science Colloquium, Northern Illinois University, August 2006, IN

McCormick, D. E., & Vercellotti, M. L. (2013). Profiles of noticing in L2 English learners: Examining online and post-production noticing moves. Secong Language Research Forum 2013.

Nokes, T. J., Hausmann, R.G.M., VanLehn, K., & Gershman, S. (2009). The design of self-explanation prompts: The fit hypothesis. 2009 Science of Learning Centers PI Meeting: Washington, D. C.

Pavlik, P. (2010). Efficiency, Design, and Efficent Design. Carnegie Mellon University, Pittsburgh, PA.

Pino, J. & Eskenazi, M. (2009). L1 Effects in students' answers to word recall questions and cloze questions. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009.

Presson, N. (2008). Explicit Instruction of Cues to Grammar: Prototypes or Exemplars? Presented at the 1st Annual iSLC Student / Postdoc Conference, Pittsburgh, PA.

Rodrigo, M.M.T., Baker, R.S.J.d., Abalos, N., Bacuyag, K., Basuel, B., Bautista, M., Cortez, M., Dulla, G., Elomina, S., Gineta, M.A., Rara, A., Rodriguez, R., Sanggalang, J., Sugay, J., Tan, A.K., Tan, M., Trajano, E., Uy, F., Victorino, N., Villaflor, K. (2009). A comparison of learners’ affect and behaviors while using an intelligent tutor and an educational game. Presentation at Philippine Computing Society Congress.

Rodrigo, M.T., Baker, R.S.J.d., Sugay, J. & Tabano (2009). Monitoring novice programmer affect and behaviors to identify learning bottlenecks. Presentation at Philippine Computing Society Congress.

Roll, I. (2009). Teaching for learning versus teaching for retention. Presentation at the 2nd Inter-Science of Learning Centers Conference, 2009. Seattle, WA.

Rosé, C. (2011). What Sociolinguistics and Machine Learning Have to Say to One Another, MIT Media Lab Applied Machine Learning Series (delivered remotely), August 2011

Rosé, C. (2012). Automated Analysis of Social Positioning in Conversation, CUNY, April, 2012.

Rosé, C. P. (2012). Colloquium talk, Soufle: A Three Dimensional Framework for Analysis of Social Positioning in Dyadic and Group Discussions. Rhetoric Colloquium, Department of English, Carnegie Mellon University, February, 2013

Rosé, C. P. (2012). Supporting Discursive Instruction Online and in the Classroom with Intelligent Conversational Agents. HCII Seminar, Carnegie Mellon University, November, 2012

Rosé, C.P. (2006). Towards Adaptive Support for On-line Learning, Technology-integrated Science and Engineering Education (TechSEE) Keynote Speech Taipei May 2006.

Sewall, J. & Bett, M. (2008). Cognitive Tutor Authoring Tools and Pittsburgh Science of Learning Center. Software & Information Industry Association Ed Tech Business Forum, December 2008.

Siler, S. A., Klahr, D., Magaro, C., & Willows, K. (2011). Training in Experimental Design (TED): Integrating Lab and Classroom Research into the Design of Computerized Instruction for Elementary and Middle School Students. Talk given at the 2011 National Association of Laboratory & University Affiliated Schools (NALS) Annual Conference. Pittsburgh, PA.

Siskin, C.B. (2005). Presentation of the software component at the “Multimedia Showcase” sponsored by the Robert Henderson Media Center at the University of Pittsburgh.

Smith, J. & Thille, C. (2009). "Learning Unbound: Disrupting the Baumol Effect in Higher Education.” Presented at the Forum for the Future of Higher Education Aspen Symposium. Aspen, CO. June 17, 2009.

Stamper, J. (2012). Datashop presentation as part of the Educational Data Mining meets Learning Analytics Panel held at the International conference on Learning Analytics Knowledge 2012 (LAK12).

Thille, C (2010). “Reforming Electric Energy Systems Curriculum.” Presented at 2010 ONR/NSF Sponsored Faculty Workshop, University of Minnesota. Tuscon, AZ . February 4, 2010.

Thille, C. (2009). “Evidence Based Design – OLI and OLNet.” Presented at Massachusetts Institute of Technology. Cambridge, MA. December 14, 2009

Thille, C. (2009). “The Open Learning Initiative and OLnet.” Presented at the Annual Meeting of The Consortium on Financing Higher Education. Philadelphia, PA. October 7, 2009.

Thille, C. (2009). “The Open Learning Initiative and OLnet.” Presented at The William and Flora Hewlett Foundation Grantees Conference. Monterey, CA March 3, 2009

Thille, C. (2009). "Engaging Students: The Open Learning Initiative.” Presented at the National Center for Academic Transformation Conference. March 22-24, Orlando FL.

Thille, C. (2010). “Continuous Improvement in Teaching and Learning.” Presented at University of Pennsylvania. Philadelphia, PA. January 15, 2010.

Thille, C. (2010). “Open Learning Initiative – Online Math.” Presented at The American Mathematical Society Joint Math Meeting. San Francisco, CA. January 13, 2010.

Thille, C., Meyer, O., Moynihan, M. K., McClure, C., & Snell, M. E. (2010). CC-OLI Statstics: Free, Research-based Online Learning Materials. Talk given at The American Mathematical Association of Two-Year Colleges 36th Annual Conference (Boston, MA).

van de Sande, B. (2010). Physics homework using Andes. Talk given at Carnegie Learning, Pittsburgh, PA, Feb. 4, 2010.

VanLehn, K. (2006). Pittsburgh Science of Learning Center (PSLC). International Conference of the Learning Sciences (ICLS). Bloomington, IN, USA.

VanLehn, K. (2006). The Pittsburgh Science of Learning Center: Studying robust learning in LearnLab classrooms. International Conference on Cognition and Neural Science. Boston, MA.

VanLehn, K. (2009). Step-based tutoring systems emulate human tutors. TII-Vanguard Conference on Learning, Washington, DC, May 9-11 2009.

VanLehn, K. (2009). Transfer of Meta-Strategies. AAAI Fall Symposium. Washington, DC, November 10, 2009.

Vercelloti (2009). Choosing a verb complement: Use and accuracy in English L2. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009.

Vercellotti, M.L. (2011). “Norming Picture Story Prompts for Second Language Production Research: Fluency, Linguistic Items, and Speakers’ Perceptions”, (with Nel de Jong), American Association of Applied Linguistics (AAAL), Chicago, IL. March 26-29, 2011.

Vercellotti, M.L. (2012). “Complexity, Accuracy, and Fluency: The Development of Language Performance”, Paper presented at Second Language Acquisition Research Symposium, English Language Institute, University of Pittsburgh, Pittsburgh, PA. July 7, 2012.

Walkington, C. (2010). UTeach National Conference Presentation (May 2010): Examining UTeach Outcomes: Classroom Observations of UTeach Graduates.

Walkington, C. (2011). Cognitive Science Society Presentation (July, 2011): Exploring the Assistance Dilemma: The Case of Context Personalization.

Walkington, C. (2011). Cognitive/Developmental Psychology Brownbag, University of Wisconsin (December 2011): Matching Instruction to Personal Interests: Impact on Performance and Learning.

Walkington, C. (2011). Interdisciplinary Training Program Seminar, University of Wisconsin (November, 2011): Teaching Effectiveness in Project-Based Settings: Bridges and Barriers to Building Conceptual Cohesion.

Walkington, C. (2011). Supporting Algebraic Reasoning with Context Personalization. Learning Science Luncheon Presentation (March, 2011).

Walkington, C. (2011). Functions Perspectives in Algebra: An Empirically-Grounded Framework for Assessing Student Knowledge. National Council of Teachers of Mathematics Research Pre-session Presentation (April, 2011).

Walkington, C. (2011). Science and Mathematics Teacher Imperative Presentation (June, 2011). UTeach, UTeach Replication, and the UTOP.

Walkington, C. (2011). Tangibility for the Teaching, Learning, and Communicating of Mathematics Advisory Board Meeting Presentation (October, 2011): Cognition from Action.

Walkington, C. (2012). Grounding Justifications in Concrete Embodied Experience: The Link between Action and Cognition. American Educational Research Association Annual Meeting Presentation (April 2012).

Walkington, C. (2012). Using Classroom Observation Research to Inform Debates about Teaching Effectiveness. National Council of Teachers of Mathematics Research Pre-Session Presentation (April, 2012).

Wu, S. (2008). The PSLC Chinese LearnLab Online project. The Opening Learning Interplay Symposium: The Evolution of Open Learning. March 10-12, 2008. Carnegie Mellon University, Pittsburgh, PA.

Wu, S. (2008). Literacy Promotion and Grammar Consolidation in an Intermediate Chinese Curriculum. Presentation at the annual meeting of the Chinese Languages Teachers Association (CLTA)/American Council on the Teaching of Foreign Languages (ACTFL) Conference. Nov 20- 23, 2008. Orlando, Florida.

Wu, S. (2008). Robust Learning of Language and Cultural Literacy in Chinese Online. Presented at the Multimedia Showcase. September 25, 2008, University of Pittsburgh, Pittsburgh, PA.

Wylie, R. (2009). Does Self-Explanation Always Help?: The effects of adding self-explanation prompts to an English as a Second Language grammar tutor. Second Annual Inter-Science of Learning Center Student and Post-Doc Conference. February 5-7, 2009.

Wylie, R. & Koedinger, K.R. (2009). Self-Explanation and Second Language Grammar Learning. IES Research Conference. Washington DC. June 7-9, 2009.

Wylie, R., Koedinger, K.R. & Mitamura, T. (2009). Would someone explain this? Adding self-explanation to an English Article Tutor. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009.

Wylie, R., Mitamura, T. & Rankin, J. (2006). From Practice to Production: Developing Tutoring Systems for English Article Use. Presentation at the Three Rivers Teachers of English to Speakers of Other Languages (3RTESOL) Conference. Pittsburgh, Pennsylvania. October 28, 2006.

Wylie, R., Mitamura, T., Rankin, J. & Koedinger, K.R.. (2006). Two Tutors, One Goal: Two tutoring systems for teaching English articles. University of Pittsburgh’s Multimedia Showcase. Pittsburgh, Pennsylvania. September 27, 2006.

Wylie, R., Mitamura, T., Rankin, J., Koedinger, K.R. & MacWhinney, B. (2006). Developing Intelligent Tutoring Systems for Language Learning. Science of Learning Center Symposium at the Society for Neuroscience conference. Atlanta, Georgia. October 13, 2006.

Yaron, D., Karabinos, M., Davenport, J. & Leinhardt, G. (2006). Virtual labs and scenario-based activities for introductory chemistry. American Chemical Society - Penn-Ohio Regional Meeting, Theil College, Greenville, PA, October 2006.

Yaron, D., Karabinos, M., Davenport, J. & Leinhardt, G. (2006). Virtual lab activities for introductory chemistry labs, American Chemical Society Annual Meeting, San Francisco, September 2006.

Yaron, D., Leinhardt, G., Karabinos, M. et al (2005). “Virtual labs and scenario-based learning for introductory chemistry”, Pacifichem, Hawaii, December 2005.

Yu, Y. (2005). Designing systematic exercises to generate learning: How exercises should be developed for optimal effectiveness, Chinese Language Teachers Association (CLTA/ ACTFL), November 18-20, 2005, Baltimore, Maryland

Zhang, Z. (2005). Awareness of Chinese CALL Learners, The Annual Meeting of Chinese Language Teachers Association (CLTA/ ACTFL), November 18-20, 2005, Baltimore, Maryland.

Zhang, Z. (2006). The Development of Morphological Awareness and Literacy Skills in Young Heritage Chinese Learners. The Annual Meeting of Chinese Language Teachers Association (CLTA/ACTFL).

PSLC People

2014-09-09T19:07:53Z

Mbett: /* Faculty */

== '''The Executive Committee''' ==

=== Directors ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [http://pact.cs.cmu.edu/koedinger.html '''Ken Koedinger'''] || Carnegie Mellon University || Human-Computer Interaction Institute
|-
| '''Charles Perfetti''' || University of Pittsburgh || Psychology, LRDC Director
|}

=== Managing Director ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Michael Bett''' || Carnegie Mellon University || Human-Computer Interaction Institute
|}

=== Members ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aleven, Vincent || Carnegie Mellon University || Human-Computer Interaction
|-
| Fiez, Julie || University of Pittsburgh || Psychology
|-
| Gordon, Geoff || Carnegie Mellon University || Machine Learning
|-
| Klahr, David || Carnegie Mellon University || Psychology
|-
| Lovett, Marsha || Carnegie Mellon University || Psychology
|-
| Nokes, Tim || University of Pittsburgh || LRDC
|-
| Resnick, Lauren || University of Pittsburgh || Learning Research and Development Center
|-
| Rose, Carolyn || Carnegie Mellon University || Human-Computer Interaction Institute/Language Technologies Institute
|}

=== Liasons ===

{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Stamper, John || Junior Faculty
|-
| Saz, Oscar || Post-docs
|-
| Matlen, Bryan || Graduate Students
|-
| Ritter, Steve || Carnegie Learning
|}

== Advisory Board ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aronson, Joshua || New York University || Applied Psychology
|-
| Azevedo, Roger || McGill University || Educational and Counselling Psychology
|-
| Biswas, Gautam || Vanderbilt University || Computer Science and Computer Engineering
|-
| Collins, Allan || Northwestern University || Education and Social Policy
|-
| Feuer, Michael || George Washington University || Graduate School of Education and Human Development
|-
| Goldman, Susan || University of Illinois || Psychology
|-
| Goldstone, Rob || Indiana University || Psychology
|-
| Griffiths, Tom || Berkeley || Psychology
|-
| Isbell, Charles || Georgia Tech || School of Interactive Computing
|-
| Kamwangamalu, Nkonko || Howard University || English
|-
| Lesgold, Alan || University of Pittsburgh || School of Education
|-
| McNamara, Danielle || University of Memphis || Psychology
|-
| Li, Ping || Penn State University || Psychology
|-
| Smith, Marshall (Mike) S.|| ||
|}

== Graduate Students ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Adam Skory || Carnegie Mellon ||
|-
| Benjamin Friedline || University of Pittsburgh || Linguistics
|-
| Colleen Davy || Carnegie Mellon || Psychology
|-
| Garbiel Parent || Carnegie Mellon || Language Technologies Institute
|-
| (Derek) Ho Leung Chan || University of Pittsburgh || Linguistics
|-
| Leida Tolentino || University of Pittsburgh || Psychology
|-
| Nora Presson || Carnegie Mellon || Psychology
|-
| Ruth Wylie || Carnegie Mellon || Human Computer Interaction Institute
|-
| Susan Dunlap || University of Pittsburgh || Psychology
|-
| Yun (Helen) Zhao || Carnegie Mellon || Second Language Acquisition
|-
| Benjamin Shih || Carnegie Mellon ||
|-
| Collin Lynch || University of Pittsburgh ||
|-
| Erik Zawadzki || Carnegie Mellon ||
|-
| Nan Li || Carnegie Mellon ||
|-
| Amy Ogan || Carnegie Mellon ||
|-
| Dan Belenky || University of Pittsburgh || Psychology
|-
| Matthew Easterday || Carnegie Mellon ||
|-
| Soniya Gadgil || University of Pittsburgh || Psychology
|-
| Yanhui Zhang || Carnegie Mellon ||
|-
| Dejana Diziol || Freiburg ||
|-
| Elizabeth Ayers || Carnegie Mellon ||
|-
| Elsa Golden || Carnegie Mellon ||
|-
| April Galyardt || Carnegie Mellon || Statistics
|-
| Jamie Jirout || Carnegie Mellon || Psychology
|-
| Martina Rau || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tom Lauwers || Carnegie Mellon ||
|-
| Tracy Sweet || Carnegie Mellon || Statistics
|-
| Kevin Del Rosa || Carnegie Mellon ||
|-
| Turadg Aleahmad || Carnegie Mellon || Human Computer Interaction Institute
|-
| Gahgene Gweon || Carnegie Mellon ||
|-
| Anagha Kulkarni (Joshi) || Carnegie Mellon ||
|-
| Bryan Matlen || Carnegie Mellon || Psychology
|-
| Sung-Young Jung || University of Pittsburgh ||
|-
| Gustavo Santos || Carnegie Mellon ||
|-
| Hao-Chuan Wang || Carnegie Mellon ||
|-
| Indrayana Rustandi || Carnegie Mellon ||
|-
| Jessica Nelson || University of Pittsburgh || Psychology
|-
| Rohit Kumar || Carnegie Mellon ||
|-
| Roxana Gheorghiu || University of Pittsburgh ||
|-
| Tamar Degani || University of Pittsburgh || Psychology
|-
| Yan Mu || Carnegie Mellon || Psychology
|-
| Elijah Mayfield || Carnegie Mellon ||
|-
| Erin Walker || Carnegie Mellon ||
|-
| Iris Howley || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tracy Clark || Univeristy of Pennslyvania ||
|-
| Laurens Feestra || Netherlands ||
|-
| Maaike Waalkens || Netherlands ||
|-
| Mary Lou Vercellotti || University of Pittsburgh || Linguistics
|-
| Nozomi Tanaka || University of Pittsburgh || Linguistics
|-
| Eliane Stampfer || Carnegie Mellon || Human Computer Interaction Institute
|-
| Katherine Martin || University of Pittsburgh || Linguistics
|}

== Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Matthew Bernacki || University of Pittsburgh || LRDC
|-
| Gregory Dyke || Carnegie Mellon University || LTI
|-
| Sherice Clarke || University of Pittsburgh || LRDC
|-
| Oscar Saz || Carnegie Mellon University || LTI
|-
| Michael Yudelson || Carnegie Mellon University || HCII
|-
| Gaowei Chen || The University of Pittsbugh || LRDC
|-
| Catherine Chase || Carnegie Mellon University || Psychology
|-
| Amy Ogan || Carnegie Mellon University || HCII
|-
| Laura Halderman || Educational Testing Services ||
|-
| Seiji Isotani || The University of Sao Paulo ||
|-
| Min Chi || Stanford University ||
|-
| John Connelly || Carnegie Learning ||
|-
| Amy Crosson || University of Pittsburgh || LRDC
|-
| Ido Roll || University of British Columbia ||
|-
| Stephanie Siler || Carnegie Mellon || Psychology
|-
| Zelha Tunc-Pekkan || Carnegie Mellon || HCII
|-
| Fan Cao || University of Pittsburgh ||
|-
| Suzanne Adlof || University of Pittsburgh ||
|-
| Candace Walkington || University of Wisconson ||
|}

More information about the PSLC post-docs at the [[PSLC_Postdocs]] wiki page

== Former Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Hua Ai || Georgia Institute of Technology || LTI
|-
| Alicia Chang || University of Delaware || Postdoctoral Researcher
|-
| Connie Guan Qun || University of Pittsburgh ||
|-
| Chin-LungYang || University of Pittsburgh ||
|-
| Scotty Craig || University of Memphis|| Research Assistant Professor, Institute for Intelligent Systems
|-
|}

== Faculty ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Al Corbett || Carnegie Mellon || HCII
|-
| Alan Juffs || University of Pittsburgh || Linguistics
|-
| Brian Junker || Carnegie Mellon || Statisics
|-
| Brian MacWhinney || Carnegie Mellon || Psychology
|-
| Bruce McLaren || Carnegie Mellon || HCII
|-
| Carolyn Rosé || Carnegie Mellon || LTI/HCII
|-
| Charles Perfetti || University of Pittsburgh || LRDC
|-
| Christa Asterhan || Hebrew University ||
|-
| David Klahr || Carnegie Mellon || Psychology
|-
| David Yaron || Carnegie Mellon || Chemistry
|-
| Geoff Gordon || Carnegie Mellon || Machine Learning
|-
| Jack Mostow || Carnegie Mellon || Robotics
|-
| Jim Greeno || University of Pittsburgh || Instruction and Learning
|-
| John Stamper || Carnegie Mellon || HCII
|-
| Ken Koedinger || Carnegie Mellon || HCII
|-
| Kirsten Butcher || University of Utah || Instructional Design & Educational Technology
|-
| Kurt VanLehn || Arizona State University || Computer Science and Engineering
|-
| Lauren Resnick || University of Pittsburgh || LRDC
|-
| Louis Gomez || University of Pittsburgh || School of Education
|-
| Marsha Lovett || Carnegie Mellon || Eberly Center
|-
| Mary Catherine O'Connor || Boston University || School of Education
|-
| Matthew Kam || Carnegie Mellon || HCII
|-
| Maxine Eskenazi || Carnegie Mellon || LTI
|-
| Nel de Jong || Vrije Universiteit Amsterdam ||
|-
| Niels Pinkwart || Clausthal University of Technology ||
|-
| Nikol Rummel || Ruhr-Universität Bochum || Psychology
|-
| Noboru Matsuda || Carnegie Mellon || HCII
|-
| Phil Pavlik || Carnegie Mellon || HCII
|-
| Richard Scheines || Carnegie Mellon || Philosphy
|-
| Ryan Baker || WPI ||
|-
| Sandy Katz || University of Pittsburgh || LRDC
|-
| Sarah Michaels || Clark University || Education
|-
| Sue-mei Wu || Carnegie Mellon || Modern Languages
|-
| Teruko Matamura || Carnegie Mellon || LTI
|-
| Tim Nokes || University of Pittsburgh ||
|-
| Vincent Aleven || Carnegie Mellon || LTI
|-
| William Cohen || Carnegie Mellon || ML
|-
| Ma. Mercedes T. Rodrigo || Ateneo de Manila University
|| Information Systems and Computer Science

|}

== Staff ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [[User:Alida|Alida Skogsholm]] || Carnegie Mellon University || DataShop Manager
|-
| Bob Hausmann || Carnegie Learning ||
|-
| Brett Leber || Carnegie Mellon University || DataShop/CTAT
|-
| Christy McGuire || Edalytics ||
|-
| Cressida Magaro || Carnegie Mellon University ||
|-
| Dorolyn Smith || University of Pittsburgh ||
|-
| Duncan Spencer || Carnegie Mellon University || DataShop
|-
| Gail Kusbit || Carnegie Mellon University || Research Manager
|-
| Jo Bodnar || Carnegie Mellon University ||
|-
| John Kowalski || Carnegie Mellon University ||
|-
| Jonathan Sewall || Carnegie Mellon University ||
|-
| Kevin Willows || Carnegie Mellon University ||
|-
| Mark Haney || University of Pittsburgh ||
|-
| Martin van Velsen || Carnegie Mellon University ||
|-
| Michael Bett || Carnegie Mellon University || Managing Director
|-
| Mike Karabinos|| Carnegie Mellon University ||
|-
| Ross Strader || Carnegie Mellon University ||
|-
| Sandy Demi || Carnegie Mellon University || DataShop/CTAT
|-
| Scott Silliman || University of Pittsburgh || OLI
|-
| Shanwen Yu || Carnegie Mellon University || DataShop
|-
| Steve Ritter || Carnegie Learning || Founder
|-
| Thomas Harris || Edalytics ||
|-
| Tristan Nixon || Carnegie Learning ||
|}

Educational Research Methods 2014

2013-12-20T15:36:05Z

Mbett: /* Flex day (Koedinger) */

==Research Methods for the Learning Sciences 05-748==
Spring 2014 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
5312 Wean Hall

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

Summary Table: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

====Goals====
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

====Course Prerequisites====
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

====Textbook and Readings====
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

====Flipped Homework: Reading Reports and Pre-Class Assignments====

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

====Grading====

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well.
:# Think of it as writing a grant proposal. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. Since this is styled as a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

====Class Schedule in Brief====
* Course Intro: Formulating Good Research Questions: Jan 14 (T)
* Cognitive Task Analysis 1: Jan 16, Feb 18, 20 (RTR)
* Video and Verbal Protocol Analysis: Jan 28, 30, Feb 4,6,11,13 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Cognitive Task Analysis 2: Jan 21, 23, (TR)
* Educational Measurement & Psychometrics: Feb 25, 27, Mar 4 (TRT)
** Guest Instructor: Brian Junker
* Educational Design Research: Mar 6 (R)
* NO CLASS – Spring break, Mar 11, 13 (TR)
* Surveys, Questionnaires, Interviews: Mar 18, 20 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 25, 27, Apr 1 (TRT)
* Flex day: Apr 3 (R)
* Educational Data Mining & Causal Inference: Apr 8, 15, 17 (TTR)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 10 (R)
* Experimental Methods: Apr 22, 24, 29 (TRT)
* Wrap-up: May 1 (R)

====Class Schedule with Readings and Assignments====

'''NOTE:''' This is a "living" document. It carries over some elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro & Formulating Good Research Questions (Koedinger)=====
*1-14
**See your email or [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard] for the pre-class assignment.
**[[Media:CourseIntroGoodQuestions13.pdf|Lecture slides]]
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter1]]
**[Optional (re)reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

=====Cognitive Task Analysis (Koedinger) =====
*1-16
**Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]
**Do a couple short assignments here: http://Assistment.org. Please create and an account, click on "Tutor", "Enroll in a class", select "Ken Koedinger" and "Educational Research Methods".
**Slides: [[Media:CTA1-2013.pdf|CTA1-2013.pdf]]
**[Optional reading] Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
*1-21
**Clark, R. E., Feldon, D., van Merriënboer, J., Yates, K., & Early, S. (2007). Cognitive task analysis: In J. M. Spector, M. D. Merrill, J. J. G. van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 577–593). Mahwah, NJ: Lawrence Erlbaum Associates. [[Media:Clarketal2007-CTAchapter.pdf|Clarketal2007-CTAchapter.pdf]]
***One point of reflection for you on the Clark et al reading is to compare and contrast the Cognitive Task Analysis (CTA) methods and output representations recommended with the approach taken by Zhu & Simon. Also, note their examples and claims about the power of CTA for improving instruction. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". How is this claim similar or different from Zhu & Simon? The notion of adaptive expertise is also important and interesting.
***As you read the 1-22 and 1-24 readings, be thinking about steps you could take to do a cognitive task analysis, empirical and rational, in a domain of your interest. Think about what tasks you would use, what CTA technique(s), and how might represent the output of your analysis.
**Slides: [[Media:CTA2-2013.pdf|CTA2-2013.pdf]]
*1-23
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
**Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) outside of math domains. The Aleven et al reading provides an example of a CTA at the level of metacognitive skills. The Siegler reading shows a CTA dealing with younger kids. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) how do the authors represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
**In the first forum (where you posted one of your research topics), reply to your thread with a post that describes an example task that you could productively analyze in your domain of interest. You might also indicate some variations on the task that might help reveal what is most challenging for learners.
**Slides: [[Media:CTA3-2013.pdf|CTA3-2013.pdf]]
*Other possible readings:
**Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The plan for these six sessions in 2014, 1-28 to 2-13, is in [[Media:PIERResearchMethodsPlan2013.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://www.cs.cmu.edu/~emayfiel/side.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 28]: Overview of Protocol Analysis

**In this introductory discussion, we will explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

*Session 2[Jan 30 Carolyn]: Protocol Analysis of Collaborative Learning Discussions

**In this session we will explore the connection between talk and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse.

**Howley, I., Adamson, D., Dyke, G., Mayfield, E., Beuth, J. & Rosé, C. (2012). Group Composition and Intelligent Dialogue Tutors for Impacting Students’ Academic Self-efficacy. Proceedings of the Intelligent Tutoring Systems Conference [[http://www.cs.cmu.edu/~emayfiel/application_papers/120113ITS12_ikh_07cpr.pdf]].

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Coding Manual for Negotiation [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Discussion Questions:
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in the Howley et al., 2012 study?

*Session 3[Feb 4 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 6 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 11 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 13 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://www.cs.cmu.edu/~emayfiel/side.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis - Revisited (Koedinger) =====
*2-18
**Do one post on [[Media:Applying-CTA-assignment.docx|this assignment]] and a second post on the reading.
**In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. While the assignment is a rational CTA, note the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions in the assignment. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
**Optional: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]

*2-20
**Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
*Other optional readings
**Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
**Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-25

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*2-27

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-4

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-6 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-11 and 3-13 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-18
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-20
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-25
**Readings:
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2013_(Geom_Area_Unit_Spring_2010).doc | Learning-curve-assignment-2013.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. (See my emails, original and followup, for further directions on how to do this assignment.)
*3-27
**Read the following paper and make two posts on the general topic of this reading and the last, namely, using educational technology data as a basis for discovering improvements to cognitive models.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**Also, do some thinking about a semester project so we can discuss (and I can give feedback) on your possible ideas for a project.
*4-1
**Please finish off one of the two exercises you started for last class. See A or B further below. In either case, provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard.
**ALSO, make a post about your idea for a course final project. What method might you apply to address what research question?
**No required reading assignment.
***Optional readings:
***'''Optional:''' Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-3 To be used in case of rescheduling or for a student-driven topic.
***And/or for Review of Projects or Past Topics
**Option1. More on Educational Data Mining

**Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
go to: http://oli.web.cmu.edu/openlearning/
in the left tab, go to "Prior work..." and then "Empirical Research Methods"
click on Peek In
complete Unit 2

*4-10 NO CLASS - Spring Carnival

*4-15
**Read Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17 Continue discussion of Causal Inference from Data & TETRAD

=====Experimental Research Methods (Koedinger)=====
*4-22
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-24 NO CLASS
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2014

2013-12-20T15:35:34Z

Mbett:

==Research Methods for the Learning Sciences 05-748==
Spring 2014 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
5312 Wean Hall

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

Summary Table: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

====Goals====
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

====Course Prerequisites====
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

====Textbook and Readings====
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

====Flipped Homework: Reading Reports and Pre-Class Assignments====

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

====Grading====

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well.
:# Think of it as writing a grant proposal. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. Since this is styled as a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

====Class Schedule in Brief====
* Course Intro: Formulating Good Research Questions: Jan 14 (T)
* Cognitive Task Analysis 1: Jan 16, Feb 18, 20 (RTR)
* Video and Verbal Protocol Analysis: Jan 28, 30, Feb 4,6,11,13 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Cognitive Task Analysis 2: Jan 21, 23, (TR)
* Educational Measurement & Psychometrics: Feb 25, 27, Mar 4 (TRT)
** Guest Instructor: Brian Junker
* Educational Design Research: Mar 6 (R)
* NO CLASS – Spring break, Mar 11, 13 (TR)
* Surveys, Questionnaires, Interviews: Mar 18, 20 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 25, 27, Apr 1 (TRT)
* Flex day: Apr 3 (R)
* Educational Data Mining & Causal Inference: Apr 8, 15, 17 (TTR)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 10 (R)
* Experimental Methods: Apr 22, 24, 29 (TRT)
* Wrap-up: May 1 (R)

====Class Schedule with Readings and Assignments====

'''NOTE:''' This is a "living" document. It carries over some elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro & Formulating Good Research Questions (Koedinger)=====
*1-14
**See your email or [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard] for the pre-class assignment.
**[[Media:CourseIntroGoodQuestions13.pdf|Lecture slides]]
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter1]]
**[Optional (re)reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

=====Cognitive Task Analysis (Koedinger) =====
*1-16
**Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]
**Do a couple short assignments here: http://Assistment.org. Please create and an account, click on "Tutor", "Enroll in a class", select "Ken Koedinger" and "Educational Research Methods".
**Slides: [[Media:CTA1-2013.pdf|CTA1-2013.pdf]]
**[Optional reading] Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
*1-21
**Clark, R. E., Feldon, D., van Merriënboer, J., Yates, K., & Early, S. (2007). Cognitive task analysis: In J. M. Spector, M. D. Merrill, J. J. G. van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 577–593). Mahwah, NJ: Lawrence Erlbaum Associates. [[Media:Clarketal2007-CTAchapter.pdf|Clarketal2007-CTAchapter.pdf]]
***One point of reflection for you on the Clark et al reading is to compare and contrast the Cognitive Task Analysis (CTA) methods and output representations recommended with the approach taken by Zhu & Simon. Also, note their examples and claims about the power of CTA for improving instruction. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". How is this claim similar or different from Zhu & Simon? The notion of adaptive expertise is also important and interesting.
***As you read the 1-22 and 1-24 readings, be thinking about steps you could take to do a cognitive task analysis, empirical and rational, in a domain of your interest. Think about what tasks you would use, what CTA technique(s), and how might represent the output of your analysis.
**Slides: [[Media:CTA2-2013.pdf|CTA2-2013.pdf]]
*1-23
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
**Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) outside of math domains. The Aleven et al reading provides an example of a CTA at the level of metacognitive skills. The Siegler reading shows a CTA dealing with younger kids. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) how do the authors represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
**In the first forum (where you posted one of your research topics), reply to your thread with a post that describes an example task that you could productively analyze in your domain of interest. You might also indicate some variations on the task that might help reveal what is most challenging for learners.
**Slides: [[Media:CTA3-2013.pdf|CTA3-2013.pdf]]
*Other possible readings:
**Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The plan for these six sessions in 2014, 1-28 to 2-13, is in [[Media:PIERResearchMethodsPlan2013.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://www.cs.cmu.edu/~emayfiel/side.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 28]: Overview of Protocol Analysis

**In this introductory discussion, we will explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

*Session 2[Jan 30 Carolyn]: Protocol Analysis of Collaborative Learning Discussions

**In this session we will explore the connection between talk and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse.

**Howley, I., Adamson, D., Dyke, G., Mayfield, E., Beuth, J. & Rosé, C. (2012). Group Composition and Intelligent Dialogue Tutors for Impacting Students’ Academic Self-efficacy. Proceedings of the Intelligent Tutoring Systems Conference [[http://www.cs.cmu.edu/~emayfiel/application_papers/120113ITS12_ikh_07cpr.pdf]].

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Coding Manual for Negotiation [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Discussion Questions:
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in the Howley et al., 2012 study?

*Session 3[Feb 4 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 6 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 11 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 13 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://www.cs.cmu.edu/~emayfiel/side.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis - Revisited (Koedinger) =====
*2-18
**Do one post on [[Media:Applying-CTA-assignment.docx|this assignment]] and a second post on the reading.
**In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. While the assignment is a rational CTA, note the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions in the assignment. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
**Optional: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]

*2-20
**Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
*Other optional readings
**Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
**Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-25

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*2-27

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-4

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-6 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-11 and 3-13 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-18
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-20
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-25
**Readings:
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2013_(Geom_Area_Unit_Spring_2010).doc | Learning-curve-assignment-2013.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. (See my emails, original and followup, for further directions on how to do this assignment.)
*3-27
**Read the following paper and make two posts on the general topic of this reading and the last, namely, using educational technology data as a basis for discovering improvements to cognitive models.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**Also, do some thinking about a semester project so we can discuss (and I can give feedback) on your possible ideas for a project.
*4-1
**Please finish off one of the two exercises you started for last class. See A or B further below. In either case, provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard.
**ALSO, make a post about your idea for a course final project. What method might you apply to address what research question?
**No required reading assignment.
***Optional readings:
***'''Optional:''' Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-03 To be used in case of rescheduling or for a student-driven topic.
***And/or for Review of Projects or Past Topics
**Option1. More on Educational Data Mining

**Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
go to: http://oli.web.cmu.edu/openlearning/
in the left tab, go to "Prior work..." and then "Empirical Research Methods"
click on Peek In
complete Unit 2

*4-10 NO CLASS - Spring Carnival

*4-15
**Read Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17 Continue discussion of Causal Inference from Data & TETRAD

=====Experimental Research Methods (Koedinger)=====
*4-22
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-24 NO CLASS
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Social and Communicative Factors in Learning

2013-04-04T13:39:24Z

Mbett:

During PSLC’s first four years, its [[Interactive Communication]] Cluster has studied interactions between a student and a tutor (either human or computer) or, less frequently, two students interacting with each other. Most of the experimental manipulations and subsequent analyses have focused on the cognitive content of interaction through learning space analyses, in other words, the what and when of instruction. Study results investigating the effect of interaction, although somewhat mixed, have largely supported the hypothesis that focused interaction promotes cognitive aspects of learning such as attention to the most important knowledge components in a domain, deeper cognitive processing, and increased engagement with the content. VanLehn and colleagues (2007) present a thorough review of this literature as well as results from recent investigations. These results encouraged early PSLC efforts to “unpack” the nature of communicative interaction in instruction and learning. Rummel and colleagues (Diziol, Rummel, Kahrimanis, et al., 2008a, 2008b), for example have recently evaluated interactions with a rating scheme analysis that quantifies the quality of an interaction on a number of dimensions. This work represents an important step towards the type of up close inspection of communication that many scholars believe is necessary if we are to understand, and be able to manipulate for instructional purposes, how communication works to produce robust learning.

In our re-named Social-Communicative Factors thrust, we propose now to expand our investigations of communication as a core enabler of robust learning to include detailed study of patterns of interaction, the role of conversation and structured talk in initiating and sustaining learning, and the effects on motivation, self-attribution and commitment to a learning group that are associated with learning through social-communicative interaction. Specifically, we propose to investigate how human linguistic interaction works in instruction and learning, and how participants in learning exchanges (both teachers and students) can best be taught productive forms of interaction. We draw from our extensive prior work related separately to classroom discourse (Chapin & O’Connor, 2004; Bill et al., 1992; Resnick et al., 1992) and collaborative learning (Gweon et al., 2007; Joshi & Rose, 2007; Rummel & Diziol, 2008). We note that, although the classroom discourse and collaborative learning communities have proceeded mainly independently from one another, the conversational processes identified as valuable within these two communities are strongly overlapping.

Investigations of valuable conversational contributions have been conducted both within communities exploring the cognitive foundations of group learning and the sociocultural community. Regardless of the theoretical framework, the same ideas have surfaced under a number of different names including [[Accountable Talk]] (Michaels, O’Connor & Resnick, 2007; Resnick, O'Connor, & Michaels, 2007), transactivity (Berkowitz & Gibbs, 1984; Teasley, 1997; Weinberger & Fishcer, 2006; King, 1999), productive agency (Schwartz, 1999), and uptake (Suthers, 2006), and have been demonstrated to predict learning both in collaborative learning contexts (Azimita & Montgomery, 1993; Joshi & Rose, 2007) and classroom contexts (O’Connor et al., 2007). For example, one cognitive justification for the value of transactive conversational behavior is its connection with cognitive conflict (Piaget, 1985), where transactive conversational moves highlight differences between the mental models of collaborating students. One can argue that a major cognitive benefit of collaborative learning is that when students bring differing perspectives to a problem-solving situation, the interaction causes the participants to consider questions that might not have occurred to them otherwise. This stimulus could cause them to identify gaps in their understanding, which they would then be in a position to address. This type of cognitive conflict has the potential to lead to productive shifts in student understanding. It has the potential to elicit elaborate explanations from students that are associated with learning (Webb, Nemer, & Zuniga 2002). From the sociocultural perspective, based on Vygotsky’s seminal work (Vygotsky 1978), we can similarly argue that when students who have different strengths and weaknesses work together, they can provide support for each other that allows them to solve problems that would be just beyond their reach if they were working alone.

We will proceed with two interacting research strategies: one, expanding capacities for recording, coding and analyzing interactive communication that can be at least partially automated; and two, conducting in vivo experiments on ways of teaching participants the most promising patterns of interactive communication and testing the effects of these patterns on measures of robust learning.

In the first thread of our proposed work, we will work toward a common conceptual framework that unifies the classroom discourse, collaborative learning and instructional tutoring communities. To this end, we plan to develop a concrete and precise formalization on a linguistic level of what counts as performing these valued conversational moves. This concrete formalization will provide a common language for documenting and investigating the specific ways in which social-communicative practices can promote (or hinder) learning of complex mathematics and science content and reasoning skills.

In the second thread of our proposed work, we will examine causal connections between these communicative processes and learning by running in vivo experiments in which specific social-communicative practices are introduced into well-defined mathematics and science units of study. We will begin by replicating and extending a series of in vivo experiments on the effects of [[Accountable Talk]] in low-income urban classrooms with high proportions of English language learners in Chelsea, Massachusetts (O’Connor et al 2007; NHSF REC 0231893, PI: O’Connor). In a tightly controlled series of three-day studies in 5th and 6th grade classrooms, O’Connor’s group sought to determine whether it was possible to get evidence supporting a hypothesized causal relationship between selected discourse-intensive instructional practices and student mathematics learning. In previous non-experimental studies in Chelsea, students had shown large gains on standardized tests after a year or more of discourse-intensive instruction, but it was not possible to test the specific features of the intervention that produced these effects. Thus it was possible that cognitive and metacognitive abilities might improve over months of practice in clarifying, justifying and describing mathematical ideas, whether or not explicit transactive communication strategies were employed. Similarly, student motivation might have improved due to long-term participation in an intensive mathematics program, without a specific impact of particular forms of linguistic participation.

We will design and run in vivo experiments to test more specific hypotheses concerning specific [[Accountable Talk]] moves. Subsequent studies will test a larger intervention that includes training in the most effective conversational moves and collaborative scripts with implementation in a number of classrooms. The studies will focus on math and science learning topics. These studies will make use of techniques from automatic collaborative learning process analysis (Rose et al., in press; Wang et al., 2007; Donmez et al., 2005) and script-based support for productive collaboration (Dillenbourg & Jermann, 2007; Kollar, Fischer, & Hesse, 2006; Rummel & Spada, 2007; Diziol, Rummel, Kahrimanis, Spada & Avaris, 2008; Diziol et al., 2008; Walker, Rummel, McLaren & Koedinger, 2007) to carefully manipulate these properties of conversation in highly controlled and context sensitive ways.

== Descendants ==

To create a new project page, enclose your project name in a double set of brackets. Details for a project format may be [[ Project_Page_Template_and_Creation_Instructions | found here.]]

*[[Rose - Integrated framework for analysis of classroom discussions]]
*[[Features of Adaptive Assistance that Improve Peer Tutoring in Algebra (Walker, Rummel, Koedinger)]]
*[[Resnick Project]]

== References ==

* Chi, M.T., Roy, M., & Hausmann, R.G. (March, 2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive Science: A Multidisciplinary Journal, 32:2, 301-341. [[Media:Chi_Observing_Tutorial_Dialogues.pdf | Click to download]]

* Michaels, S., O’Connor, C., & Resnick, L. B. (2008). Deliberative discourse idealized and realized: Accountable talk in the classroom and in civic life. Studies in the Philosophy of Education, 27(4), 283-297.

* Meier, A., Spada, H. & Rummel, N. (2007). A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning, 2, 63-86. [[Media: Meier_Rating_Scheme.pdf| Click to download]]

* Resnick, L., O'Connor, C., and Michaels, S. (2007). Classroom Discourse, Mathematical Rigor, and Student Reasoning: An Accountable Talk Literature Review.[[Media: Accountable_Talk_Lit_Review.pdf | Click to download]]

* Rose, C., et al. (2007). Analyzing collaborative learning processes automatically: Exploiting the advance of computational linguistics in computer-supported collaborative learning. [[Media: Rose_Analyzing_Collaborative.pdf | Click to download]]

* Walker, E., Rummel, N., & Koedinger, K. (2008). A Research-Oriented Architecture for Providing Adaptive Collaborative Learning Support [[Media: Walker_Architecture_for_Learing.pdf? | Click to download]]

* Yamakawa,Y., Forman, E., and Ansell, E. (2005). The role of positioning in constructing an identity in a third grade mathematics classroom. [[Media: Yamakawa_role_of_positioning.pdf| Click to download]]

[[Link title]]== Other recommended readings on the role of classroom dialogue in learning and development==
* Adey, P.S. & Shayer, M. (1990). Accelerating the development of formal thinking in middle and high school students. Journal of Research in Science Teaching, 27(31), 267 - 285.
* Adey, P. & Shayer, M. (1993). An Exploration of Long-Term Far-Transfer Effects Following an Extended Intervention Program in the High School Science Curriculum. Cognition & Instruction, 11, 1 - 29.
* Adey, P., & Shayer, M. (2001). Thinking Science. London: Nelson Thormes.
* Adey, P. (2005). Issues arising from the long-term evaluation of cognitive acceleration programs. Research in Science Education, 35, 3-22.
* Adey, P.S. & Shayer, M. (1994). Really Raising Standards: cognitive intervention and academic achievement. London: Routledge.
* [http://www.robinalexander.org.uk Alexander, R.] (2000) Culture and pedagogy: International comparisons in primary education Blackwell , Oxford.
* Alexander, R. (2008) Towards teaching: Rethinking classroom talk. 4th ed., Dialogos , York, England
* [http://www.robinalexander.org.uk Alexander, R.] Mercer, N. and Hodgkinson, S. (eds) (2005) [[Media: Robinalexander_IACEP_2005.pdf | Culture, dialogue and learning: Notes on an emerging pedagogy. Exploring talk in school]] Sage , London.
* Anderson, R. C., Chinn, C., Waggoner, M., Nquyen, K. (1998). Intellectually stimulating story discussions. In J. Osborn & F. Lehr (Eds), Literacy for all: Issues in teaching and learning (170-187). New York, NY: Guilford Press.
* Anderson. R. C., Chinn, C., Chang, J., Waggoner, M., & Yi, H. (1997). On the Logical Integrity of Children's Arguments. Cognition and Instruction, 15 (2 ), 135 – 167.
* Applebee, A. N., Langer, J. A., Nystrand, M., & Gamoran, A. (2003). Discussion-Based Approaches to Developing Understanding: Classroom Instruction and Student Performance in Middle and High School English. American Educational Research Journal, 40, 685-730.
* Asterhan, C. S. C., & Schwarz, B. B. (2009). Transformation of robust misconceptions through peer argumentation. In: B. B. Schwarz, T. Dreyfus, & R. Hershkowitz (Eds.) Guided Transformation of Knowledge in Classrooms (159-172). New York, NY: Routledge, Advances in Learning & Instruction series.
* Asterhan, C. S. C. & Schwarz, B. B. (in press). Online human guidance of small group discussions: The case of synchronous e-argumentation in a diagram-based discussion space. International Journal of Computer-Supported Collaborative Learning.
* Asterhan, C. S. C. & Schwarz, B. B. (2009). Argumentation and explanation in conceptual change: Indications from protocol analyses of peer-to-peer dialogue. Cognitive Science, 33, 373-399.
* Asterhan, C. S. C. & Schwarz, B. B. (2007). The effects of monological and dialogical argumentation on concept learning in evolutionary theory. Journal of Educational Psychology, 99, 626-639.
* Ball, D. L., & Bass, H. (2000). Making believe: the collective construction of public mathematical knowledge in the elementary classroom. In D. Phillips (Ed.), Yearbook of the national society for the study of education, Constructivism in education. (pp. 193–224). Chicago: University of Chicago Press.
* Beck, I. L., & McKeown, M. G., (2006). Improving comprehension with Questioning the Author: A fresh and expanded view of a powerful approach. NY: Scholastic.
* Bernstein, B. (1971/2003). Class, Codes and Control: Theoretical studies towards a sociology of language. London, UK: Routledge.
* Bill, V. L., Leer, M. N., Reams, L. E., & Resnick, L. B. (1992). From cupcakes to equations: The structure of discourse in a primary mathematics classroom. Verbum, 15(1), 63-85
* Boaler, J. (2006). How a Detracked Mathematics Approach Promoted Respect, Responsibility, and High Achievement. Theory Into Practice, 45(1), p40-46.
* Brown, A. L., & Palincsar, A. S. (1989). Guided, cooperative learning and individual knowledge acquisition. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 393-451). Hillsdale, NJ: Erlbaum. Cambridge, MA: Harvard University Press.
* Cazden, C. (2001). Classroom discourse: The language of teaching and learning. Portsmouth, NH: Heinemann.
* Chapin, S. & O’Connor, M.C. (2004). Project Challenge: Identifying and developing talent in mathematics within low-income urban schools. Boston University School of Education Research Report No. 1, 1-6.
* Chi, M. T. H., de Leeuw, N., Chiu, M., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.
* Chi, M. T. H., Roy, M., & Hausmann, R. G. M. (2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectivness from vicarious learning. Cognitive Science, 33, 301–341.
* Chi, M.T.H., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R. (2001). Learning from human tutoring. Cognitive Science, 25, 471-534.
* Chin, C. & Osborne, J. (in press). Supporting argumentation through students’ questions: Case studies in science classrooms. To appear in the Journal of the Learning Sciences.
* Chinn, C. A., & Anderson, R. C. (1998). The structure of discussions that promote reasoning. Teachers College Record, 100, 315–368.
* Cobb, P., Wood, T., Yackel, E., Nicholls, J., Wheatley, G., Trigatti, B., et al. (1991). Assessment of a Problem-Centered Second-Grade Mathematics Project. Journal for Research in Mathematics Education, 22(1), 3-29.
* Coleman, E. B. (1998). Using explanatory knowledge during problem solving in science. Journal of the Learning Sciences, 7, 387–427.
* DeVries, E., Lund, K., & Baker, M. (2002). Computer-mediated epistemic dialogue: Explanation and argumentation as vehicles for understanding scientific notions. Journal of the Learning Sciences, 11, 63–103.
* Driver, R., Newton, P., Osborne, J. Establishing the norms of scientific argumentation in classrooms.
* Duschl, R. A., & Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education, 38,39–72.
* Engle, R. A. & Conant, F. C. (2002). Guiding principles for fostering productive disciplinary engagement: Explaining an emergent argument in a community of learners classroom. Cognition & Instruction, 20(4), 399-483.
* Felton, M. & Kuhn, D. (2001) The Development of argumentive discourse skill. Discourse Processes, 32(2&3), 135–153
* Ford, M. J., & Forman, E. A. (2006). Redefining disciplinary learning in classroom contexts. In J. Green & A. Luke (Eds.), Review of Research in Education (Vol. 30, pp. 1-32). Washington, DC: American Educational Research Association.
* Gee, J. P. (1996). Social linguistics and literacies: Ideology in discourses. Bristol, PA: Taylor & Francis.
* Gillies, R. M. (2004). The effects of communication training on teachers’and students’verbal behaviours during cooperative learning. International Journal of Educational Research, 41, 257–279.
* Goldberg, T., Schwarz, B. B., & Porat, D (2008). Living and dormant collective memories as contexts of history learning. Learning and Instruction, 18, 223-237.
* Hart, B., & Risley, R. T. (1995). Meaningful differences in the everyday experience of young American children. Baltimore: Paul H. Brookes.
* Howe, C., Tolmie, A., Duchak-Tanner, V., & Rattay, C. (2000). Hypothesis-testing in science: Group consensus and the acquisition of conceptual and procedural knowledge. Learning & Instruction, 10, 361-391.
* Hugener, I., Pauli, C., Reusser, K., Lipowsky, F., Rakoczy, K., & Klieme, E. (2009). Teaching patterns and learning quality in Swiss and German mathematics lessons. Learning and Instruction, 19(1), 66-78.
* Iordanou, K. & Kuhn, D. (2009). Arguing on the computer in scientific and non-scientific domains. In C. O'Malley, D. Suthers, P. Reimann & A. Dimitracopoulou (Eds), Computer-Supported Collaborative Learning Practices: CSCL2009 Conference Proceedings (pp. 576-585). [http://www.bestessayhelp.com custom writing services]
* King, A., & Rosenshine, B. (1993). Effects of guided cooperative questioning on children’s knowledge construction. Journal of ExperimentalEducation, 61, 127–148.
* Kuhn, D. & Udell, W. (2003). The Development of Argument Skills. Child Development, 74 (5), 1245-1260.
* Kuhn, D. (1999). A developmental model of critical thinking. Educational Researcher, 28, 16-25.[http://www.bestessayhelp.com/essay-help/buy-essay buy essays online]
* Kuhn, D., Shaw, V., & Felton, M. (1997). Effects of dyadic interaction on argumentative reasoning. Cognition and Instruction, 15, 287–315.
* Lefstein, A. & Snell, J. (in press). Classroom Discourse: The Promise and Complexity of Dialogic Practice. To appear in : S. Ellis, E. McCartney, J. Bourne (Eds), Insight and Impact: Applied Linguistics and the Primary School, Cambridge, UK: Cambridge University Press
* Lipman, M. (1975). Philosophy for Children. . ERIC Document Reproduction Service No. ED103296.
* Mason, L. (1998). Sharing cognition to construct scientific knowledge in school context: The role of oral and written discourse Instructional Science, 26: 359–389.
* McKeown, M. G., Beck, I., & Blake, R. G. K. (2009). Rethinking reading comprehension instruction: A comparison of instruction for strategies and content approaches. Reading Research Quarterly, 44, 218-253.
* Mercer, N., Dawes, L et al. (2004). Reasoning as a Scientist: Ways of Helping Children to Use Language to Learn Science. British Educational Research Journal, 30(3): 359-377.
* Mercer, N., Dawes, L., Wegerif, R., & Sams, C. (2004). Reasoning as a scientist: Ways of helping children to use language to learn science. British Educational Research Journal, 30, 359–377.
* Mercer, N. & Littleton, K. (2007) Dialogue and the Development of Children's Thinking: a sociocultural approach. London: Routledge.
* Mercer, N., Wegerif, R. & Dawes, L. (1999). Children's Talk and the Development of Reasoning in the Classroom. British Educational Research Journal, 25, 95-111.
* Murphy, P. K., Wilkinson, I.A.G., Soter, A. o., Henessey, M. N., & Alexander, J. F. (2009). Examining the effects of classroom discussion on students’ comprehension of text: A meta-analysis. Journal of Educational Psychology, 101, 740-764.
* Nussbaum, E. M., & Sinatra, G. M. (2003). Argument and conceptual engagement. Contemporary Educational Psychology, 28, 384–395.
* Nystrand, M. & Gamoran, A. (1991). Instructional Discourse, Student Engagement, Literature Achievement. Research in the Teaching of English, 25(3): 261-290.
* Palincsar, A-M., & Brown A. L. (1984). Reciprocal Teaching of Comprehension-Fostering and Comprehension-Monitoring Activities. Cognition & Instruction, 1(2) 117-175
* Pontecorvo, C., & Girardet, H. (1993). Arguing and reasoning in understanding historical topics. Cognition and Instruction, 11, 365-395.
* Resnick, L. B., & Nelson-Le Gall, S. (1997). Socializing intelligence. In L. Smith, J. Dockrell, & P.
Eds.), Piaget, Vygotsky and beyond (pp. 145-158). London/New York: Routledge.
* Resnick, L. B., Bill, V., Lesgold, S., & Leer, M. (1991). Thinking in arithmetic class. In B. Means, C. Chelemer, & M. S. Knapp (Eds.), Teaching advanced skills to at-risk students: Views from research and practice (pp. 27-53). San Francisco: Jossey-Bass.
* Resnick, L.B., Michaels, S., & O’Connor, C. (in press). How (well structured) talk builds the mind. In R. Sternberg & D. Preiss (Eds.), From Genes to Context: New Discoveries about Learning from Educational Research and Their Applications. New York: Springer.
* Resnick, L. B., Salmon, M. H., Zeitz, C. M., Wathen, S. H., & Holowchak, M. (1993). Reasoning in conversation. Cognition and Instruction, 11, 347-364.
* Reznitskaya, A., Anderson, R. C., McNurlen, B., Nguyen-Jahiel, K., Archodidou, A., & Kim, S. (2001). Influence of oral discussion on written argument. Discourse Processes, 32(2-3), 155-157.
* Sandora, C., Beck, I. & McKeown, M. (1999). A comparison of two discussion strategies on students’ comprehension and interpretation of complex literature. Journal of Reading Psychology, 20, 177-212.
* Schwarz, B. B., & Asterhan, C. S. C. (2010). Argumentation and Reasoning. To appear in: K. Littleton, C. Wood, & J. Kleine Staarman (Eds). International Handbook of Psychology in Education. Bingley, UK: Emerald Group Publishing.
* Schwarz, B. B. & Asterhan, C. S. C. (in press). E-moderation of synchronous discussions in educational settings: A nascent practice. Journal of the Learning Sciences.
* Schwarz, B. B., Neuman, Y., & Biezuner, S. (2000). Two wrongs may make a right...if they argue together! Cognition & Instruction, 18, 461-494.
* Seymour, J. R. & Lehrer, R. (2006). Tracing the evolution of pedagogical content knowledge as the development of interanimated discourses. Journal of the Learning Sciences, 15, 549-582.
* Sfard, A. (2008). Thinking as communicating: Human development, the growth of discourses, and mathematizing. Cambridge, UK: Cambridge University Press.
* Shayer, M. (1999). Cognitive acceleration through science education II: its effects and scope. International Journal of Science Education, 21(8), 883 - 902.
* Simon, S. & Richardson, K. (2009). Argumentation in School Science: Breaking the Tradition of Authoritative Exposition Through a Pedagogy that Promotes Discussion and Reasoning. Argumentation, 23,469–493
* Stein, M. K., Engle, R. A., Smith, M. S. & Hughes, E. K. (2008). Orchestrating Productive Mathematical Discussions: Five Practices for Helping Teachers Move Beyond Show and Tell. Mathematical Thinking & Learning, 10, 313–340.
* Topping, K. J. & S. Trickey (2007a). Collaborative philosophical enquiry for school children: Cognitive effects at 10-12 years. British Journal of Educational Psychology, 77, 271-288.
* Topping, K. J. & Trickey, S. (2007b). Collaborative philosophical inquiry for schoolchildren: Cognitive gains at 2-year follow-up. British Journal of Educational Psychology, 77, 787-796.
* Walshaw, M., & Anthony, G. (2008). The Teacher’s Role in Classroom Discourse: A Review of Recent Research Into Mathematics Classrooms. Review of Educational Research, 78(3), 516-551.
* Webb, N. M. (2009). The teacher’s role in promoting collaborative dialogue in the classroom. British Journal of Educational Psychology, 79, 1-28.
* Webb, N. M., & Palincsar, A. S. (1996). Group processes in the classroom. In D. Berliner & R. Calfee (Eds.), Handbook of educational psychology (pp. 841–873). New York, NY: Macmillan.
* Webb, N., Franke, M. L., Ing, M., Chan, A., De, T., Freund, D., & Battey, D. (2009). The role of teacher instructional practices in student collaboration. Contemporary Educational Psychology, 33, 360-381.
* Wegerif, N., Mercer, N. & Dawes, L. (1999). From social interaction to individual reasoning: an empirical investigation of a possible socio-cultural model of cognitive development. Learning & Instruction, 9. 493-516.

== Meeting Notes ==
'''Social and Communicative Factors Thrust Workshop on Coding and Analysis of Classroom Dialogue, Pittsburgh May 26-27, 2011'''
*''Effects of Social Metacognition on Micro-Creativity: Statistical Discourse Analyses of Group Problem Solving'' - Ming Ming Chiu [[Media:CHIU_-Social_Metacognition.pptx | Click to download]]
*''Dialogue Analysis to Inform the Development of a Natural-language Tutoring System for Physics'' - Sandra Katz, Michael Ford, Pamela Jordan, Diane Litman
*''Temporal patterns of knowledge construction: Statistical discourse analysis of a role-based online discussion'' - Ming Ming Chiu & Alyssa Wise [[Media:Knowledge_construction.pptx‎ | Click to download]]
*''Analyzing Teacher-Led Talks: A Talk Map Representation'' - Gaowei Chen
*''Towards Academically Productive Talk Supported by Conversational Agents'' - Carolyn Penstein Rosé, Lauren Resnick, Gregory Dyke, Iris Howley, Rohit Kumar [[Media:Bio-Analysis-Carolyn.pdf | Click to download]]
*''What (if anything) about framing? Is it significant? Do we need to consider it?''- Jim Greeno [[Media:WorkshopSlides.pptx | Click to download]]
*''Measuring Classroom Discussions'' - Rip Correnti, Moddy McKeown, Jimmy Scherrer, Peg Smith, Mary Kay Stein, Kevin Ashley, Jim Greeno

Accountable Talk

2012-12-04T16:16:40Z

Mbett: Undo revision 12519 by Tonyguards (Talk)

Resnick, O'Connor, & Michaels (2007) illustrate accountable talk moves as follows:

The six most important talk moves and an example of each move in its prototypical form
follows: Talk Move (1) Revoicing: “So let me see if I’ve got your thinking right. You’re saying
XXX?” (with time for students to accept or reject the teacher’s formulation); (2) Asking students
to restate someone else’s reasoning: “Can you repeat what he just said in your own words?”; (3)
Asking students to apply their own reasoning to someone else’s reasoning:
“Do you agree or disagree and why?”; (4) Prompting students for further participation: “Would
someone like to add on?”; (5) Asking students to explicate their reasoning: “Why do you think
that?” or “How did you arrive at that answer?” or “Say more about that”; (6) Challenge or
Counter Example: “Is this always true?” or “Can you think of any examples that would not
work?”

Accountable talk should be accountable to community, accurate knowledge, and rigorous reasoning.

* Resnick, L., O'Connor, C., and Michaels, S. (2007). Classroom Discourse, Mathematical Rigor, and Student Reasoning: An Accountable Talk Literature Review.

Accountable Talk

2012-12-04T16:15:50Z

Mbett: Reverted edits by Tonyguards (Talk); changed back to last version by Stefficorneliusa

Active Processing

2012-12-04T16:15:17Z

Mbett:

Active processing refers to sets of procedures in which a learner acts on instructional inputs to generate, re-organize, self-explain, or otherwise goes beyond the encoding of presented material. Active processing in learning or testing may result in more learning. See the [[testing effect]].

[[Category:Glossary]]
[[Category:Learning Processes]]
[[Category:PSLC General]]
[[Category:Refinement and Fluency]]

Active Processing

2012-12-04T16:14:01Z

Mbett: Reverted edits by Tonyguards (Talk); changed back to last version by Stefficorneliusa

PSLC People

2012-03-23T17:31:31Z

Mbett: /* Advisory Board */

== '''The Executive Committee''' ==

=== Directors ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [http://pact.cs.cmu.edu/koedinger.html '''Ken Koedinger'''] || Carnegie Mellon University || Human-Computer Interaction Institute
|-
| '''Charles Perfetti''' || University of Pittsburgh || Psychology, LRDC Director
|}

=== Managing Director ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Michael Bett''' || Carnegie Mellon University || Human-Computer Interaction Institute
|}

=== Members ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aleven, Vincent || Carnegie Mellon University || Human-Computer Interaction
|-
| Eskenazi, Maxine || Carnegie Mellon University || Language Technologies Institute
|-
| Fiez, Julie || University of Pittsburgh || Psychology
|-
| Gordon, Geoff || Carnegie Mellon University || Machine Learning
|-
| Klahr, David || Carnegie Mellon University || Psychology
|-
| Lovett, Marsha || Carnegie Mellon University || Psychology
|-
| Nokes, Tim || University of Pittsburgh || LRDC
|-
| Resnick, Lauren || University of Pittsburgh || Learning Research and Development Center
|-
| Rose, Carolyn || Carnegie Mellon University || Human-Computer Interaction Institute/Language Technologies Institute
|}

=== Liasons ===

{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Stamper, John || Junior Faculty
|-
| Saz, Oscar || Post-docs
|-
| Matlen, Bryab || Graduate Students
|-
| Ritter, Steve || Carnegie Learning
|}

== Advisory Board ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aronson, Joshua || New York University || Applied Psychology
|-
| Azevedo, Roger || McGill University || Educational and Counselling Psychology
|-
| Biswas, Gautam || Vanderbilt University || Computer Science and Computer Engineering
|-
| Collins, Allan || Northwestern University || Education and Social Policy
|-
| Feuer, Michael || George Washington University || Graduate School of Education and Human Development
|-
| Goldman, Susan || University of Illinois || Psychology
|-
| Goldstone, Rob || Indiana University || Psychology
|-
| Griffiths, Tom || Berkeley || Psychology
|-
| Isbell, Charles || Georgia Tech || School of Interactive Computing
|-
| Kamwangamalu, Nkonko || Howard University || English
|-
| Lesgold, Alan || University of Pittsburgh || School of Education
|-
| McNamara, Danielle || University of Memphis || Psychology
|-
| Li, Ping || Penn State University || Psychology
|-
| Smith, Marshall (Mike) S.|| ||
|}

== Graduate Students ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Adam Skory || Carnegie Mellon ||
|-
| Benjamin Friedline || University of Pittsburgh || Linguistics
|-
| Colleen Davy || Carnegie Mellon || Psychology
|-
| Garbiel Parent || Carnegie Mellon || Language Technologies Institute
|-
| (Derek) Ho Leung Chan || University of Pittsburgh || Linguistics
|-
| Leida Tolentino || University of Pittsburgh || Psychology
|-
| Nora Presson || Carnegie Mellon || Psychology
|-
| Ruth Wylie || Carnegie Mellon || Human Computer Interaction Institute
|-
| Susan Dunlap || University of Pittsburgh || Psychology
|-
| Yun (Helen) Zhao || Carnegie Mellon || Second Language Acquisition
|-
| Benjamin Shih || Carnegie Mellon ||
|-
| Collin Lynch || University of Pittsburgh ||
|-
| Erik Zawadzki || Carnegie Mellon ||
|-
| Nan Li || Carnegie Mellon ||
|-
| Amy Ogan || Carnegie Mellon ||
|-
| Dan Belenky || University of Pittsburgh || Psychology
|-
| Matthew Easterday || Carnegie Mellon ||
|-
| Soniya Gadgil || University of Pittsburgh || Psychology
|-
| Yanhui Zhang || Carnegie Mellon ||
|-
| Dejana Diziol || Freiburg ||
|-
| Elizabeth Ayers || Carnegie Mellon ||
|-
| Elsa Golden || Carnegie Mellon ||
|-
| April Galyardt || Carnegie Mellon || Statistics
|-
| Jamie Jirout || Carnegie Mellon || Psychology
|-
| Martina Rau || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tom Lauwers || Carnegie Mellon ||
|-
| Tracy Sweet || Carnegie Mellon || Statistics
|-
| Kevin Del Rosa || Carnegie Mellon ||
|-
| Turadg Aleahmad || Carnegie Mellon || Human Computer Interaction Institute
|-
| Gahgene Gweon || Carnegie Mellon ||
|-
| Anagha Kulkarni (Joshi) || Carnegie Mellon ||
|-
| Bryan Matlen || Carnegie Mellon || Psychology
|-
| Sung-Young Jung || University of Pittsburgh ||
|-
| Gustavo Santos || Carnegie Mellon ||
|-
| Hao-Chuan Wang || Carnegie Mellon ||
|-
| Indrayana Rustandi || Carnegie Mellon ||
|-
| Jessica Nelson || University of Pittsburgh || Psychology
|-
| Rohit Kumar || Carnegie Mellon ||
|-
| Roxana Gheorghiu || University of Pittsburgh ||
|-
| Tamar Degani || University of Pittsburgh || Psychology
|-
| Yan Mu || Carnegie Mellon || Psychology
|-
| Elijah Mayfield || Carnegie Mellon ||
|-
| Erin Walker || Carnegie Mellon ||
|-
| Iris Howley || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tracy Clark || Univeristy of Pennslyvania ||
|-
| Laurens Feestra || Netherlands ||
|-
| Maaike Waalkens || Netherlands ||
|-
| Mary Lou Vercellotti || University of Pittsburgh || Linguistics
|-
| Nozomi Tanaka || University of Pittsburgh || Linguistics
|-
| Eliane Stampfer || Carnegie Mellon || Human Computer Interaction Institute
|-
| Katherine Martin || University of Pittsburgh || Linguistics
|}

== Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Matthew Bernacki || University of Pittsburgh || LRDC
|-
| Gregory Dyke || Carnegie Mellon University || LTI
|-
| Sherice Clarke || University of Pittsburgh || LRDC
|-
| Oscar Saz || Carnegie Mellon University || LTI
|-
| Michael Yudelson || Carnegie Mellon University || HCII
|-
| Gaowei Chen || The University of Pittsbugh || LRDC
|-
| Catherine Chase || Carnegie Mellon University || Psychology
|-
| Amy Ogan || Carnegie Mellon University || HCII
|-
| Laura Halderman || Educational Testing Services ||
|-
| Seiji Isotani || The University of Sao Paulo ||
|-
| Min Chi || Stanford University ||
|-
| John Connelly || Carnegie Learning ||
|-
| Amy Crosson || University of Pittsburgh || LRDC
|-
| Ido Roll || University of British Columbia ||
|-
| Stephanie Siler || Carnegie Mellon || Psychology
|-
| Zelha Tunc-Pekkan || Carnegie Mellon || HCII
|-
| Fan Cao || University of Pittsburgh ||
|-
| Suzanne Adlof || University of Pittsburgh ||
|-
| Candace Walkington || University of Wisconson ||
|}

More information about the PSLC post-docs at the [[PSLC_Postdocs]] wiki page

== Former Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Hua Ai || Georgia Institute of Technology || LTI
|-
| Alicia Chang || University of Delaware || Postdoctoral Researcher
|-
| Connie Guan Qun || University of Pittsburgh ||
|-
| Chin-LungYang || University of Pittsburgh ||
|-
| Scotty Craig || University of Memphis|| Research Assistant Professor, Institute for Intelligent Systems
|-
|}

== Faculty ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Al Corbett || Carnegie Mellon || HCII
|-
| Alan Juffs || University of Pittsburgh || Linguistics
|-
| Brian Junker || Carnegie Mellon || Statisics
|-
| Brian MacWhinney || Carnegie Mellon || Psychology
|-
| Bruce McLaren || Carnegie Mellon || HCII
|-
| Carolyn Rosé || Carnegie Mellon || LTI/HCII
|-
| Charles Perfetti || University of Pittsburgh || LRDC
|-
| Christa Asterhan || Hebrew University ||
|-
| David Klahr || Carnegie Mellon || Psychology
|-
| David Yaron || Carnegie Mellon || Chemistry
|-
| Geoff Gordon || Carnegie Mellon || Machine Learning
|-
| Jack Mostow || Carnegie Mellon || Robotics
|-
| Jim Greeno || University of Pittsburgh || Instruction and Learning
|-
| John Stamper || Carnegie Mellon || HCII
|-
| Ken Koedinger || Carnegie Mellon || HCII
|-
| Kirsten Butcher || University of Utah || Instructional Design & Educational Technology
|-
| Kurt VanLehn || Arizona State University || Computer Science and Engineering
|-
| Lauren Resnick || University of Pittsburgh || LRDC
|-
| Louis Gomez || University of Pittsburgh || School of Education
|-
| Marsha Lovett || Carnegie Mellon || Eberly Center
|-
| Mary Catherine O'Connor || Boston University || School of Education
|-
| Matthew Kam || Carnegie Mellon || HCII
|-
| Maxine Eskenazi || Carnegie Mellon || LTI
|-
| Nel de Jong || Vrije Universiteit Amsterdam ||
|-
| Niels Pinkwart || Clausthal University of Technology ||
|-
| Nikol Rummel || Ruhr-Universität Bochum || Psychology
|-
| Noboru Matsuda || Carnegie Mellon || HCII
|-
| Phil Pavlik || Carnegie Mellon || HCII
|-
| Richard Scheines || Carnegie Mellon || Philosphy
|-
| Ryan Baker || WPI ||
|-
| Sandy Katz || University of Pittsburgh || LRDC
|-
| Sarah Michaels || Clark University || Education
|-
| Teruko Matamura || Carnegie Mellon || LTI
|-
| Tim Nokes || University of Pittsburgh ||
|-
| Vincent Aleven || Carnegie Mellon || LTI
|-
| William Cohen || Carnegie Mellon || ML
|-
| Ma. Mercedes T. Rodrigo || Ateneo de Manila University
|| Information Systems and Computer Science

|}

== Staff ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [[User:Alida|Alida Skogsholm]] || Carnegie Mellon University || DataShop Manager
|-
| Bob Hausmann || Carnegie Learning ||
|-
| Brett Leber || Carnegie Mellon University || DataShop/CTAT
|-
| Christy McGuire || Edalytics ||
|-
| Cressida Magaro || Carnegie Mellon University ||
|-
| Dorolyn Smith || University of Pittsburgh ||
|-
| Duncan Spencer || Carnegie Mellon University || DataShop
|-
| Gail Kusbit || Carnegie Mellon University || Research Manager
|-
| Jo Bodnar || Carnegie Mellon University ||
|-
| John Kowalski || Carnegie Mellon University ||
|-
| Jonathan Sewall || Carnegie Mellon University ||
|-
| Kevin Willows || Carnegie Mellon University ||
|-
| Mark Haney || University of Pittsburgh ||
|-
| Martin van Velsen || Carnegie Mellon University ||
|-
| Michael Bett || Carnegie Mellon University || Managing Director
|-
| Mike Karabinos|| Carnegie Mellon University ||
|-
| Ross Strader || Carnegie Mellon University ||
|-
| Sandy Demi || Carnegie Mellon University || DataShop/CTAT
|-
| Scott Silliman || University of Pittsburgh || OLI
|-
| Shanwen Yu || Carnegie Mellon University || DataShop
|-
| Steve Ritter || Carnegie Learning || Founder
|-
| Thomas Harris || Edalytics ||
|-
| Tristan Nixon || Carnegie Learning ||
|}

PSLC People

2012-02-17T18:22:58Z

Mbett: /* '''The Executive Committee''' */

== '''The Executive Committee''' ==

=== Directors ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [http://pact.cs.cmu.edu/koedinger.html '''Ken Koedinger'''] || Carnegie Mellon University || Human-Computer Interaction Institute
|-
| '''Charles Perfetti''' || University of Pittsburgh || Psychology, LRDC Director
|}

=== Managing Director ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Michael Bett''' || Carnegie Mellon University || Human-Computer Interaction Institute
|}

=== Members ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aleven, Vincent || Carnegie Mellon University || Human-Computer Interaction
|-
| Eskenazi, Maxine || Carnegie Mellon University || Language Technologies Institute
|-
| Fiez, Julie || University of Pittsburgh || Psychology
|-
| Gordon, Geoff || Carnegie Mellon University || Machine Learning
|-
| Klahr, David || Carnegie Mellon University || Psychology
|-
| Lovett, Marsha || Carnegie Mellon University || Psychology
|-
| Nokes, Tim || University of Pittsburgh || LRDC
|-
| Resnick, Lauren || University of Pittsburgh || Learning Research and Development Center
|-
| Rose, Carolyn || Carnegie Mellon University || Human-Computer Interaction Institute/Language Technologies Institute
|}

=== Liasons ===

{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Stamper, John || Junior Faculty
|-
| Saz, Oscar || Post-docs
|-
| Matlen, Bryab || Graduate Students
|-
| Ritter, Steve || Carnegie Learning
|}

== Advisory Board ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aronson, Joshua || New York University || Applied Psychology
|-
| Azevedo, Roger || University of Memphis || Psychology
|-
| Biswas, Gautam || Vanderbilt University || Computer Science and Computer Engineering
|-
| Collins, Allan || Northwestern University || Education and Social Policy
|-
| Feuer, Michael || George Washington University || Graduate School of Education and Human Development
|-
| Goldman, Susan || University of Illinois || Psychology
|-
| Goldstone, Rob || Indiana University || Psychology
|-
| Griffiths, Tom || Berkeley || Psychology
|-
| Isbell, Charles || Georgia Tech || School of Interactive Computing
|-
| Kamwangamalu, Nkonko || Howard University || English
|-
| Lesgold, Alan || University of Pittsburgh || School of Education
|-
| McNamara, Danielle || University of Memphis || Psychology
|-
| Li, Ping || Penn State University || Psychology
|-
| Smith, Marshall (Mike) S.|| ||
|}

== Graduate Students ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Adam Skory || Carnegie Mellon ||
|-
| Benjamin Friedline || University of Pittsburgh || Linguistics
|-
| Colleen Davy || Carnegie Mellon || Psychology
|-
| Garbiel Parent || Carnegie Mellon || Language Technologies Institute
|-
| (Derek) Ho Leung Chan || University of Pittsburgh || Linguistics
|-
| Leida Tolentino || University of Pittsburgh || Psychology
|-
| Nora Presson || Carnegie Mellon || Psychology
|-
| Ruth Wylie || Carnegie Mellon || Human Computer Interaction Institute
|-
| Susan Dunlap || University of Pittsburgh || Psychology
|-
| Yun (Helen) Zhao || Carnegie Mellon || Second Language Acquisition
|-
| Benjamin Shih || Carnegie Mellon ||
|-
| Collin Lynch || University of Pittsburgh ||
|-
| Erik Zawadzki || Carnegie Mellon ||
|-
| Nan Li || Carnegie Mellon ||
|-
| Amy Ogan || Carnegie Mellon ||
|-
| Dan Belenky || University of Pittsburgh || Psychology
|-
| Matthew Easterday || Carnegie Mellon ||
|-
| Soniya Gadgil || University of Pittsburgh || Psychology
|-
| Yanhui Zhang || Carnegie Mellon ||
|-
| Dejana Diziol || Freiburg ||
|-
| Elizabeth Ayers || Carnegie Mellon ||
|-
| Elsa Golden || Carnegie Mellon ||
|-
| April Galyardt || Carnegie Mellon || Statistics
|-
| Jamie Jirout || Carnegie Mellon || Psychology
|-
| Martina Rau || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tom Lauwers || Carnegie Mellon ||
|-
| Tracy Sweet || Carnegie Mellon || Statistics
|-
| Kevin Del Rosa || Carnegie Mellon ||
|-
| Turadg Aleahmad || Carnegie Mellon || Human Computer Interaction Institute
|-
| Gahgene Gweon || Carnegie Mellon ||
|-
| Anagha Kulkarni (Joshi) || Carnegie Mellon ||
|-
| Bryan Matlen || Carnegie Mellon || Psychology
|-
| Sung-Young Jung || University of Pittsburgh ||
|-
| Gustavo Santos || Carnegie Mellon ||
|-
| Hao-Chuan Wang || Carnegie Mellon ||
|-
| Indrayana Rustandi || Carnegie Mellon ||
|-
| Jessica Nelson || University of Pittsburgh || Psychology
|-
| Rohit Kumar || Carnegie Mellon ||
|-
| Roxana Gheorghiu || University of Pittsburgh ||
|-
| Tamar Degani || University of Pittsburgh || Psychology
|-
| Yan Mu || Carnegie Mellon || Psychology
|-
| Elijah Mayfield || Carnegie Mellon ||
|-
| Erin Walker || Carnegie Mellon ||
|-
| Iris Howley || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tracy Clark || Univeristy of Pennslyvania ||
|-
| Laurens Feestra || Netherlands ||
|-
| Maaike Waalkens || Netherlands ||
|-
| Mary Lou Vercellotti || University of Pittsburgh || Linguistics
|-
| Nozomi Tanaka || University of Pittsburgh || Linguistics
|-
| Eliane Stampfer || Carnegie Mellon || Human Computer Interaction Institute
|-
| Katherine Martin || University of Pittsburgh || Linguistics
|}

== Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Matthew Bernacki || University of Pittsburgh || LRDC
|-
| Gregory Dyke || Carnegie Mellon University || LTI
|-
| Sherice Clarke || University of Pittsburgh || LRDC
|-
| Oscar Saz || Carnegie Mellon University || LTI
|-
| Michael Yudelson || Carnegie Mellon University || HCII
|-
| Gaowei Chen || The University of Pittsbugh || LRDC
|-
| Catherine Chase || Carnegie Mellon University || Psychology
|-
| Amy Ogan || Carnegie Mellon University || HCII
|-
| Laura Halderman || Educational Testing Services ||
|-
| Seiji Isotani || The University of Sao Paulo ||
|-
| Min Chi || Stanford University ||
|-
| John Connelly || Carnegie Learning ||
|-
| Amy Crosson || University of Pittsburgh || LRDC
|-
| Ido Roll || University of British Columbia ||
|-
| Stephanie Siler || Carnegie Mellon || Psychology
|-
| Zelha Tunc-Pekkan || Carnegie Mellon || HCII
|-
| Fan Cao || University of Pittsburgh ||
|-
| Suzanne Adlof || University of Pittsburgh ||
|-
| Candace Walkington || University of Wisconson ||
|}

More information about the PSLC post-docs at the [[PSLC_Postdocs]] wiki page

== Former Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Hua Ai || Georgia Institute of Technology || LTI
|-
| Alicia Chang || University of Delaware || Postdoctoral Researcher
|-
| Connie Guan Qun || University of Pittsburgh ||
|-
| Chin-LungYang || University of Pittsburgh ||
|-
| Scotty Craig || University of Memphis|| Research Assistant Professor, Institute for Intelligent Systems
|-
|}

== Faculty ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Al Corbett || Carnegie Mellon || HCII
|-
| Alan Juffs || University of Pittsburgh || Linguistics
|-
| Brian Junker || Carnegie Mellon || Statisics
|-
| Brian MacWhinney || Carnegie Mellon || Psychology
|-
| Bruce McLaren || Carnegie Mellon || HCII
|-
| Carolyn Rosé || Carnegie Mellon || LTI/HCII
|-
| Charles Perfetti || University of Pittsburgh || LRDC
|-
| Christa Asterhan || Hebrew University ||
|-
| David Klahr || Carnegie Mellon || Psychology
|-
| David Yaron || Carnegie Mellon || Chemistry
|-
| Geoff Gordon || Carnegie Mellon || Machine Learning
|-
| Jack Mostow || Carnegie Mellon || Robotics
|-
| Jim Greeno || University of Pittsburgh || Instruction and Learning
|-
| John Stamper || Carnegie Mellon || HCII
|-
| Ken Koedinger || Carnegie Mellon || HCII
|-
| Kirsten Butcher || University of Utah || Instructional Design & Educational Technology
|-
| Kurt VanLehn || Arizona State University || Computer Science and Engineering
|-
| Lauren Resnick || University of Pittsburgh || LRDC
|-
| Louis Gomez || University of Pittsburgh || School of Education
|-
| Marsha Lovett || Carnegie Mellon || Eberly Center
|-
| Mary Catherine O'Connor || Boston University || School of Education
|-
| Matthew Kam || Carnegie Mellon || HCII
|-
| Maxine Eskenazi || Carnegie Mellon || LTI
|-
| Nel de Jong || Vrije Universiteit Amsterdam ||
|-
| Niels Pinkwart || Clausthal University of Technology ||
|-
| Nikol Rummel || Ruhr-Universität Bochum || Psychology
|-
| Noboru Matsuda || Carnegie Mellon || HCII
|-
| Phil Pavlik || Carnegie Mellon || HCII
|-
| Richard Scheines || Carnegie Mellon || Philosphy
|-
| Ryan Baker || WPI ||
|-
| Sandy Katz || University of Pittsburgh || LRDC
|-
| Sarah Michaels || Clark University || Education
|-
| Teruko Matamura || Carnegie Mellon || LTI
|-
| Tim Nokes || University of Pittsburgh ||
|-
| Vincent Aleven || Carnegie Mellon || LTI
|-
| William Cohen || Carnegie Mellon || ML
|-
| Ma. Mercedes T. Rodrigo || Ateneo de Manila University
|| Information Systems and Computer Science

|}

== Staff ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [[User:Alida|Alida Skogsholm]] || Carnegie Mellon University || DataShop Manager
|-
| Bob Hausmann || Carnegie Learning ||
|-
| Brett Leber || Carnegie Mellon University || DataShop/CTAT
|-
| Christy McGuire || Edalytics ||
|-
| Cressida Magaro || Carnegie Mellon University ||
|-
| Dorolyn Smith || University of Pittsburgh ||
|-
| Duncan Spencer || Carnegie Mellon University || DataShop
|-
| Gail Kusbit || Carnegie Mellon University || Research Manager
|-
| Jo Bodnar || Carnegie Mellon University ||
|-
| John Kowalski || Carnegie Mellon University ||
|-
| Jonathan Sewall || Carnegie Mellon University ||
|-
| Kevin Willows || Carnegie Mellon University ||
|-
| Mark Haney || University of Pittsburgh ||
|-
| Martin van Velsen || Carnegie Mellon University ||
|-
| Michael Bett || Carnegie Mellon University || Managing Director
|-
| Mike Karabinos|| Carnegie Mellon University ||
|-
| Ross Strader || Carnegie Mellon University ||
|-
| Sandy Demi || Carnegie Mellon University || DataShop/CTAT
|-
| Scott Silliman || University of Pittsburgh || OLI
|-
| Shanwen Yu || Carnegie Mellon University || DataShop
|-
| Steve Ritter || Carnegie Learning || Founder
|-
| Thomas Harris || Edalytics ||
|-
| Tristan Nixon || Carnegie Learning ||
|}

PSLC People

2012-01-23T20:11:28Z

Mbett: /* Advisory Board */

== '''The Executive Committee''' ==

=== Directors ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [http://pact.cs.cmu.edu/koedinger.html '''Ken Koedinger'''] || Carnegie Mellon University || Human-Computer Interaction Institute
|-
| '''Charles Perfetti''' || University of Pittsburgh || Psychology, LRDC Director
|}

=== Managing Director ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Michael Bett''' || Carnegie Mellon University || Human-Computer Interaction Institute
|}

=== Members ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aleven, Vincent || Carnegie Mellon University || Human-Computer Interaction
|-
| Eskenazi, Maxine || Carnegie Mellon University || Language Technologies Institute
|-
| Fiez, Julie || University of Pittsburgh || Psychology
|-
| Gordon, Geoff || Carnegie Mellon University || Machine Learning
|-
| Klahr, David || Carnegie Mellon University || Psychology
|-
| Lovett, Marsha || Carnegie Mellon University || Psychology
|-
| Nokes, Tim || University of Pittsburgh || LRDC
|-
| Resnick, Lauren || University of Pittsburgh || Learning Research and Development Center
|-
| Rose, Carolyn || Carnegie Mellon University || Human-Computer Interaction Institute/Language Technologies Institute
|}

== Advisory Board ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aronson, Joshua || New York University || Applied Psychology
|-
| Azevedo, Roger || University of Memphis || Psychology
|-
| Biswas, Gautam || Vanderbilt University || Computer Science and Computer Engineering
|-
| Collins, Allan || Northwestern University || Education and Social Policy
|-
| Feuer, Michael || George Washington University || Graduate School of Education and Human Development
|-
| Goldman, Susan || University of Illinois || Psychology
|-
| Goldstone, Rob || Indiana University || Psychology
|-
| Griffiths, Tom || Berkeley || Psychology
|-
| Isbell, Charles || Georgia Tech || School of Interactive Computing
|-
| Kamwangamalu, Nkonko || Howard University || English
|-
| Lesgold, Alan || University of Pittsburgh || School of Education
|-
| McNamara, Danielle || University of Memphis || Psychology
|-
| Li, Ping || Penn State University || Psychology
|-
| Smith, Marshall (Mike) S.|| ||
|}

== Graduate Students ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Adam Skory || Carnegie Mellon ||
|-
| Benjamin Friedline || University of Pittsburgh || Linguistics
|-
| Colleen Davy || Carnegie Mellon || Psychology
|-
| Garbiel Parent || Carnegie Mellon || Language Technologies Institute
|-
| (Derek) Ho Leung Chan || University of Pittsburgh || Linguistics
|-
| Leida Tolentino || University of Pittsburgh || Psychology
|-
| Nora Presson || Carnegie Mellon || Psychology
|-
| Ruth Wylie || Carnegie Mellon || Human Computer Interaction Institute
|-
| Susan Dunlap || University of Pittsburgh || Psychology
|-
| Yun (Helen) Zhao || Carnegie Mellon || Second Language Acquisition
|-
| Benjamin Shih || Carnegie Mellon ||
|-
| Collin Lynch || University of Pittsburgh ||
|-
| Erik Zawadzki || Carnegie Mellon ||
|-
| Nan Li || Carnegie Mellon ||
|-
| Amy Ogan || Carnegie Mellon ||
|-
| Dan Belenky || University of Pittsburgh || Psychology
|-
| Matthew Easterday || Carnegie Mellon ||
|-
| Soniya Gadgil || University of Pittsburgh || Psychology
|-
| Yanhui Zhang || Carnegie Mellon ||
|-
| Dejana Diziol || Freiburg ||
|-
| Elizabeth Ayers || Carnegie Mellon ||
|-
| Elsa Golden || Carnegie Mellon ||
|-
| April Galyardt || Carnegie Mellon || Statistics
|-
| Jamie Jirout || Carnegie Mellon || Psychology
|-
| Martina Rau || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tom Lauwers || Carnegie Mellon ||
|-
| Tracy Sweet || Carnegie Mellon || Statistics
|-
| Kevin Del Rosa || Carnegie Mellon ||
|-
| Turadg Aleahmad || Carnegie Mellon || Human Computer Interaction Institute
|-
| Gahgene Gweon || Carnegie Mellon ||
|-
| Anagha Kulkarni (Joshi) || Carnegie Mellon ||
|-
| Bryan Matlen || Carnegie Mellon || Psychology
|-
| Sung-Young Jung || University of Pittsburgh ||
|-
| Gustavo Santos || Carnegie Mellon ||
|-
| Hao-Chuan Wang || Carnegie Mellon ||
|-
| Indrayana Rustandi || Carnegie Mellon ||
|-
| Jessica Nelson || University of Pittsburgh || Psychology
|-
| Rohit Kumar || Carnegie Mellon ||
|-
| Roxana Gheorghiu || University of Pittsburgh ||
|-
| Tamar Degani || University of Pittsburgh || Psychology
|-
| Yan Mu || Carnegie Mellon || Psychology
|-
| Elijah Mayfield || Carnegie Mellon ||
|-
| Erin Walker || Carnegie Mellon ||
|-
| Iris Howley || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tracy Clark || Univeristy of Pennslyvania ||
|-
| Laurens Feestra || Netherlands ||
|-
| Maaike Waalkens || Netherlands ||
|-
| Mary Lou Vercellotti || University of Pittsburgh || Linguistics
|-
| Nozomi Tanaka || University of Pittsburgh || Linguistics
|-
| Eliane Stampfer || Carnegie Mellon || Human Computer Interaction Institute
|-
| Katherine Martin || University of Pittsburgh || Linguistics
|}

== Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Matthew Bernacki || University of Pittsburgh || LRDC
|-
| Gregory Dyke || Carnegie Mellon University || LTI
|-
| Sherice Clarke || University of Pittsburgh || LRDC
|-
| Oscar Saz || Carnegie Mellon University || LTI
|-
| Michael Yudelson || Carnegie Mellon University || HCII
|-
| Gaowei Chen || The University of Pittsbugh || LRDC
|-
| Catherine Chase || Carnegie Mellon University || Psychology
|-
| Amy Ogan || Carnegie Mellon University || HCII
|-
| Laura Halderman || Educational Testing Services ||
|-
| Seiji Isotani || The University of Sao Paulo ||
|-
| Min Chi || Stanford University ||
|-
| John Connelly || Carnegie Learning ||
|-
| Amy Crosson || University of Pittsburgh || LRDC
|-
| Ido Roll || University of British Columbia ||
|-
| Stephanie Siler || Carnegie Mellon || Psychology
|-
| Zelha Tunc-Pekkan || Carnegie Mellon || HCII
|-
| Fan Cao || University of Pittsburgh ||
|-
| Suzanne Adlof || University of Pittsburgh ||
|-
| Candace Walkington || University of Wisconson ||
|}

More information about the PSLC post-docs at the [[PSLC_Postdocs]] wiki page

== Former Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Hua Ai || Georgia Institute of Technology || LTI
|-
| Alicia Chang || University of Delaware || Postdoctoral Researcher
|-
| Connie Guan Qun || University of Pittsburgh ||
|-
| Chin-LungYang || University of Pittsburgh ||
|-
| Scotty Craig || University of Memphis|| Research Assistant Professor, Institute for Intelligent Systems
|-
|}

== Faculty ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Al Corbett || Carnegie Mellon || HCII
|-
| Alan Juffs || University of Pittsburgh || Linguistics
|-
| Brian Junker || Carnegie Mellon || Statisics
|-
| Brian MacWhinney || Carnegie Mellon || Psychology
|-
| Bruce McLaren || Carnegie Mellon || HCII
|-
| Carolyn Rosé || Carnegie Mellon || LTI/HCII
|-
| Charles Perfetti || University of Pittsburgh || LRDC
|-
| Christa Asterhan || Hebrew University ||
|-
| David Klahr || Carnegie Mellon || Psychology
|-
| David Yaron || Carnegie Mellon || Chemistry
|-
| Geoff Gordon || Carnegie Mellon || Machine Learning
|-
| Jack Mostow || Carnegie Mellon || Robotics
|-
| Jim Greeno || University of Pittsburgh || Instruction and Learning
|-
| John Stamper || Carnegie Mellon || HCII
|-
| Ken Koedinger || Carnegie Mellon || HCII
|-
| Kirsten Butcher || University of Utah || Instructional Design & Educational Technology
|-
| Kurt VanLehn || Arizona State University || Computer Science and Engineering
|-
| Lauren Resnick || University of Pittsburgh || LRDC
|-
| Louis Gomez || University of Pittsburgh || School of Education
|-
| Marsha Lovett || Carnegie Mellon || Eberly Center
|-
| Mary Catherine O'Connor || Boston University || School of Education
|-
| Matthew Kam || Carnegie Mellon || HCII
|-
| Maxine Eskenazi || Carnegie Mellon || LTI
|-
| Nel de Jong || Vrije Universiteit Amsterdam ||
|-
| Niels Pinkwart || Clausthal University of Technology ||
|-
| Nikol Rummel || Ruhr-Universität Bochum || Psychology
|-
| Noboru Matsuda || Carnegie Mellon || HCII
|-
| Phil Pavlik || Carnegie Mellon || HCII
|-
| Richard Scheines || Carnegie Mellon || Philosphy
|-
| Ryan Baker || WPI ||
|-
| Sandy Katz || University of Pittsburgh || LRDC
|-
| Sarah Michaels || Clark University || Education
|-
| Teruko Matamura || Carnegie Mellon || LTI
|-
| Tim Nokes || University of Pittsburgh ||
|-
| Vincent Aleven || Carnegie Mellon || LTI
|-
| William Cohen || Carnegie Mellon || ML
|-
| Ma. Mercedes T. Rodrigo || Ateneo de Manila University
|| Information Systems and Computer Science

|}

== Staff ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [[User:Alida|Alida Skogsholm]] || Carnegie Mellon University || DataShop Manager
|-
| Bob Hausmann || Carnegie Learning ||
|-
| Brett Leber || Carnegie Mellon University || DataShop/CTAT
|-
| Christy McGuire || Edalytics ||
|-
| Cressida Magaro || Carnegie Mellon University ||
|-
| Dorolyn Smith || University of Pittsburgh ||
|-
| Duncan Spencer || Carnegie Mellon University || DataShop
|-
| Gail Kusbit || Carnegie Mellon University || Research Manager
|-
| Jo Bodnar || Carnegie Mellon University ||
|-
| John Kowalski || Carnegie Mellon University ||
|-
| Jonathan Sewall || Carnegie Mellon University ||
|-
| Kevin Willows || Carnegie Mellon University ||
|-
| Mark Haney || University of Pittsburgh ||
|-
| Martin van Velsen || Carnegie Mellon University ||
|-
| Michael Bett || Carnegie Mellon University || Managing Director
|-
| Mike Karabinos|| Carnegie Mellon University ||
|-
| Ross Strader || Carnegie Mellon University ||
|-
| Sandy Demi || Carnegie Mellon University || DataShop/CTAT
|-
| Scott Silliman || University of Pittsburgh || OLI
|-
| Shanwen Yu || Carnegie Mellon University || DataShop
|-
| Steve Ritter || Carnegie Learning || Founder
|-
| Thomas Harris || Edalytics ||
|-
| Tristan Nixon || Carnegie Learning ||
|}

Nokes - Dialectical Interaction and Robust Learning

2012-01-11T16:03:02Z

Mbett:

Dialectical Interaction and Robust Learning
==Summary Table==

{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| '''PIs''' || Timothy Nokes, John Levine
|-
| '''Other Contributers''' || Daniel Belenky, Soniya Gadgil
|-
| '''Study Start Date''' ||
|-
| '''Study End Date''' ||
|-
| '''Site''' || University of Pittsburgh
|-
| '''Number of Students''' ||
|-
| '''Total Participant Hours''' ||
|-
| '''DataShop''' ||
|}
 

==Abstract==
This work builds on prior research investigating the relationship between cognitive conflict
and learning (e.g., Doise & Mugny, 1984), the links between motivation, affect, and cognition (e.g.,
Forgas, 2001; Schwarz & Clore, 2007), and the mechanisms underlying conceptual learning (e.g., Chi
& Ohlsson, 2005; Nokes & Ross, 2007). Although much prior work has investigated each of these
areas separately, few studies have tried to build connections across all three. We hypothesize that
conflict scenarios that increase engagement, arousal, and positive affect will facilitate participants’
deep processing of discourse through a variety of cognitive mechanisms including inference
generation, elaboration, analogy, and the framing and re-framing of the information discussed.
Participants in such scenarios are expected to develop more complex and coherent knowledge of the
issue and to learn both their own and their opponent’s side of the issue. In contrast, conflict scenarios
that decrease engagement, arousal, and induce negative affect should lead to less robust learning.
Participants in these scenarios are expected to focus on their own side of the debate, ignoring their
opponent’s view, and to engage in shallow cognitive processing strategies such as rehearsal of their
own argument.
==Background & Significance==
==Glossary==
==Research questions==
How do students learn when engaged in a debate? Do they integrate their own viewpoint with that of their opponent, or focus only on their own side? Does the format of the debate affect this? Also, what motivational and affective factors play into this? How do student goals (like performance or mastery goals) influence what information gets processed? Do different affective experiences lead to different patterns of learning?
==Independent Variables==
Our first study has a 2 x 2 design. Factor 1: Debate Format - Alternating Turns (1 minute each) or Free-Form Factor 2: Debate Criterion - Substance or Rhetoric

==Dependent Variables==
Our dependent variables will consist of various measures of learning, gathered after the debate. These measures include a multiple-choice test, as well as an essay. Both of these will be evaluated in terms of how well a student has learned his side of the debate, as well as how well he has learned the other side, and how well he has integrated the two.

We will also gather measures of affect during the debate. This will be used as a dependent variable to see if our manipulations relating to the debate structure influence the affective reactions experienced. We will also be able to affective response as a predictor of learning, or as mediating variable between debate and learning. These affective measures will come from analysis of the vocal parameters of speech of each participant, as well as by analysis of facial cues.
==Hypothesis==
We predict that focusing the debate on the substance of the arguments will produce a more coherent representation of both sides of the argument. We also predict that the free-form debate will lead to better learning of both sides, as the participants must be engaging with what a participant is saying more actively, and respond more immediately and thoroughly than when they have a minute between speaking turns.

In terms of affect, we expect that positive affective reactions to cognitive conflict will produce systematic processing of an opponent’s arguments, which in turn will facilitate learning these arguments and developing a more complex cognitive representation of the discussion topic. In contrast, negative affective reactions will produce superficial processing of the opponent’s arguments coupled with rehearsal of one’s own arguments. When negative affect is mild, interactants are unlikely to learn the opponent’s arguments or to develop a complex representation of the topic. Moreover, when negative affect is strong, interactants may actually show cognitive regression -- less complex representations of the topic after interaction than before.

==Results==
Forthcoming
==Explanation==
Forthcoming
===Future Plans===
===References===

Publications

2011-12-01T13:57:32Z

Mbett:

== Books ==

Chi, Michelene. Talk, Dialogue and Learning. Hillsdale, NJ: Lawrence Erlbaum Associates. in preparation.

Smith, Dorolyn; Brown, Steven. Active Listening, Second edition, Levels 1, 2 and 3. A listening comprehension textbook series with CD, for beginning to intermediate students of ESL. Cambridge University Press. 2007.

== Edited Books ==

Klatzky, Roberta; MacWhinney, Brian; Behrmann, Marlene. Embodiment, ego-space, and action. R. Klatzky, B. MacWhinney, Brian, & M. Behrmann, (Eds). Carnegie Mellon Symposia on Cognition. Psychology Press: Taylor & Francis Group. 2008.

Romero, Cristobal; Ventura, Sebastian; Viola, Silvia Rita; Pechenizkiy, Mykola; Baker, Ryan. Handbook of Educational Data Mining. Virginia Beach, VA: Chapman & Hall/CRC. in press.

Schmalhofer, Franz; Perfetti, Charles. Higher level language processes in the brain: Inference and comprehension processes. Routledge: Psychology Press. 2007.

== Book Chapters ==

Aleven, Vincent; McLaren, Bruce; Koedinger, Kenneth. Towards computer-based tutoring of help-seeking skills. S. Karabenick and R. Newman, (Eds.), Help seeking in academic settings: Goals, groups and contexts. Mahwah NJ: Erlbaum, 259-296. 2006.

Asterhan, Christa & Schwarz, Baruch. Transformation of robust misconceptions through peer argumentation. In: B. B. Schwarz, T. Dreyfus, & R. Hershkowitz (Eds.) Guided Transformation of Knowledge in Classrooms (159-172). New York, NY: Routledge, Advances in Learning & Instruction series. 2009

Baker, Ryan. Discovery with Models (Backer). C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC. in press.

Baker, Ryan. Data Mining for Education. To appear in McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier. in press.

Chen, Zhe; Klahr, David. Remote Transfer of Scientific Reasoning and Problem-Solving Strategies in Children. R. V. Kail (Ed.) Advances in Child Development and Behavior, Vol. 36. (pp. 419 – 470) Amsterdam: Elsevier. 2008.

Chenoweth, N. Ann; Jones, Christopher; Tucker, G. Richard. Language online: Principles of design and methods of assessment. R. P. Donaldson & M. A. Haggstrom (Eds.), Changing Language Education through CALL. New York, NY: Routledge, 147—167. 2006.

Chi, Michelene. Laboratory Methods for Assessing Experts’ and Novices’ Knowledge. (2006). N. Charness, P. Feltovich, & R. Hoffman (Eds.), Cambridge Handbook of Expertise and Expert Performance. Cambridge University Press. p 167-184. 2006.

Chi, Michelene. Two approaches to the study of experts’ characteristics. (2006). N. Charness, P. Feltovich, & R. Hoffman (Eds.), Cambridge Handbook of Expertise and Expert Performance. Cambridge University Press. p 21-30. 2006.

Chi, Michelene. Three types of conceptual change: Belief revision, mental model transformation, and categorical shift. S. Vosniadou (Ed.), Handbook of research on conceptual change. Hillsdale, NJ: Erlbaum, 61-82. 2008.

Chi, Michelene; Ohlsson, Stellan. Complex declarative learning. In:Holyoak, K.J. & Morrison, R.G. (Eds.) The Cambridge Handbook of Thinking and Reasoning (Pp. 371-399). Cambridge University Press. 2005.

Eskenazi, Maxine; Brown, Jonathan. Teaching the creation of software that uses speech recognition. P. Hubbard and M. Levy, (Eds.), Teacher Education in CALL. John Benjamins Publishing, 135-151. 2006.

Forbes-Riley, Kate; Litman, Diane. Analyzing Dependencies Between Student Certainness States and Tutor Responses in a Spoken Dialogue Corpus. L. Dybkjaer and W. Minker (Eds.), Text, Speech and Language Technology: Recent Trends in Discourse and Dialogue, Vol. 39, 275-304. Springer Netherlands. 2008.

Frishkoff, Gwen; White, Gregory; Perfetti, Charles. "In vivo" testing of learning and instructional principles: The design and implementation of school-based experimentation. L. Dinella (Ed.), Conducting Science-Based Psychology Research in Schools. Washington, D.C.: APA Books. 2009.

Glennan, Thomas; Resnick, Lauren. "School Districts as Learning Organizations: A Strategy for Scaling Education Reform. " T.K. Glennan, Jr., S.J. Bodilly, J. Galegher, and K. Kerr, (Eds.) Expanding the Reach of Education Reforms: Collected Essays by Leaders in the Scale-up of Educational Interventions. Santa Monica, CA: RAND, MG-177-FF. p517-. 2004.

Juffs, Alan. Second language acquisition of the lexicon. W. Ritchie and T. Bhatia, (Eds.), The New handbook of second language acquisition, 2nd edition. Amsterdam, The Netherlands: Elsevier. 2009.

Junker, Brian. The role of psychometric methods in EDM. C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC. in press.

Klahr, David. Evolution of Scientific Thinking: Comments on Geary’s “Educating the Evolved Mind” In Carlson, J. & Levin, J. (Eds.) Psychological Perspectives on Contemporary Educational Issues. Greenwich, CT. Information Age Publishing. 2007.

Koedinger, Kenneth; Aleven, Vincent; Roll, Ido; Baker, Ryan. In vivo experiments on whether supporting metacognition in intelligent tutoring systems yields robust learning. To appear in Graesser, A., Hacker, D. (Eds.), Handbook of Metacognition in Education. Routledge. in press.

Koedinger, K.R., Baker, R.S.J.d., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J. (in press) A Data Repository for the EDM commuity: The PSLC DataShop. To appear in Romero, C., Ventura, S.,Pechenizkiy, M., Baker, R.S.J.d. (Eds.) Handbook of Educational Data Mining. Boca Raton, FL: CRC Press.

Koedinger, Kenneth; Corbett, Albert. Cognitive Tutors: Technology bringing learning science to the classroom. K. Sawyer (Ed.) The Cambridge Handbook of the Learning Sciences, (pp. 61-78). Cambridge University Press. 2006.

Koedinger, Kenneth; McLaren, Bruce. Data Sharing and Data Repositories for EDM. C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC. in press.

MacWhinney, Brian. A Unified Model of Language Acquisition. Handbook of bilingualism: Psycholinguistic approaches. 2004. p 49-67. 2004.

MacWhinney, Brian. Emergent Fossilization. Studies of Fossilization in Second Language Acquisition. Z. Han and T. Odlin (Eds.). Clevedon, UK: Multilingual Matters. 2005. p 134-156. 2005.

MacWhinney, Brian. How Mental Models Encode Embodied Linguistic Perspectives. Klatzky, R., MacWhinney, Brian, B., and Behrmann, M. (Eds.). Embodiment, Ego-Space, and Action, 365-405. Carnegie Mellon Symposia on Cognition. Psychology Press: Taylor & Francis Group. 2008.

Masnick, Amy; Klahr, David; Morris, Bradley. Separating signal from noise: Children's understanding of error and variability in experimental outcomes. M. Lovett & P. Shaw, P. (Eds) Thinking With Data. Mawah, NJ: Erlbaum. 2007.

Mostow, Jack. Project LISTEN's session browser for exploring data logged by the Reading Tutor. C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC. in press.

Nokes, Timothy; Schunn, Christian; Chi, Michelene. Problem solving and human expertise. International Encyclopedia of Education, 3rd Edition. Oxford, UK: Elsevier. in press.

Pavlik, Phillip. Timing is in order: Modeling order effects in the learning of information. F. E. Ritter, J. Nerb, E. Lehtinen & T. O'Shea (Eds.), order to learn: How order effects in machine learning illuminate human learning. New York: Oxford University Press. 2007.

Perfetti, Charles; Dunlap, Susan. Learning to read: General principles and writing system variations. K. Koda & A. Zehler (Eds.). Learning to read across languages (13-38). Mahwah, NJ: Erlbaum. 2008.

Perfetti, Charles; Frishkoff, Gwen. Neural bases of text and discourse processing. B. Stemmer and H.A. Whitaker (Eds.), Handbook of neuroscience of language (pp. 165-174). Cambridge, MA: Elsevier. 2008.

Perfetti, Charles; Landi, Nicole; Oakhill, Jane. The acquisition of reading comprehension skill. M. J. Snowling & C. Hulme (Eds.), The science of reading: A handbook (pp. 227-247). Oxford: Blackwell. 2005.

Perfetti, Charles; Liu, Ying. Reading Chinese characters: Orthography, phonology, meaning, and the Lexical Constituency Model. P. Li, L. H. Tan, E. Bates, & O. J. L. Tzeng (Eds.), Handbook of East Asian psycholinguistics (pp. 225-236). New York: Cambridge University Press. P 225-236. 2006.

Perfetti, Charles; Liu, Ying; Fiez, Julie; Tan, Li Hai. The neural bases of reading: The accommodation of the brain’s reading network to writing systems. P. Cornelissen, M. Kringelbach, & P. Hansen (Eds.), The neural basis of reading. Oxford University Press. in press.

Razzaq, Leena; Feng, Mingyu; Heffernan, Neil; Koedinger, Kenneth; Junker, Brian; Nuzzo-Jones, Goss; Macasek, Michael; Rasmussen, Kai; Turner, Terrence; Walonoski, Jason. A Web-based authoring tool for intelligent tutors: Assessment and instructional assistance. N. Nedjah, et al. (Eds.) Intelligent Educational Machines. Intelligent Systems Engineering Book Series. Springer, 23-49. 2007.

Reed, Steven. Manipulating multimedia materials. Robert Zheng (Ed), Cognitive Effects of Multimedia Learning (51-66). Hershey, PA: IGI Global, Inc. 2008.

Renkl, Alexander; Atkinson, Robert. Cognitive skill acquisition: Ordering instructional events in example-based learning. F. E. Ritter, J. Nerb, E. Lehtinen, and T. O’Shea (Eds.), order to learn: How ordering effect in machine learning illuminate human learning and vice versa. Oxford, UK: Oxford University Press. 2007.

Renkl, Alexander; Hilbert, Tatjana; Schworm, Silke; Reiss, Kristina. Cognitive skill acquisition from complex examples: A Taxonomy of examples and tentative instructional guidelines. M. Prenzel (Ed.), Studies on the educational quality of schools, 239-249. Münster, Germany: Waxmann. 2007.

Resnick, Lauren. Giving Psychology Away: From Individual Learning to Learning Organizations. Jing, Q. (Ed.), Progress in Psychological Science around the World, 28th International Congress of Psychology, Vol. 2, Social and Applied Issues. ISBN: 1841699624 . 2007.

Resnick, Lauren; Lesgold Alan; Hall, Megan. Technology and the new culture of learning: Tools for education professionals. P. Gardenfors & P. Johansson (Eds.), Cognition, education, and communication technology (pp. 77-107). Mahwah, NJ: Erlbaum. 2005.

Resnick, Lauren; Michaels, Sarah; O'Connor, Catherine. How (well structured) talk builds the mind. R. Sternberg & D. Preiss (Eds.), From Genes to Context: New Discoveries about Learning from Educational Research and Their Applications. New York: Springer. in press.

Resnick, Lauren; Spillane, James. From individual learning to organizational designs for learning. L. Verschaffel, F. Dochy, M. Boekaerts, & S. Vosniadou, (Eds). Instructional psychology: Past, present and future trends. Sixteen essays in honor of Erik De Corte (Advances in Learning and Instruction Series). Oxford: Pergamon. 2006.

Ritter, Steven; Haverty, Lisa; Koedinger, Kenneth; Hadley, William; Corbett, Albert. Integrating intelligent software tutors with the math classroom. G. Blume and K. Heid (Eds.), Research on Technology and the Teaching and Learning of Mathematics: Vol. 2 Cases and Perspectives. Charlotte, NC: IAP. . 2008.

Ritter, Steven; Kulikowich, Jonna; Lei, Pui-Wa; McGuire, Christy; Morgan, Pat. What evidence matters? A randomized field trial of Cognitive Tutor Algebra I. . 2007.

Romero, Cristobal; Ventura, Sebastian; Viola, Silvia Rita; Pechenizkiy, Mykola; Baker, Ryan. Introduction to EDM. C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC. in press.

Romero, Cristobal; Ventura, Sebastian; Viola, Silvia Rita; Pechenizkiy, Mykola; Baker, Ryan. Conclusions and future trends. C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC. in press.

Roy, Marguerite; Chi, Michelene. The self-explanation principle in multi-media learning. R. Mayer (Ed.), Cambridge Handbook of Multimedia Learning (Pp. 271-286). Cambridge Press. p 271-286. 2005.

Schwarz, Baruch & Asterhan, Christa. Argumentation and Reasoning. To appear in: K. Littleton, C. Wood, & J. Kleine Staarman (Eds). International Handbook of Psychology in Education. Bingley, UK: Emerald Group Publishing. 2010

Shih, Benjamin . A Response time model for bottom-out hints as worked examples. C Romero, S. Ventura, M. Viola, R. Pechnizkiy, & R. Baker, (Eds.). Handbook of Educational Data Mining. Virginia Beach, VA; Chapman & Hall/CRC. in press.

Singh, Ajit; Gordon, Geoffrey. A unified view of matrix factorization models. R. Goebel, J. Siekmann, and W. Wahlster (Eds). Machine Learning and Knowledge Discovery in Databases (Proc. ECML PKDD), volume 5212/2008 of Lecture Notes in Computer Science, pages 358-373. Springer Berlin / Heidelberg, 2008. 2008.

Tokowicz, Natasha; Perfetti, Charles. Introduction to section II: Comprehension. J. F. Kroll & A. M. B. de Groot (Eds.), Handbook of bilingualism: Psycholinguistic approaches (pp. 173-177). New York: Oxford University Press. p 173-178. 2005.

Tucker, Richard. Learning other languages: The case for promoting bilinguality within our educational system. D. Brinton & O. Kagan (Eds.) Heritage language: A new field emerging. Mahwah, NJ: Lawrence Erlbaum. 2004.

VanLehn, Kurt. Getting out of order: Avoiding lesson effects through instruction. F. E. Ritter, J. Nerb, T. O'Shea, & E. Lehtinen (Eds.), order to learn: How the sequences of topics affect learning. Oxford University Press. 2007.

VanLehn, Kurt. Intelligent tutoring systems for continuous, embedded assessment. C. A. Dwyer (Ed.), The future of assessment: Shaping teaching and learning. Mahwah, NJ: Erbaum. 2008.

VanLehn, Kurt; van de Sande, Brett. Acquiring Conceptual Expertise from Modeling: The Case of Elementary Physics. K. A. Ericsson (Ed.) The Development of Professional Performance: Approaches to Objective Measurement and Design of Optimal Learning Environments. 2009.

White, Gregory; Frishkoff, Gwen; Bullock, Merry. Bridging the gap between psychological science and educational policy and practice. K. T. C. Fiorello. (Ed.), Cognitive development in K-3 classroom learning: Research applications (227-263). Mahwah, NJ: Lawrence Erlbaum Associates. 2008.

== Journal Articles ==

Aleven, Vincent; McLaren, Bruce; Roll, Ido; Koedinger, Kenneth. Toward meta-cognitive tutoring: A Model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence in Education, 16, 101-128. 2006.

Aleven, Vincent; McLaren, Bruce; Sewall, Jonathan. Applying Programming by Demonstration to Large-Scale Intelligent Tutoring Systems Development: An Open-Access Website for Middle-School Math Learning. Accepted by IEEE Transactions on Learning Technologies, Special Issue on "Real-World Applications of Intelligent Tutoring Systems.". in press.

Aleven, Vincent; McLaren, Bruce; Sewall, Jonathan; Koedinger, Kenneth. Example-Tracing Tutors: A New Paradigm for Intelligent Tutoring Systems. International Journal of Artificial Intelligence in Education (IJAIED). Special Issue on "Authoring Systems for Intelligent Tutoring Systems.". in press.

Asterhan, Christa & Schwarz, Baruch. The effects of monological and dialogical argumentation on concept learning in evolutionary theory. Journal of Educational Psychology, 99, 626-639. 2007

Asterhan, Christa & Schwarz, Baruch. The role of argumentation and explanation in conceptual change: Indications from protocol analyses of peer-to-peer dialogue. Cognitive Science, 33, 373-399. 2009

Asterhan, Christa & Schwarz, Baruch. Online human guidance of small group discussions: The case of synchronous e-argumentation in a diagram-based discussion space. International Journal of Computer-Supported Collaborative Learning. in press

Baker, Ryan; Corbett, Albert; Roll, Ido; Koedinger, Kenneth. Developing a Generalizable Detector of When Students Game the System. User Modeling and User-Adapted Interaction, 18(3), 287-314. 2008.

Baker, Ryan; Walonoski, Jason; Heffernan, Neil; Roll,Ido; Corbett,Albert; Koedinger, Kenneth. Why Students Engage in "Gaming the System" Behavior in Interactive Learning Environments. Journal of Interactive Learning Research, 19(2), 185-224. 2008.

Ben-Yehudah, Gal; Guediche, Sara; Fiez, Julie. Cerebellar contributions to verbal working memory: Beyond cognitive theory. The Cerebellum, 63:193-201. 2007.

Blessing, Stephen; Gilbert, Stephen; Oureda, Steven; Ritter, Steven. Authoring model-tracing cognitive tutors. International Journal of AI in Education. in press.

Bolger, Donald; Balass, Michal; Landen, Eve; Perfetti, Charles. Contextual variation and definitions in learning the meaning of words. Discourse Processes, 45(2), 122-159. 2008.

Bolger, Donald; Perfetti, Charles; Schneider, Walter. A cross-cultural effect on the brain revisited: Universal structures plus writing system variation. Human Brain Mapping, Vol 25(1), 92-104. 2005.

Booth, Julie; Siegler, Robert. Numerical magnitude representations influence arithmetic learning. Child Development, 79, 1016-1031. 2008.

Butcher, Kirsten. Learning From Text With Diagrams: Promoting Mental Model Development and Inference Generation. Journal of Educational Psychology, 98(1), 182-197. 2006.

Chen, Bao Guo; Zhou, Hui Xia; Dunlap, Susan; Perfetti, Charles. Age of acquisition effects in reading Chinese: Evidence in favour of the arbitrary mapping hypoThesis. British Journal of Psychololgy, Vol 98(3): 499-516. 2007.

Chi, Michelene; Roy, Marguerite; Hausmann, Robert. Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive Science, 32, 301-341. 2008.

Chi, Michelene; Siler, Stephanie; Heisawn, Jeong. Can Tutors Monitor Students’ Understanding Accurately?. Cognition and Instruction. Vol 22, No 3. Pages 363-387. 2004.

Collins-Thompson, Kevyn; Callan, Jamie. Predicting reading difficulty with statistical reading models. Journal of the American Society for Information Science and Technology, 56(13) (pp. 1448-1462). 2005. 2005.

Connelly, John; Katz, Sandra. Intelligent dialogue support for physics problem solving: Some preliminary mixed results. Technology, Instruction, Cognition, and Learning, 4, 1-29. 2006.

de Jong, Nel; Perfetti, Charles. Fluency training in the ESL classroom: An experimental study of fluency development and proceduralization. Language Learning. in press.

Diziol, Dejana; Walker, Erin; Rummel, Nikol; Koedinger, Kenneth. Using intelligent tutor technology to implement adaptive support for student collaboration. Educational Psychology Review. in press.

Eskenazi, Maxine. An overview of spoken language technology for education. Speech Communication (2009) doi:10.1016/j.specom.2009.04.005. in press.

Evans, Karen; Karabinos, Michael; Leinhardt, Gaea; Yaron, David. Chemistry in the field and chemistry in the classroom: A cognitive disconnect. Journal of Chemical Education 83 (4), 655-661. 2006.

Forbes-Riley, Kate; Rotaru, Mihai; Litman, Diane. The Relative Impact of Student Affect on Performance Models in a Spoken Dialogue Tutoring System. User Modeling and User-Adapted Interaction. Special issue on Affective Modeling and Adaptation. 18(1-2), 11-42. 2008.

Frishkoff, Gwen; Collins-Thompson, Kevyn; Perfetti, Charles; Callan, Jamie. Measuring incremental changes in word knowledge: Experimental validation and implications for learning and assessment. Behavioral Research Methods, 40(4), 907-925. 2008.

Frishkoff, Gwen; Perfetti, Charles. ERP measures reveal multiple aspects of robust word learning in children and adults. Invited for Special Issue of Developmental Neuropsychology. in press.

Frishkoff, Gwen; Perfetti, Charles; Collins-Thompson, Kevyn. Lexical quality in the brain: ERP evidence for robust word learning from context. Developmental Neuropsychology. in press.

Frishkoff, Gwen; Perfetti, Charles; Westbury, Chris. ERP Measures of Partial Semantic Knowledge: Left temporal indices of skill differences and lexical quality. Biological Psychology, 80(1), 130-147. 2009.

Gholson, Barry; Craig, Scotty. Promoting constructive activities that support vicarious learning during computer-based instruction. Educational Psychology Review, 18, 119-139. 2006.

Goldberg, Robert; Perfetti, Charles; Schneider, Walter. Distinct and common cortical activations for multimodal semantic categories. Cognitive, Affective, and Behavioral Neuroscience. Volume 6, Number 3, September 2006, pp. 214-222(9). 2006.

Goldberg; Perfetti, Charles; Fiez, Julie; Schneider, Walter. Selective retrieval of abstract semantic knowledge in left prefrontal cortex. Journal of Neuroscience, 27:3790-8. 2007.

Goldberg; Perfetti, Charles; Schneider, Walter. Perceptual knowledge retrieval activates sensory brain regions. Journal of Neuroscience. 26:4917 – 4921. 2006.

Graesser, Arthur; McNamara, Danielle; VanLehn, Kurt. Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iSTART. Educational Psychologist, 40(4), 225-234. 2005.

Harrer, Andreas; McLaren, Bruce; Pinkwart, Niels; Rummel, Nikol. A Mixed-Initiative Design for Adaptive Feedback to Support Collaborative Learning. Educational Psychology Review, Special Issue on "Instructional Communication in Computer-Supported Settings: Multidisciplinary Efforts Towards Adaptiveness.". in press.

Harrer, Andreas; McLaren, Bruce; Walker, Erin; Bollen, Lars; Sewall, Jonathan. Creating cognitive tutors for collaborative learning: steps toward realization. User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI), 16: 175-209. 2006.

Harrer, Andreas; Pinkwart, Niels; McLaren, Bruce; Scheuer, Oliver. The Scalable Adapter Design Pattern: Enabling Interoperability Between Educational Software Tools. IEEE Transactions on Learning Technologies, 1(2), 131-143. . 2008.

Hausmann, Robert; Chi, Michelene. Constructive collaboration interactions: The differential impact of critical and elaborative dialogues on problem solving and deep learning. Journal of Educational Psychology. in press.

Hernandez, Arturo; Li, Ping; MacWhinney, Brian. The emergence of competing modules in bilingualism. TRENDS in Cognitive Sciences Vol.9 No.5 Page 220-225. 2005.

Juffs, Alan. Second language acquisition of relative clauses in the languages of East Asia. Studies in Second Language Acquisition, 29, 361-365. 2007.

Klahr, David; Triona, Lara; Williams, Cameron. Hands On What? The Relative Effectiveness of Physical vs. Virtual Materials in an Engineering Design Project by Middle School Children. Journal of Research in Science Teaching , 44, 183-203. 2007.

Koedinger, Kenneth; Aleven, Vincent. Exploring the assistance dilemma in experiments with Cognitive Tutors. Educational Psychology Review, 19: 239-264. 2007.

Koedinger, Kenneth; Alibali, Martha; Nathan, Mitchell. Trade-offs between grounded and abstract representations: Evidence from algebra problem solving. Cognitive Science 32(2), 366-397. 2008.

Landi, Nicole; Perfetti, Charles; Bolger, Donald; Dunlap, Susan; Foorman, Barbara. The role of discourse context in developing word form representations: A paradoxical relationship between reading and learning. Journal of Experimental Child Psychology. 94(2), 114-133.`. 2006.

Lane, H. Chad; VanLehn, Kurt. Teaching program planning skills to novices with natural language tutoring. Computer Science Education, 15(3), 183-201. 2005.

Li, Ping; Zhao, Xiaowei; MacWhinney, Brian. Self-organizing processes in early lexical learning. Cognitive Science. 2007.

Litman, Diane; Forbes-Riley, Kate. Correlations between Dialogue Acts and Learning in Spoken Tutoring Dialogues. Natural Language Engineering, Vol 12(2), pp. 161-176, June 2006. 2006.

Litman, Diane; Forbes-Riley, Kate. Recognizing Student Emotions and Attitudes on the Basis of Utterances in Spoken Tutoring Dialogues with both Human and Computer Tutors. Speech Communication, Vol 48(5), pp. 559-590, May 2006. 2006.

Liu, Ying; Dunlap, Susan; Fiez, Julie; Perfetti, Charles. Evidence for neural accommodation to a writing system following learning. Human Brain Mapping, 28: 1223-1234. 2007.

Liu, Ying; Perfetti, Charles; Wang, Min. Visual analysis and lexical access of Chinese charactgers by Chinese as second language readers. Language and Linguistics, 7(3), 637-657. Institute of Linguistics, Academia Sinica in Taiwai. ISSN 1606-822X. 2006.

Liu, Ying; Wang, Min; Perfetti, Charles. Threshold-style processing of Chinese characters for adult second language learners. Memory and Cognition, 35(3), 471-480. 2007.

MacWhinney, Brian. The emergence of linguistic form in time. Connection Science. 17 (Number 3-4/September-December 2005): Pages 191-211. 2005.

Makatchev, Maxim; Jordan, Pamela; VanLehn, Kurt. Abductive Theorem Proving for Analyzing Student Explanations and Guiding Feedback in Intelligent Tutoring Systems. Journal of Automated Reasoning for Special Issue on Automated Reasoning and Theorem Proving in Education, Vol. 32(3):187-226. 2004.

Makatchev, Maxim; Jordan, Pamela; VanLehn, Kurt. Abductive Theorem Proving for Analyzing Student Explanations and Guiding Feedback in Intelligent Tutoring Systems. Journal of Automated Reasoning. Special issue on Automated Reasoning and Theorem Proving in Education, 32(3), 187-226. 2004.

Matsuda, Noboru; VanLehn, Kurt. GRAMY: A geometry theorem prover capable of construction. Journal of Automated Reasoning, 32(1), 3-33. 2004.

Meier, Anne; Spada, Hans; Rummel, Nikol. A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning. 2007.

Michaels, Sarah; O'Connor, Catherine; Resnick, Lauren. Deliberative discourse idealized and realized: Accountable talk in the classroom and in civic life. Studies in Philosophy and Education. DOI 10.1007/S11217-007-9071-1. 2007.

Mostow, Jack; Beck, Joseph. Some useful tactics to modify, map and mine data from intelligent tutors. Natural Language Engineering, Cambridge University Press, 12(2), 195-208. 2006.

Murray, R. Charles; VanLehn, Kurt; Mostow, Jack. Looking ahead to select tutorial actions: A decision-theoretic approach. International Journal of Artificial Intelligence and Education, 14, 235-278. 2004.

Nelson, Jessica; Balass, Michal; Perfetti, Charles. Differences between written and spoken input in learning new words. Written Language & Literacy, 8(2), 25-44. 2005.

Nelson, Jessica; Liu, Ying; Fiez, Julie; Perfetti, Charles. Assimilation and accommodation patterns in ventral occipitotemporal cortex in learning a second writing system. Human Brain Mapping, 30(3), 810-820. 2009.

Pavlik, Phillip. Understanding and applying the dynamics of test practice and study practice. Instructional Science. 2006.

Pavlik, Phillip; Anderson, John. Using a model to compute the optimal schedule of practice. Journal of Experimental Psychology: Applied, 14(2), 101-117. 2008.

Perfetti, Charles. Reading ability: Lexical quality to comprehension. Scientific Studies of Reading, 11(4), 357-383. 2007.

Perfetti, Charles; Bolger, Donald. The brain might read that way. Scientific Studies of Reading, 8(3), 293-304. 2004.

Perfetti, Charles; Liu, Ying. Orthography to phonology and meaning: Comparisons across and within writing systems. Reading and Writing, 18(3), 193-210. 2005.

Perfetti, Charles; Liu, Ying; Fiez, Julie; Nelson, Jessica; Bolger, Donald; Tan. Reading in two writing systems: Accommodation and assimilation in the brain’s reading network. Bilingualism: Language and Cognition, 10(2). 131-146. Special issue on “Neurocognitive approaches to bilingualism: Asian languages”, P. Li (Ed.). 2007.

Perfetti, Charles; Liu, Ying; Tan, Li Hai. The Lexical Constituency Model: some implications of research on Chinese for general theories of reading. Psychological Review, Vol 112, No 1, pages 43-59. 2005.

Perfetti, Charles; Tan, Li Hai; Siok, Wai Ting. Brain-behavior relations in reading and dyslexia: Implications of Chinese results. Brain and Language. 2006.

Perfetti, Charles; Wlotko, Edward; Hart, Lesley. Word learning and individual differences in word learning reflected in Event-Related Potentials. Journal of Experimental Psychology: Learning Memory and Cognition, 31(6), 1281-1292. 2005.

Perfetti, Charles; Yang, Chin-Lung; Schmalhofer, Franz. Comprehension skill and word-to-text integration processes. Applied Cognitive Psychology 22 (3), 303-318. 2008.

Pinkwart, Niels; Aleven, Vincent; Ashley, Kevin; Lynch, Collin. Weakness Detection and Feedback Principles in an Intelligent Tutoring System for Legal Argumentation. (article is in German). 2006.

Popescu, Octav; Aleven, Vincent; Koedinger, Kenneth. Logic-Based Natural Language Understanding for Cognitive Tutors. Natural Language Engineering. Pages 1-15. 2005.

Rau, Martina; Aleven, Vincent; Rummel, Nikol. Blocked versus Interleaved Practice with Multiple Graphical Representations of Fractions in an Intelligent Tutoring System. Under review.

Resnick, Lauren. Making accountability really count. Educational Measurement: Issues and Practice, 25(1), 33-37. 2006.

Resnick, Lauren; Zurawsky, Chris. Getting Back on Course: Fixing Standards-Based Reform and Accountability. American Educator, 29(1), 8-46. 2005.

Ritter, Steven. Authoring model-tracing tutors. Technology, Instruction, Cognition and Learning, 2(3), 231-247. 2005.

Ritter, Steven; Anderson, John; Koedinger, Kenneth; Corbett, Albert. The Cognitive Tutor: Applied research in mathematics education. Psychonomics Bulletin & Review, 14(2), pp. 249-255. 2007.

Roll, Ido; Aleven, Vincent; McLaren, Bruce; Koedinger, Kenneth. Designing for Metacognition - Applying Cognitive Tutor Principles to Metacognitive Tutoring. Metacognition and Learning, 2(2), 125-140. 2007.

Roscoe, Rod; Chi, Michelene. Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutors' explanations and questions. Review of Educational Research, 77(4), 534-574. 2007.

Roscoe, Rod; Chi, Michelene. Tutor learning: The role of explaining and responding to questions. Instructional Science, 36(4), 321-350. 2008.

Rose, Carolyn; Kumar, Rohit; Aleven, Vincent; Robinson, Allen; Wu, Chih. CycleTalk: Data Driven Design of Support for Simulation Based Learning. International Journal of Artificial Intelligence in Education, 16, 195-223. 2006.

Rose, Carolyn; VanLehn, Kurt. An Evaluation of a Hybrid Language Understanding Approach for Robust Selection of Tutoring Goals. International Journal of Artificial Intelligence in Education, 15(4), 325-355. 2005.

Salden, Ron; Aleven, Vincent; Renkl, Alexander; Schwonke, Rolf. Worked examples and tutored problem solving: redundant or synergistic formsof support. Topics in Cognitive Science, 1, 203-213. 2009.

Schwarz, Baruch & Asterhan, Christa. E-moderation of synchronous discussions in educational settings: A nascent practice. Journal of the Learning Sciences. in press

Schwonke, Rolf; Renkl, Alexander; Krieg, Carmen; Wittwer, Jorg; Aleven, Vincent; Salden, Ron. The Worked-example Effect: Not an Artifact of Lousy Control Conditions. Computers in Human Behavior, 25, 258-266. 2009.

Siok, Wai Ting; Niu, Zhendong; Jin, Zhen; Perfetti, Charles; Tan, Li Hai. A structural-functional basis for dyslexia in the cortex of Chinese readers. National Academy of Sciences, 105, 5561-5566. 2008.

Strand-Cary, Mari; Klahr, David. Developing elementary science skills: Instructional effectiveness and path independence. Cognitive Development, 23(4), 488-511. 2008.

Tan, Li Hai; Spinks, John; Eden, Guinevere; Perfetti, Charles; Siok, Wai Ting. Reading depends on writing, in Chinese. PNAS, 102, 8781-8785. 2005.

Tokowicz, Natasha; MacWhinney, Brian. Implicit and explicit measures of sensitivity to violations in second language grammar: An event-related potential investigation. Studies in Second Language Acquisition. Cambridge University Press. 27, Pages 173-204. 2005.

Tricomi, Elizabeth; Fiez, Julie. Feedback signals in the caudate reflect goal achievement on a declarative memory task. NeuroImage, 41(3), 1154-1167. 2008.

Triona, Lara; Klahr, David. Hands-on science: Does it matter what the student's hands are on in 'hands-on’ science. The Science Education Review, 6, 121-125. 2007.

VanLehn, Kurt. The Behavior of Tutoring Systems, International Journal of Artificial Intelligence in Education. . 2006.

VanLehn, Kurt; Graesser, Arthur, Arthur; Jackson, G. Tanner; Jordan, Pamela; Olney, Andrew; Rose, Carolyn. When are tutorial dialogues more effective than reading. Cognitive Science 31(1), 3-62. . 2007.

VanLehn, Kurt; Lynch, Collin; Schulze, Kay. The Andes Physics Tutoring System: Lessons Learned. International Journal of Artificial Intelligence in Education, 15 (3). Pages 147-204. 2005.

Walker, Erin; Rummel, Nikol; Koedinger, Kenneth. Integrating collaboration and cognitive tutoring data in evaluation of a reciprocal peer tutoring environment. Research and Practice in Technology Enhanced Learning. in press.

Walker, Erin; Rummel, Nikol; Koedinger, Kenneth. CTRL: A Research Architecture for Providing Adaptive Collaborative Learning Support. User Modeling and User-Adapted Interaction. in press.

Wang Min; Liu, Ying; Perfetti, Charles. The implicit and explicit learning of Chinese orthographic structure and function by alphabetic readers. Scientific Studies of Reading, 8(4), 357-379. 2004.

Wang, Min; Perfetti, Charles; Liu, Ying. Chinese-English biliteracy acquisition: Cross-language and writing system transfer. Cognition, 97, 67-88. 2005.

Yang, Chin-Lung; Perfetti, Charles. Contextual Constraints on the Comprehension of Relative Clause Sentences in Chinese: ERPs Evidence. Language and Linguistics, 7(3): 697-730. 2006.

Yang, Chin-Lung; Perfetti, Charles; Schmalhofer, Franz. Less skilled comprehenders’ ERPs show sluggish word-to-text integration processes. Written Language & Literacy, 8(2), 233-257. 2005.

Yang, Chin-Lung; Perfetti, Charles; Schmalhofer, Franz. ERP indicators of text integration across sentence boundaries. Journal of Experimental Psychology: Learning, Memory and Cognition. 2007 Jan Vol 33(1) 55-89. 2007.

== Conference Proceedings ==

Baker, Ryan; Barnes, Tiffany; Beck, Joseph. Educational Data Mining 2008: 1st International Conference on Educational Data Mining, Proceedings. Montreal, Quebec, Canada. June 2008. 2008.

== Conference Papers ==

Ai, Hua; Litman, Diane. Knowledge Consistent User Simulations for Dialog Systems. Interspeech, Antwerp, Belgium, August 2007. 2007.

Aleven, Vincent; Ashley, Kevin. Toward supporting hypoThesis formation and testing in an interpretive domain. 12th International Conference on Artificial Intelligence in Education. P 732-734. 2005.

Aleven, Vincent; McLaren, Bruce; Roll, Ido; Koedinger, Kenneth. Toward Tutoring Help Seeking: Applying Cognitive Modeling to Meta-Cognitive Skills; In the Proceedings of the Seventh International Conference on Intelligent Tutoring Systems (ITS-2004), Maceio, Brazil, August 2004. pp 227-239. 2004.

Aleven, Vincent; McLaren, Bruce; Roll, Ido; Koedinger, Kenneth. Exploring meta-cognitive tutoring by the Help Tutor: An Interactive Event. 12th International Conference on Artificial Intelligence in Education. 2005.

Aleven, Vincent; McLaren, Bruce; Ryu, Eunjeong; Baker, Ryan; Koedinger, Kenneth. The Help Tutor: Does Metacognitive Feedback Improve Students' Help-Seeking Actions, Skills and Learning?;Roll, I. Proceedings of the 8th International Conference on Intelligent Tutoring Systems. Lecture Notes in Computer Science: Intelligent Tutoring Systems, Volume 4053/2006, 360-369. Springer Berlin. 2006.

Aleven, Vincent; McLaren, Bruce; Sewall, Jonathan; Koedinger, Kenneth. The Cognitive Tutor Authoring Tools (CTAT): Preliminary evaluation of efficiency gains. M. Ikeda, K.D. Ashley, Kevin, & T-W. Chan (Eds), 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Lecture Notes in Computer Science, 4053 (pp. 61-70). Berlin: Springer. 2006.

Aleven, Vincent; Pinkwart, Niels; Ashley, Kevin; Lynch, Collin. Supporting Self-explanation of Argument Transcripts: Specific v. Generic Prompts . Workshop Proceedings on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems, 2006. 2006.

Aleven, Vincent; Roll, Ido; McLaren, Bruce; Ryu, Eunjeong; Koedinger, Kenneth. An architecture to combine meta-cognitive and cognitive tutoring: Pilot testing the Help Tutor. 12th International Conference on Artificial Intelligence in Education. 2005. Pp 17-24. 2005.

Aleven, Vincent; Rose, Carolyn. Authoring plug-in tutor agents by demonstration: Rapid, rapid tutor development. 12th International Conference on Artificial Intelligence in Education. P 735-737. 2005.

Aleven, Vincent; Sewall, Jonathan; McLaren, Bruce; Koedinger, Kenneth. Rapid Authoring of Intelligent Tutors for Real-World and Experimental Use. Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson, & W. Didderen (Eds.), 6th IEEE International Conference on Advanced Learning Technologies (ICALT 2006) (pp. 847-851). Los Alamitos, CA: IEEE Computer Society. 2006.

Anthony, Lisa; Yang, Jie; Koedinger, Kenneth. Evaluation of Multimodal Input for Entering Mathematical Equations on the Computer, ACM Conference on Human Factors in Computing Systems (CHI’2005), Portland, OR, 6 April 2005, p. . 2005.

Anthony, Lisa; Yang, Jie; Koedinger, Kenneth. Towards the Application of a Handwriting Interface for Mathematics Learning. IEEE Conference on Multimedia and Exp(ICME’2006), Toronto, Canada, July 2006. 2006.

Anthony, Lisa; Yang, Jie; Koedinger, Kenneth. Benefits of handwritten input for students learning algebra equation solving. International Conference on Artificial Intelligence in Education (AIED, 2007). 2007.

Anthony, Lisa; Yang, Jie; Koedinger, Kenneth. Adapting Handwriting Recognition for Applications in Algebra Learning. ACM Workshop on Educational Multimedia and Multimedia Education (EMME’2007), Augsburg, Germany, Sep 2007, pp. 47-56. 2007.

Anthony, Lisa; Yang, Jie; Koedinger, Kenneth. Toward Next-Generation, Intelligent Tutors: Adding Natural Handwriting Input. IEEE Multimedia 15(3), pp. 64-68. 2008.

Anthony, Lisa; Yang, Jie; Koedinger, Kenneth. Steps toward enhancing robust learning through worked examples and handwriting-based input. Short 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Arguello, Jaime; Rose, Carolyn. InfoMagnets: Making Sense of Corpus Data. Companion Proceedings for the N. American Chapter of the Association for Computational Linguistics. 2006.

Arguello, Jaime; Rose, Carolyn. Topic Segmentation of Dialogue. NAACL Workshop on Analyzing Conversations in Text and Speech. 2006.

Arguello, Jaime; Rose, Carolyn. Museli: A Multi-source Evidence Integration Approach to Topic Segmentation of Spontaneous Dialogue. North American Chapter of the Association for Computational Linguistics (short paper). 2006.

Asterhan, C. S. C., Butera, F., Nokes, T., Darnon, C., Schwarz, B. B., Butler, R., Levin, J., Belenky, D., & Gadgil, S. (in press). Motivation and affect in peer argumentation and socio-cognitive conflict. Proceedings of the 2010 International Conference of the Learning Sciences – ICLS 2010. In press

Asterhan, Christa & Eisenmann, Tammy. Online and face-to-face discussions in the classroom: A study on the experiences of 'active' and 'silent' students. In C. O'Malley, D. Suthers, P. Reimann & A. Dimitracopoulou (Eds), Computer-Supported Collaborative Learning Practices: CSCL2009 Conference Proceedings (pp. 132-136). 2009

Asterhan, Christa, Schwarz, Baruch & Butler, Ruth (2009). Inhibitors and facilitators of peer interaction that supports conceptual learning: The role of achievement goal orientations. In: N. A. Taatgen & H. van Rijn (Eds), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 1633-1638). Mahaw, NJ: Erlbaum. 2009

Baker, Ryan. Is Gaming the System State-or Trait. . On-Line Workshop on Data Mining for User Modeling at the 11th International Conference on User Modeling 2007, 76-80. 2007.

Baker, Ryan. Modeling and understanding students’ off-task behavior in intelligent tutoring systems. SIGCHI conference on Human Factors in Computing Systems. ACM Publishers. 2007.

Baker, Ryan. Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task. 2nd International Conference on Educational Data Mining (EDM 2009), Cordoba, Spain, July 1-3, 2009. to appear.

Baker, Ryan; Aleven, Vincent. Help abuse and proper use: How helpful is on-demand help when it is used properly?. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Baker, Ryan; Corbett, Albert; Aleven, Vincent. Improving Contextual Models of Guessing and Slipping with a Truncated Training Set. 1st International Conference on Educational Data Mining, 2008, 67-76. 2008.

Baker, Ryan; Corbett, Albert; Aleven, Vincent. More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian Knowledge Tracing. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008), 406-415. 2008.

Baker, Ryan; Corbett, Albert; Koedinger, Kenneth; Evenson, Shelley; Roll, Ido; Wagner, Angela; Naim, Meghan; Raspat, Jay; Baker, Ryan; Beck, Joseph. Adapting to When Students Game an Intelligent Tutoring System. 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. p 392-401. 2006.

Baker, Ryan; Corbett, Albert; Koedinger, Kenneth; Roll, Ido. Detecting When Students Game The System, Across Tutor Subjects and Classroom Cohorts. 10th International Conference on User Modeling. 2005.

Baker, Ryan; Corbett, Albert; Koedinger, Kenneth; Roll, Ido. Generalizing Detection of Gaming the System Across a Tutoring Curriculum. 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. p 402.-411. 2006.

Baker, Ryan; Corbett,Albert; Wagner, Angela. Human Classification of Low-Fidelity Replays of Student Actions. Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (ITS 2006). Jhongli, Taiwan. Pages 29-36. 2006.

Baker, Ryan; deCarvalho, Adriana . Labeling Student Behavior Faster and More Precisely with Text Replays. 1st International Conference on Educational Data Mining, 2008, 38-47. 2008.

Baker, Ryan; deCarvalho, Adriana; Raspat, Jay; Aleven, Vincent; Corbett, Albert; Koedinger, Kenneth. Educational Software Features that Encourage and Discourage "Gaming the System". 14th International Conference on Artificial intelligence in Education (AIED), July 6-10, 2009, Brighton, England. 2009.

Baker, Ryan; Rodrigo, Mercedes; Heffernan, Neil; Corbett, Albert; Roll,I do; Aleven, Vincent; Koedinger, Kenneth. Gaming the System: Evidence from data mining and human observation on affect, attitudes and learning. Abstract in Symposium: Learners Challenging ID – Unobtrusive Views on the Use of Instructional Interventions in CBE. (AERA 2008). 2008.

Baker, Ryan; Roll, Ido; Corbett, Albert; Koedinger, Kenneth. Do Performance Goals Lead Students to Game the System? Proceedings of the 12th International Conference on Artificial Intelligence in Education. 2005. Pp57-64. 2005.

Balass, Michal; Nelson, Jessica; Perfetti, Charles. Learning ESL Vocabulary with Context and Definitions: Order Effects and Self-Generation. Second Annual Meeting of Inter-Science of Learning Center Student and Post-doctoral Conference, Seattle, WA. . 2009.

Bauer, Aaron; Koedinger, Kenneth. Developing a Note Taking Tool from the Ground Up. Ed-Media 2005. AACE Press, 4181-4186. 2006.

Bauer, Aaron; Koedinger, Kenneth. Pasting and Encoding: Note-taking in Online Courses. IEEE International Conference on Advanced Learning Technologies (ICALT) 2006, pps 789-793. 2006.

Bauer, Aaron; Koedinger, Kenneth. Selection-based note-taking applications. ACM Symposium on Human Factors in Computing Systems 2007. 2007.

Beck, Joseph. Using learning decomposition to analyze student fluency development. Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (ITS 2006). Jhongli, Taiwan. Pages 21-28.. 2006.

Beck, Joseph. Does learner control affect learning. 13th International Conference on Artificial Intelligence in Education (AIED 2007). 2007.

Beck, Joseph. Difficulties in inferring student knowledge from observations (and why you should care). Workshop on Educational Data Mining (AIED 2007). 21-30. 2007.

Beck, Joseph; Chang, Kai-min; Mostow, Jack; Corbett, Albert. Does help help? A comparison of three evaluation frameworks. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Beck, Joseph; Mostow, Jack. How who should practice: Using learning decomposition to evaluate the efficacy of different types of…. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Belenky, Daniel; Nokes, Timothy. Motivation and Transfer: The role of achievement goals in preparation for future learning. 31st Annual Meeting of the Cognitive Science Society, 2009. to appear.

Blessing, Stephen; Gilbert, Stephen; Oureda, Steven; Ritter, Steven. Lowering the Bar for Creating Model-Tracing Intelligent Tutoring Systems. 13th International Conference on Artificial Intelligence in Education. 2007.

Blessing, Stephen; Gilbert, Stephen; Ritter, Steven. Developing an authoring system for cognitive models within commercial-quality ITSs. Nineteenth International FLAIRS Conference, pp. 497-502. 2006.

Bolger, Donald; Yang, Chin-Lung; Perfetti, Charles. Learning the meanings of words from contexts and definitions: ERP evidence. 15th Annual Meeting of the Society for the Scientific Study of Reading, Asheville, NC (July 2008). 2008.

Booth, Julie. Improving Algebra Learning in Real World Classrooms with Worked Examples and Self-Explanation. Presidential Symposium entitled The New Learning Sciences at the annual meeting of the Eastern Psychological Association, Pittsburgh, PA. 2009.

Booth, Julie; Koedinger, Kenneth. Key misconceptions in algebraic problem solving. B.C. Love, K. McRae, & V. M. Sloutsky (Eds.), 30th Annual Cognitive Science Society (pp. 571-576). Austin, TX: Cognitive Science Society. 2008.

Booth, Julie; Koedinger, Kenneth. Facilitating the Diagrammatic Advantage for Algebraic Word Problems. AERA, 2009. 2009.

Booth, Julie; Koedinger, Kenneth; Siegler, Robert. The effect of prior conceptual knowledge on procedural performance and learning in algebra. D.S. McNamara & J.G. Trafton (Eds.), 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society. [Abstract]. 2007.

Booth, Julie; Siegler, Robert. The Role of internal representations of magnitude in numerical estimation. 12th Biennial Conference for Research on Learning and Instruction (EARLI). Budapest, Hungary, August, 2007. 2007.

Borek; McLaren, Bruce; Karabinos, Michael; Yaron, David. How Much Assistance is Helpful to Students in Discovery Learning? One Step Toward Answering This Question. Submitted to the Fourth European Conference on Technology Enhanced Learning "Learning in the Synergy of Multiple Disciplines" (EC-TEL 2009), September 29-October 2, 2009, Nice, France. submitted.

Brown, Jonathan; Eskenazi, Maxine. Student Text And Curriculum Modelling For Reader-Specific Document Retrieval. IASTED International Conference on Human-Computer Interaction. 2005. 2005.

Brown, Jonathan; Eskenazi, Maxine. Using Simulated Students for the Assessment of Authentic Document Retrieval. 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan. P 685-688. 2006.

Brown; Frishkoff, Gwen; Eskenazi, Maxine. Automatic question generation for vocabulary assessment. Human Language Technology, HLT/EMNLP 2005. Vancouver, B.C. 2005.

Butcher, Kirsten; Aleven, Vincent. Integrating visual and verbal knowledge during classroom learning with computer tutors. D.S. McNamara & J.G. Trafton (Eds.), 29th Annual Meeting of the Cognitive Science Society, (pp. 137-142). 2007.

Butcher, Kirsten; Aleven, Vincent. Diagram Interaction during Intelligent Tutoring in Geometry: Support for Knowledge Retention and Deep Transfer. B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), 30th Annual Conference of the Cognitive Science Society (pp. 1736-1741). Austin, TX: Cognitive Science Society. 2008.

Butcher, Kirsten; Aleven, Vincent. Learning from visual-verbal sources in intelligent tutoring. Inter-Science of Learning Center (iSLC) Conference, Carnegie Mellon University, Pittsburgh, PA. 2008.

Butcher, Kirsten; Aleven, Vincent. Visual self-explanation during intelligent tutoring. More than attentional focus? European Association for Research on Learning and Instruction, 2009. to appear.

Butcher, Kirsten; Chi, Michelene. How can diagrams scaffold text comprehension. EARLI SIG2 Meeting, University of Nottingham. 2006.

Callan, Jamie; Eskenazi, Maxine; Perfetti, Charles. Progress in Providing Reader-Specific lexical Practice for Inproved Reading Comprehension. IES Research Conference. June 15-16 2006, Washington DC. 2006.

Cen, Hao; Koedinger, Kenneth; Junker, Brian. Automating Cognitive Model Improvement by A*Search and Logistic Regression. AAAI Workshop on Educational Data Mining. 2005. 2005.

Cen, Hao; Koedinger, Kenneth; Junker, Brian. Learning Factors Analysis – A General Method for Cognitive Model Evaluation and Improvement. Ikeda et al (Eds.). 8th International Conference on Intelligent Tutoring Systems (ITS-2006), p 164-175. Springer: Berlin/Heidelberg. 2006.

Cen, Hao; Koedinger, Kenneth; Junker, Brian. Is over practice necessary? – Improving learning efficiency with the Cognitive Tutor through educational data mining. R. Luckin et al (Eds.). 13th International Conference on Artificial Intelligence in Education (AIED 2007), pp. 511-518. IOS Press. 2007.

Cen, Hao; Koedinger, Kenneth; Junker, Brian. Comparing two IRT models for cognitive model evaluation. Short 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Chang, Kai-min; Beck, Joseph; Mostow, Jack; Corbett, Albert. A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems. 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. p 104-113. 2006.

Chi, Michelene. Teaching a stand-alone module: Emergence for understanding science concepts. Paper in Symposium: Complex Systems and the Cognitive Sciences: Potential for Pervasive Theoretical and Research Implications? (CogSci 2007). 2007.

Chi, Min; Jordan, Pamela; VanLehn, Kurt; Litman, Diane. To Elicit Or To Tell: Does It Matter. 14th International Conference on Artificial Intelligence in Education (AIED), Brighton, UK, July 2009. to appear.

Chi, Min; VanLehn, Kurt. The impact of explicit strategy instruction on problem-solving behaviors across intelligent tutoring systems. D. McNamara & G. Trafton (Eds.) 29th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum. 2007.

Chi, Min; VanLehn, Kurt. Domain-specific and domain-independent interactive behaviors in Andes. R. Luckin & K. Koedinger, Kenneth (Eds.), Artificial Intelligence in Education. Amsterdam, Netherlands: IOS Press. 2007.

Chi, Min; VanLehn, Kurt. Porting an intelligent tutoring system across domains. R. Luckin & K. Koedinger, Kenneth (Eds.), Artificial Intelligence in Education. Amsterdam, Netherlands: IOS Press. 2007.

Chi, Min; VanLehn, Kurt. Accelerated future learning via explicit instruction of a problem solving strategy. R. Luckin, K. R. Koedinger, Kenneth & J. Greer (Eds.) Artificial Intelligence in Education. pp. 409-416. Amsterdam, Netherlands: IOS Press. 2007.

Chi, Min; VanLehn, Kurt. Eliminating the gap between the high and low students through meta-cognitive strategy instruction. Lecture Notes in Computer Science: Vol 5091. 9th International Conference on Intelligent Tutoring Systems, 2008. Heidelberg: Springer Berlin, 603-614. 2008.

Cocea, Mihaela; Hershkovitz, Arnon; Baker, Ryan. The Impact of Off-Task Behavior and Gaming on Learning: Immediate or Aggregate. 14th International Conference on Artificial Intelligence in Education (AIED), July 6-10, 2009, Brighton, England. to appear.

Collins-Thompson, Kevyn; Callan, Jamie. Automatic and human scoring of word definition resopnses. Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Rochester, NY. (April, 2007). 2007.

Connelly, John; Katz, Sandra. Toward more robust learning of physics via reflective dialogue extensions. To appear ED-MEDIA 2009. to appear.

Corbett, Albert; Wagner, Angela; Lesgold, Sharon; Ulrich, Harry; Stevens, Scott. Analyzing Algebra Example Solutions. International Conference of the Learning Sciences (ICLS 2006). Bloomington, IN, USA. p. 99. 2006.

Craig, Scotty; VanLehn, Kurt; Chi, Michelene . Promoting learning by observing deep-level reasoning questions on quantitative physics problem solving with Andes. K. McFerrin et al. (Eds.) Society for Information Technology & Teacher Education International Conference 2008 (1065-1068). Chesapeake, VA: AACE. 2008.

Craig, Scotty; VanLehn, Kurt; Gadgil, Soniya; Chi, Michelene. Learning from collaboratively observing videos during problem solving with Andes. R. Luckin, K. R. Koedinger, Kenneth & J. Greer (Eds.) Artificial Intelligence in Education. pp. 554-556. Amsterdam, Netherlands: IOS Press. 2007.

Cui; Kumar, Rohit; Rose, Carolyn; Koedinger, Kenneth. Story generation to accelerate math problem authoring for practice and assessment. Short 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Cui; Rose, Carolyn. An Authoring tool that facilitates the rapid development of dialogue agents for intelligent tutoring. Short 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Davenport, Jodi; Karabinos, Michael; Yaron, David. Exploring the ways in which coordinating different representations of chemical processes influences conceptual learning in introductory chemistry. Nineteenth Biennial Conference on Chemical Education in West Lafayette, Indiana. July 31, 2006. P 104. 2005.

Davenport, Jodi; Klahr, David; Koedinger, Kenneth. The influence of diagrams on chemistry learning. 12th Biennial Conference of the European Association for Research on Learning and Instruction (EARLI), August 2007. 2007.

Davenport, Jodi; McEldoon, Katherine; Klahr, David. Depicting invisible processes: The influence of molecular-level diagrams in Chemistry instruction. 29th Annual meeting of the Cognitive Science Society, p. 1737, August 2007. 2007.

Davenport, Jodi; Yaron, David; Karabinos, Michael; Greeno, James. Conceptual development in chemical equilibrium. Research in Chemical Education Symposium at the 20th Biannual Conference on Chemical Education, Bloomington, IN (July 2008). 2008.

Davenport, Jodi; Yaron, David; Karabinos, Michael; Klahr, David; Koedinger, Kenneth. Chemical equilibrium: an evaluation of a new type of instruction. Gordon Conference for Chemistry Education Research and Practice, 2007. 2007.

Davenport, Jodi; Yaron, David; Klahr, David; Koedinger, Kenneth. When do diagrams enhance learning? A framework for designing relevant representations. 2008 International Conference of the Learning Sciences, Utrecht, Netherlands, June 2008. 2008.

Davenport, Jodi; Yaron, David; Klahr, David; Koedinger, Kenneth. When do diagrams enhance science learning. First Annual Inter-Science of Learning Center Conference in Pittsburgh, PA, February 2008. 2008.

de Jong, Nel; Halderman; Ross. The effect of formulaic sequences training on fluency development in an ESL classroom. American Association for Applied Linguistics Conference, Denver, CO, March 2009. 2009.

de Jong, Nel; McCormick, Dawn; O'Neill, M. Christine; Bradin Siskin,Claire. Self-correction and fluency in ESL speaking development. American Association for Applied Linguistics (AAAL)Conference, April 2007 in Costa Mesa, CA. 2007.

Diziol, Dejana; Rummel, Nikol; Kahrimanis; Guevara; Holz; Spada, Hans; Fiotakis. Using contrasting cases to better understand the relationship between students’ interactions and their learning outcome. G. Kanselaar, V. Jonker, P.A. Kirschner, & F. Prins, (Eds.), International perspectives of the learning sciences: Cre8ing a learning world. Eighth International Conference of the Learning Sciences (ICLS 2008), Vol 3 (pp. 348-349). International Society of the Learning Sciences, Inc. ISSN 1573-4552. 2008.

Diziol, Dejana; Rummel, Nikol; Spada, Hans. Unterstützung von computervermitteltem kooperativem Lernen in Mathematik durch Strukturierung des Problemlöseprozesses und adaptive Hilfestellung [Supporting computer-mediated collaborative learning in mathematics by structuring the problem-solving process and offering adaptive support]. 11th Conference of the "Fachgruppe Pädagogische Psychologie der Deutschen Gesellschaft für Psychologie" [German Psychological Association]. Berlin, Germany. 2007.

Diziol, Dejana; Rummel, Nikol; Spada, Hans. Erwerb von prozeduralem und konzeptuellem Wissen in Mathematik – Wo ist kooperatives Lernen hilfreich? [Acquisition of procedural and conceptual knowledge in mathematics – When is cooperative learning beneficial?] Paper presented at the 71st conference of the "Arbeitsgemeinschaft für Empirische Pädagogische Forschung (AEPF)" [Consortium for empirical educational research]. Kiel. 2008.

Diziol, Dejana; Rummel, Nikol; Spada, Hans. Procedural and Conceptual Knowledge Acquisition in Algebra – When Does Collaboration Make a Difference? Paper submitted to 13th European Conference for Research on Learning and Instruction (EARLI) 2009. Amsterdam, The Netherlands. to appear.

Diziol, Dejana; Rummel, Nikol; Spada, Hans. Procedural and Conceptual Knowledge Acquisition in Mathematics: Where is Collaboration Helpful. Computer-Supported Collaborative Learning (CSCL) Conference 2009, Rhodes, Greece. to appear.

Diziol, Dejana; Rummel, Nikol; Spada, Hans; McLaren, Bruce. Promoting learning in mathematics: script support for collaborative problem solving with the Cognitive Tutor Algebra. C.A. Chinn, G. Erkins & S. Puntambekar (Eds.), Mice minds and society: Conference on Computer Supported Collaborative Learning (CSCL-07), 8(1), 39-41. 2007.

Donmez, Pinar; Carbonell; Rose, Carolyn. TurboCharging: A New Cascaded Ensemble Learning Method. Submitted to International Conference on Machine Learning. 2005.

Donmez, Pinar; Rose, Carolyn, Carolyn; Stegmann, Karsten; Weinberger, Armin; Fischer, Frank. Supporting CSCL with Automatic Corpus Analysis Technology, Proceedings of Computer Supported Collaborative Learning, 2005, pages 1-10. (nominated for best paper award). 2005.

Evans, Karen; Karabinos, Michael; Leinhardt, Gaea; Yaron, David. “Chemistry in the field and chemistry in the classroom: A disconnect. ” First-Year Undergraduate Chemistry Education International Conference, Urbana-Champagne, IL, May 2005. 2005.

Evans, Karen; Yaron, David; Leinhardt, Gaea. Learning stoichiometry: A comparison of text and multimedia formats. 20th Biannual Conference on Chemical Education, Bloomington, IN (July 2008). 2008.

Feenstra, Laurens; Aleven, Vincent; Rummel, Nikol; & Taatgen, Nils. Multiple interactive representations for fractions learning. I10th international conference on intelligent tutoring systems (ITS), 221-3. 2010.

Feng, Mingyu; Beck, Joseph; Heffernan, Neil; Koedinger, Kenneth. Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standardized Test? 1st International Conference on Educational Data Mining, 2008. [full paper]. 2008.

Feng, Mingyu; Heffernan, Neil. Informing Teachers Live about Student Learning: Reporting in Assistment System. 12th Annual Conference on Artificial Intelligence in Education Workshop on Usage Analysis in Learning Systems. 2005. Amsterdam. P25-32. 2005.

Feng, Mingyu; Heffernan, Neil; Koedinger, Kenneth. Looking for Sources of Error in Predicting Student’s Knowledge. AAAI 2005 workshop on Educational Data Mining. 2005. 2005.

Feng, Mingyu; Heffernan, Neil; Koedinger, Kenneth. Predicting State Test Scores Better with Intelligent Tutoring Systems: Developing Metrics to Measure Assistance Required. In the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. p 31-40. 2006.

Fiez, Julie. Educational neuroscience: Time for a bridge? In J Geake & U Goswami (Organizers) Challenges and Opportunities for Educational Neuroscience. Workshop sponsored by the National Science Foundation, Washington, D.C. 2007.

Forbes-Riley, Kate; Litman, Diane. Adapting to Student Uncertainty Improves Tutoring Dialogues. 14th International Conference on Artificial Intelligence in Education (AIED), Brighton, UK, July 2009. to appear.

"Forbes-Riley, Kate; Litman, Diane; Purandare, Amruta; Rotaru, Mihai; Tetreault, Joel. Comparing Linguistic Features for Modeling Learning in Computer Dialogue Tutoring. 13th International Conference on Artificial Intelligence in Education (AIED), Los Angeles, CA, July, 2007.

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Forbes-Riley, Kate; Litman, Diane; Rotaru, Mihai. Responding to student uncertainty during computer tutoring: An Experimental evaluation. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Forbes-Riley, Kate; Litman, Diane; Silliman, Scott; Purandare, Amruta. Uncertainty Corpus: Resource to Study User Affect in Complex Spoken Dialogue Systems. 6th Language Resources and Evaluation Conference (LREC 2008), Marrakech, Morocco, (May-June 2008). 2008.

Forbes-Riley, Kate; Rotaru, Mihai; Litman, Diane; Tetreault, Joel. Exploring affect-context dependencies for adaptive system development. Human Language technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), 41-44, Rochester, NY. (April, 2007). 2007.

Frishkoff, Gwen. Neural correlates of vocabulary acquisition: Evidence from dense-array EEG. 2007 meeting of the American Educational Research Association, Chicago, IL. 2007.

Frishkoff, Gwen. ERP measures of word learning: Separation of N3, MFN, and N4 semantic components, Paper presented at the 47th Annual Meeting of the Society for Psychophysiological Research. Savannah, Georgia, October 19, 2007. 2007.

Frishkoff, Gwen; Levin, Lori; Pavlik, Phillip; Idemaru, Kaori; de Jong, Nel. A model-based approach to second-language learning of grammatical constructions. B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), 30th Annual Conference of the Cognitive Science Society (pp. 1665-1670). Austin, TX: Cognitive Science Society. 2008.

Frishkoff, Gwen; Pavlik, Phillip; Levin, Lori; de Jong, Nel. Providing optimal support for robust learning of syntactic constructions in ESL. Annual Meeting of the Cognitive Science Society, 2008. 2008.

Frishkoff, Gwen; Perfetti, Charles. Partial word knowledge and measures of Incremental word learning: Brain and behavioral experiments with adults and children (Ages 9 - 11). 2007 meeting of the American Educational Research Association, Chicago, IL. 2007.

Frishkoff, Gwen; Perfetti, Charles. ERP Evidence for stages of meaning acquisition in the development of the print lexicon. 15th Annual Meeting of the Society for the Scientific Study of Reading, Asheville, NC (July 2008). 2008.

Frishkoff, Gwen; Perfetti, Charles; Collins-Thomjpson, Kevyn; Callan, Jamie. Effects of Misleading Contexts on Word Learning: How Malapropisms May Affect the Development of Stable and Robust Word Representations. American Educational Research Association (2006). 2006.

Gadgil, Soniya; Nokes, Timothy. Analogical scaffolding in collaborative learning. 31st Annual Meeting of the Cognitive Science Society, 2009. to appear.

Greeno, James; MacWhinney, Brian. Learning as Perspective Taking: Conceptual Alignment in the Classroom, International Conference of the Learning Sciences (ICLS 2006). Bloomington, IN, USA, p. 930. 2006.

Guo, Yu; Heffernan, Neil; Beck, Joseph. Trying to reduce bottom-out hinting: Will telling students how many hits they have left help. . 2008.

Gweon, Gahgene; Arguello, Jaime; Pai, Carol; Carey, Regan; Zaiss, Zachary; Rose, Carolyn. Towards a Prototyping Tool for Behavior Oriented Authoring of Conversational Agents for Educational A. Second Workshop for Building Educational Applications using NLP. Associationl for Computational Linguistics 2005. 2005.

Gweon, Gahgene; Rose, Carolyn; Albright, Emil; Cui, Yue. Evaluating the Effect of Feedback from a CSCL Problem Solving Environment on Learning, Interaction, and Perceived Interdependence. Conference on Computer Supported Collaborative Learning (CSCL-07). Rutgers University. 2007.

Gweon, Gahgene; Rose, Carolyn; Wittwer, Joerg; Nueckles, Matthias. Supporting Efficient and Reliable Content Analysis Using Automatic Text Processing Technology. Interact ’05 (short paper) Pp 1112. 2005.

Gweon, Gahgene; Rose, Carolyn; Zaiss, Zachary; Carey, Regan. Providing Support for Adaptive Scripting in an On-Line Collaborative Learning Environment, Proceedings of CHI 06: ACM conference on human factors in computer systems. New York: ACM Press. (nominated for a best paper award). 2006.

Harrer, Andreas; McLaren, Bruce; Walker, Erin; Bollen, Lars; Sewall, Jonathan. Collaboration and Cognitive Tutoring: Integration, Empirical Results, and Future Directions. 12th International Conference on Artificial Intelligence in Education; Amsterdam, the Netherlands in July 2005. 2005.

Harrer, Andreas; Pinkwart, Niels; McLaren, Bruce; Scheuer, Oliver. How Do We Get the Pieces to Work Together? A New Software Architecture to Support Interoperability between Educational Software Tools. B. Woolf, E. Aimeur, R. Nkambou, S. Lajoie (Eds), 9th International Conference on Intelligent Tutoring Systems (ITS-08), Lecture Notes in Computer Science, 5091 (pp. 715-718). Berlin: Springer. 2008.

Hausmann, Robert. Why do elaborative dialogs lead to effective problem solving and deep learning? In R. Sun & N. Miyake (Eds.), 28th Annual Meeting of the Cognitive Science Society (pp.1465-1469). Alpha, NJ: Sheridan Printing. 2006.

Hausmann, Robert. An analysis of generative dialogue patterns across interactive learning environments: Explanation, elaboration, and co-construction. Intelligent Tutoring in Serious Games Workshop, hosted by the Institute for Creative Technologies at USC, Marina del Rey, CA. 2007.

Hausmann, Robert; Nokes, Timothy. Harnessing What You Know: Models of Transfer in Introductory Physics. Full paper submitted to the Second International Conference on Educational Data Mining (EDM 2009), Cordoba, Spain. submitted.

Hausmann, Robert; Nokes, Timothy; VanLehn, Kurt; Gershman, Sophia. The design of Self-explanation prompts: The fit hypoThesis. 31st Annual Meeting of the Cognitive Science Society. to appear.

Hausmann, Robert; Nokes, Timothy; VanLehn, Kurt; Gershman, Sophia. Revising models or filling gaps? The impact of prompting on self-explanation and robust learning. Symposium accepted to the 13th Biennial European Association for Research on Learning and Instruction Conference (EARLI). Amsterdam, Netherlands, 2009. to appear.

Hausmann, Robert; Nokes, Timothy; VanLehn, Kurt; van de Sande, Brett. Integrating Multiple Sources of Data to Evaluate Learning in Intelligent Tutoring Systems. The International Conference on Artificial Intelligence in Education (AIED2009). submitted.

Hausmann, Robert; van de Sande, Brett. An Analysis of Student Learning Using the Andes Intelligent Tutor Homework System. summer meeting of the American Association of Physics Teachers, Greensboro, NC. August 2007. 2007.

Hausmann, Robert; van de Sande, Brett; van de Sande, Carla; VanLehn, Kurt. Productive Dialogue During Collaborative Problem Solving. P.A. Kirschner, F. Prins, V. Jonker, & G. Kanselaar (Eds.), International Conference for the Learning Sciences -- ICLS 2008 (Vol. 1, pp. 327-334). The Netherlands: ISLS. 2008.

Hausmann, Robert; van de Sande, Brett; VanLehn, Kurt. Trialog: How peer collaboration helps remediate errors in an ITS. 21st International FLAIRS Conference, (pp. 415-420), Menlo Park: CA, AAAI Press. 2008.

Hausmann, Robert; van de Sande, Brett; VanLehn, Kurt. Shall we explain? Augmenting learning from intelligent tutoring systems and peer collaboration. B. P. Woolf, E. Aimeur, R. Nkambou & S. Lajoie (eds). Intelligent Tutoring Systems: 9th International Conference, ITS2008, pp. 636-645. Amsterdam: IOS Press. 2008.

Hausmann, Robert; van de Sande, Brett; VanLehn, Kurt. Are self-explaining and coached problem solving more effective when done by pairs of students than alone? In B. C. Love, K. McRae & V. M. Sloutsky (Eds.), 30th Annual Conference of the Cognitive Science Society. (pp. 2369-2374). Austin, TX: Cognitive Science Society. 2008.

Hausmann, Robert; VanLehn, Kurt. Self-explaining in the Classroom: Learning Curve Evidence. 29th Annual Conference of the Cognitive Science Society. 2007. Pages 1067-1072. Austin, TX: Cognitive Science Society. 2007.

Hausmann, Robert; VanLehn, Kurt. Explaining self-explaining: A contrast between content and generation. R. Luckin, K.R. Koedinger, Kenneth & J. Greer (Eds.), Artificial Intelligence in Education: Building technology rich learning contexts that work (Vol 158, pp. 417-424). Amsterdam: IOS Press. [Best Paper Award]. 2007.

Heffernan, Neil; Koedinger, Kenneth. The Assistment Builder: A Rapid Development Tool for ITS. 12th Annual Conference on Artificial Intelligence in Education 2005 Workshop on Adaptative Systems for Web Based Education: Tools and Reusability. 2005. 2005.

Heilman, Michael; Collins-Thompson, Kevyn; Callan, Jamie; Eskenazi, Maxine. Classroom success of an intelligent tutoring system for lexical practice and reading comprehension. 9th International Conference on Spoken Language Processing (ICSLP). 2006.

Heilman, Michael; Collins-Thompson, Kevyn; Callan, Jamie; Eskenazi, Maxine. Combining lexical and grammatical features to improve readability measures for first and second language texts. Human Language Technology Conference. Rochester, NY, (2007). 2007.

Heilman, Michael; Collins-Thompson, Kevyn; Eskenazi, Maxine. An Analysis of Statistical Models and Features for Reading Difficulty Prediction. The 3rd Workshop on Innovative Use of NLP for Building Educational Applications. Annual Meeting of the Association for Computational Linguistics, 2008. 2008.

Heilman, Michael; Eskenazi, Maxine. Language Learning: Challenges for Intelligent Tutoring Systems. Workshop of Intelligent Tutoring Systems for Ill-Defined Domains. 8th International Conference on Intelligent Tutoring Systems. June 2006, pp 20-28. 2006.

Heilman, Michael; Eskenazi, Maxine. Application of automatic thesaurus extraction for computer generation of vocabulary questions. SLaTE2007 Workshop on Speech and Language Technology in Education, Farmington, PA (October 2007). 2007.

Heilman, Michael; Feeney. An automatic measure of reader engagement and attention. Short 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Heilman, Michael; Juffs, Alan; Eskenazi, Maxine. Choosing reading passages for vocabulary learning by topic to increase intrinsic motivation. 13th International Conference on Artificial Intelligence in Education. Marina del Rey, CA., 2007. 2007.

Heilman, Michael; Zhao; Pino, Juan; Eskenazi, Maxine. Retrieval of Reading Materials for Vocabulary and Reading Practice. The 3rd Workshop on Innovative Use of NLP for Building Educational Applications. Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Columbus OH, 2008. 2008.

Heiner, Cecily; Beck, Joseph; Mostow, Jack. Automated Vocabulary Instruction in a Reading Tutor. 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. 2006.

Hensler, Brooke; Beck, Joseph. Are all questions created equal? Factors that influence cloze question difficulty. Thirteenth Annual Meeting Society for the Scientific Study of Reading. July 5-8, 2006. Vancouver, Canada. 2006.

Hu, Wenze; Wu, Sue-Mei; Zhang, Zheng-sheng; Cai, Jie. Bridging between classical and modern Chinese. American Council on the Teaching of Foreign Languages (ACTFL) Annual Meeting, 2007. 2007.

Jones, Christopher. French Online and the Open Learning Initiative. Kentucky Foreign Language Conference, April 2007, Lexington, Kentucky. 2007.

Jones, Christopher; Queuniet, Sophie. French Online and the French LearnLab: Instruction and Research. European Computer Assisted Language Learning 2006. 2006.

Jones, Christopher; Siskin,Claire. Building the New French Online: The Challenges of shared infrastructure. CALICO (Computer-Assisted Language Instruction Consortium), May 2007, Texas State University, San Marcos. 2007.

Jordan, Pamela. Using Student Explanations as Models for Adapting Tutorial Dialogue. 17th International FLAIRS Conference. P905-910. 2004.

Jordan, Pamela. Topic initiative in a simulated peer dialogue agent. 13th International Conference on Artificial Intelligence in Education, (AIED), Marina del Ray, CA (July, 2007). 2007.

Jordan, Pamela; Albacete, Patricia; VanLehn, Kurt. Taking Control of Redundancy in Scripted Tutorial Dialogue. Int. Conference on Artificial Intelligence in Education, pp. 314 - 321. 2005.

Jordan, Pamela; Hall, Brian; Ringenberg, Michael; Cui, Yue; Rose, Carolyn. Tools for authoring a dialogue agent that participates in learning studies. 13th International Conference on Artificial Intelligence in Education. Marina del Rey, CA. (July 2007). 2007.

Jordan, Pamela; Litman, Diane. Minimal feedback during tutorial dialogue. Short 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Jordan, Pamela; Litman, Diane; Lipschultz, Michael; Drummond, Joanna. Evidence of Misunderstandings in Tutorial Dialogue and their Impact on Learning. 14th International Conference on Artificial Intelligence in Education (AIED), Brighton, UK, July 2009. to appear.

Jordan, Pamela; Makatchev, Maxim; Pappuswamy, Umarani; VanLehn, Kurt; Albacete. A natural language tutorial dialogue system for physics. G. Sutcliffe & R. Goebel (Eds.), 19th International FLAIRS Conference. Menlo Park, CA: AAAI Press. P 521-526. 2006.

Jordan, Pamela; Makatchev, Maxim; VanLehn, Kurt. Combining Competing Language Understanding Approaches in an Intelligent Tutoring System. Intelligent Tutoring Systems Conference, vol 3220, pp 346-357. 2004.

Jordan, Pamela; Ringenberg, Michael; Hall, Brian. Rapidly Developing Dialogue Systems that Support Learning Studies. Workshop Proceedings on Teaching With Robots, Agents, and NLPat, 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan. 2006.

Jordan, Pamela; VanLehn, Kurt. Discourse Processing for Explanatory Essays in Tutorial Applications. 3rd SIGdial Workshop on Discourse and Dialogue, Vol. 2, from the Annual Meeting of the ACL, pp 74-83. 2006.

Juffs, Alan; Eskenazi, Maxine; Heilman, Michael; Wilson, Lois; Friedline, Benjamin. Activity theory and computer assisted learning of English vocabulary. American Association for Applied Linguistics, 2007. 2007.

Juffs, Alan; Eskenazi, Maxine; Wilson, Lois; Pelletreau, Timothy; Sanders, James; Callan, Jamie; Brown, Jonathan. Promoting robust learning of vocabulary through computer assisted language learning. Joint conference of AAAL and ACLA/CAAL 2006, Montreal, June 2006. 2006.

Juffs, Alan; Rodriguez. Working memory capacity in context: differential effects on comprehension of relative clauses and binding. Second Language Research Forum. University of Illinois, Champaign Urbana. October 13, 2007. 2007.

Juffs, Alan; Wilson, Lois; Eskenazi, Maxine; Heilman, Michael. Robust learning of vocabulary in classrooms and in CALL. American Association of Applied Linguistics, Washington, DC. 2008.

Kao, Yvonne; Roll, Ido; Koedinger, Kenneth. Source of difficulty in multi-step geometry area problems. 29th Annual Meeting of the Cognitive Science Society, 2007. 2007.

Kao, Yvonne; Roll, Ido; Koedinger, Kenneth. Sources of difficulty in multi-step geometry area problems. 29th Annual Meeting of the Cognitive Science Society. (CogSci 2007). 2007.

Kao, Yvonne; Roll, Ido; Koedinger, Kenneth. The composition effect in geometry area problems. Twenty-Ninth Meeting of the Cognitive Science Society, CogSci 2007. 2007.

Katz, Sandra; Connelly, John; Wilson, Christine. When should dialogues in a scaffolded learning environment take place? In P. Kommers & G. Richards (Eds.), EdMedia 2005 (pp. 2850-2855). Norfolk: VA: AACE. 2005.

Katz, Sandra; Connelly, John; Wilson, Christine. Out of the lab and into the classroom: An evaluation of reflective dialogue in Andes. K. Koedinger, Kenneth & R. Luckin (Eds). Artificial Intelligence in Education: Building Technology Rich Learning Contexts that Work (pp. 425-432). Amsterdom: IOS Press. 2007.

Koedinger, Kenneth. Cognitive Tutors and Opportunities for Convergence of Human and Machine Learning Theory. AAAI 2006. 2006.

Koedinger, Kenneth. Enabling technologies from the Pittsburgh Science of Learning Center. 2007 meeting of the American Educational Research Association, Chicago, IL. 2007.

Koedinger, Kenneth. Achieving robust learning through cognitive analysis and advanced technology. Gordon Conference for Chemistry Education Research and Practice, 2007. 2007.

Koedinger, Kenneth; Aleven, Vincent; Baker, Ryan. In vivo experiments on whether tutoring meta-cognition yields robust learning. 12th Biennial Conference for Research on Learning and Instruction (EARLI). Budapest, Hungary, August, 2007. 2007.

Koedinger, Kenneth; Aleven, Vincent; Baker, Ryan. In vivo experiments on whether tutoring meta-cognition yields robust learning. 2007 meeting of the American Educational Research Association, Chicago, IL. 2007.

Koedinger, Kenneth; Aleven, Vincent; Baker, Ryan; Roll,Ido. Toward understanding when tutoring meta-cognition enhances domain learning. Workshop on Metacognition and SRL. (AIED 2007). 2007.

Koedinger, Kenneth; Aleven, Vincent; Heffernan, Neil; MacLaren; Hockenberry, Matthew. Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration;. the Seventh International Conference on Intelligent Tutoring Systems (ITS-2004), Maceio, Brazil, August 2004. 2004.

Koedinger, Kenneth; Baker, Ryan. Comparing Knowledge Representations and Methods for Creating Cognitive Models in Advanced Learning and Tutorial Systems. American Educational Research Association (2006). 2006.

Koedinger, Kenneth; Cunningham, Kyle; Skogsholm, Alida; Leber, Brett. An open repository and analysis tools for fine-grained, longitudinal learner data. 1st International Conference on Educational Data Mining, 2008. [full paper], 157-166. 2008.

Koedinger, Kenneth; Pavlik, Phillip; McLaren, Bruce; Aleven, Vincent. Is it Better to Give than to Receive? The Assistance Dilemma as a Fundamental Unsolved Problem in the Cognitive Science of Learning and Instruction. B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), 30th Annual Conference of the Cognitive Science Society (pp. 2155-2160). Austin, TX: Cognitive Science Society. 2008.

Kulkarni, Anagha; Callan, Jamie. Dictionary Definitions based Homograph Identification using a Generative Hierarchical Model. ACL-08: HLT, Short Papers (Companion Volume), 85-88, Columbus, OH, June 2008. Association for Computational Linguistics. 2008.

Kulkarni, Anagha; Callan, Jamie; Eskenazi, Maxine. Dictionary definitions: The Likes and the unlikes. SLaTE2007 Workshop on Speech and Language Technology in Education, Farmington, PA (October 2007). 2007.

Kulkarni, Anagha; Heilman, Michael; Eskenazi, Maxine; Callan, Jamie. Word Sense Disambiguation for Vocabulary Learning. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Kumar, Rohit; Gweon, Gahgene; Joshi; Cui; Rose, Carolyn. Supporting students working together on math with social dialogue. SLaTE2007 Workshop on Speech and Language Technology in Education, Farmington, PA (October 2007). 2007.

Kumar, Rohit; Rose, Carolyn; Aleven, Vincent; Iglesias, Ana; Robinson, Allen. Evaluating the Effectiveness of Tutorial Dialogue Instruction in an Exploratory Learning Context; Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. . 2006.

Kumar, Rohit; Rose, Carolyn; Wang, Hao-Chuan; Joshi; Robinson. Tutorial Dialogue as adaptive collaborative learning support. AIED 2007 (nominated for a best paper award from one reviewer). 2007.

Lane, H. Chad; VanLehn, Kurt. A dialogue-based tutoring system for beginning programming. V. Barr & Z. Markov (Eds.), Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS) (pp. 449-454). Menlo Park, CA: AAAI Press. 2004.

Lane, H. Chad; VanLehn, Kurt. Intention-based scoring: An approach to measuring success at solving the composition problem. W. Dann, P. T. Tymann, & D. Baldwin (Eds.), 36th ACM Technical Symposium on Computer Science Education (SIGCSE).: ACM Press. P373-374. 2005.

Lee, Jong-Ki; Lee, Jang-Hyung. The effect of learning management system quality and self-regulated learning strategy on effectiveness of an e-Learning. E-Learning Conference, 2006, page 8. 2006.

Li, Junlei; Klahr, David; Jabbour, Amanda. When the Rubber Meets the Road -- Putting Research-based Methods to Test in Urban Classrooms. International Conference of the Learning Sciences (ICLS 2006). Bloomington, IN, USA. P. 418. 2006.

Litman, Diane; Rose, Carolyn; Forbes-Riley, Kate. Spoken versus typed human and computer dialogue tutoring. J. C. Lester, R. M. Vicari, & F. Paraguacu, (Eds.), Intelligent Tutoring Systems: 7th International Conference (pp. 368-379). Berlin: Springer-Verlag Berlin & Heidelberg GmbH & Co. K. 2004.

Liu, Ying. Chinese ESL Readers’ On-line Inferences in Text Processing. American Association for Applied Linguistics Conference, March, 2009. 2009.

Liu, Ying; Massaro; Chen; Chan; Perfetti, Charles. Using visual speech for training Chinese pronounciation: An in-vivo experiment. SLaTE2007 Workshop on Speech and Language Technology in Education, Farmington, PA (October 2007). 2007.

Liu, Ying; Wang, Min; Perfetti, Charles; Brubaker, Brian; Wu, Sue-mei; MacWhinney, Brian. Learning a tonal language by attending to the tone. 13th annual meeting of Society for the Scientific Study of Reading, Vancouver, July 5-8, 2006. 2006.

Liu, Ying; Wang, Min; Perfetti, Charles; Brubaker, Brian; Wu, Sue-mei; MacWhinney, Brian. Learning a tonal language by attending to the tone: An in-vivo experiment. 12th Biennial Conference for Research on Learning and Instruction, EARLI 2007, Aug 2007. Budapest, Hungary. Symposium title: Understanding robust learning via in vivo experimentation. 2007.

Lloyd; Heffernan, Neil; Ruiz. Predicting student engagement in intelligent tutoring systems using teacher expert knowledge. Workshop on Educational Data Mining (AIED 2007) 40-49. 2007.

Lu, Chan. The Effects of Word Knowledge Depth and Proficiency Level on Word Association for Learners of Chinese as a Second Language. The Annual Meeting of Chinese Language Teachers Association (CLTA/ ACTFL), 2006. 2006.

Lynch, Collin; Ashley, Kevin; Aleven, Vincent; Pinkwart, Niels. Defining Ill-Defined Domains; A literature survey. Workshop Proceedings on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems, 2006. 2006.

Lynch, Collin; Ashley, Kevin; Pinkwart, Niels; Aleven, Vincent. Argument diagramming as focusing device: does it scaffold reading. Workshop on Applications in Ill-Defined Domains (AIED 2007). 2007.

Lynch, Collin; Ashley, Kevin; Pinkwart, Niels; Aleven, Vincent. Argument graph classification with Genetic Programming and C4. 5. 1st International Conference on Educational Data Mining, 2008. [full paper]. 2008.

MacWhinney, Brian. Item-based Constructions and the Logical Problem. Second Workshop on Psychocomputational Models of Human Language Acquisition. 2005. Pages 53-68. 2005.

Makatchev, Maxim; Jordan, Pamela; Pappuswamy, Umarani; VanLehn, Kurt. Abductive proofs as models of qualitative reasoning. J. de Kleer & K. Forbus (Eds.), Workshop on Qualitative Reasoning (pp. 11-18). Evanston, IL . 2004.

Makatchev, Maxim; Jordan, Pamela; Pappuswamy, Umarani; VanLehn, Kurt. Abductive proofs as models of students’ reasoning about qualitative physics. Sixth International Conference on Cognitive Modeling (pp. 166-171). Mahwah, NJ: Erlbaum. 2004.

Makatchev, Maxim; Jordan, Pamela; VanLehn, Kurt. Modeling student’s reasoning about qualitative physics: Heuristics for abductive proof search. J. C. Lester, R. M. Vicari, & F. Paraguacu, (Eds.), Intelligent Tutoring Systems: 7th International Conference (pp. 699-709). Berlin: Springer-Verlag Berlin & Heidelberg GmbH & Co. K. 2004.

Makatchev, Maxim; Jordan, Pamela; VanLehn, Kurt. Mixed language processing in the Why2-Atlas tutoring system. Workshop on Mixed Language Explanations in Learning Environments, AIED2005. Amsterdam, Netherlands. 2005.

Makatchev, Maxim; VanLehn, Kurt. Analyzing completeness and correctness of utterances using an ATMS. G. McCalla, C. K. Looi, B. Bredeweg & J. Breuker (Eds.), Artificial Intelligence in Education (pp. 403-410). Amsterdam, Netherlands: IOS Press. 2005.

Makatchev, Maxim; VanLehn, Kurt. Combining Bayesian networks and formal reasoning for semantic classification of student utterances. 13th International Conference on Artificial Intelligence in Education (AIED-07). 2007.

Makatchev, Maxim; VanLehn, Kurt; Jordan, Pamela; Pappuswamy, Umarani. Representation and reasoning for deeper natural language understanding in a physics tutoring system. G. Sutcliffe & R. Goebel (Eds.), 19th International FLAIRS conference. Menlo Park, CA: AAAI Press, 682-687. 2006.

Martin, Brent; Koedinger, Kenneth; Mitrovic, Antonija; Mathan, Santosh. On Using Learning Curves to Evaluate ITS . 12th International Conference on Artificial Intelligence in Education. 2005. 2005.

Massaro, Dominic; Liu, Ying; Chen, Trevor; Perfetti, Charles. A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. 9th International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP), September, Pittsburgh, PA. 825-828. 2006.

Mathan; Koedinger, Kenneth. Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring. American Educational Research Association. 2006.

Matsuda, Noboru; Cohen, William; Koedinger, Kenneth. Applying Programming by Demonstration in an Intelligent Authoring Tool for Cognitive Tutors. AAAI Workshop on Human Comprehensible Machine Learning (Technical Report WS-05-04). 2005. Pages 1-8. 2005.

Matsuda, Noboru; Cohen, William; Koedinger, Kenneth. An Intelligent Authoring System with Programming by Demonstration. the Japan National Conference on Information and Systems in Education, Kanazawa, Japan. 2005.

Matsuda, Noboru; Cohen, William; Lacerda, Gustavo; Koedinger, Kenneth. Predicting students’ performance with SimStudent that learns cognitive skills from observation. 13th International Conference on Artificial Intelligence in Education (AIED-07). 2007.

Matsuda, Noboru; Cohen, William; Sewall, Jonathan; Lacerda, Gustavo; Koedinger, Kenneth. Evaluating a simulated student using real students’ data for training and testing. International Conference on User Modeling, Corfu, Greece, 2007. 2007.

Matsuda, Noboru; Cohen, William; Sewall, Jonathan; Lacerda, Gustavo; Koedinger, Kenneth. Why tutored problem solving may be better than example study. B. P. Woolf, E. Aimeur, R. Nkambou & S. Lajoie (Eds.), International Conference on Intelligent Tutoring Systems (pp. 111-121). Heidelberg, Berlin: Springer. 2008.

Matsuda, Noboru; Lee, Andrew; Cohen, William; Koedinger, Kenneth. A Computational Model of How Learner Errors Arise from Weak Prior Knowledge. Annual Meeting of the Cognitive Science Society, 2009. to appear.

Matsuda, Noboru; VanLehn, Kurt. Advanced geometry tutor: An intelligent tutor that teaches proof-writing with construction. G. McCalla, C. K. Looi, B. Bredeweg & J. Breuker (Eds.), Artificial Intelligence in Education (pp.443-450). Amsterdam: IOS Press. 2005.

Matsuda, Noboru; VanLehn, Kurt. Advanced Geometry Tutor: An Intelligent Tutoring System for Proof-Writing with Construction. Japan National Conference on Information and Systems in Education. 2005. 2005.

McCormick, Dawn; O'Neill, M. Christine; Siskin, Claire Bradin. Serving three mistresses in CALL: Students, teachers, researchers. CALICO Symposium, Honolulu. 2006.

McCormick, Dawn; Vercellotti, Mary Lou. To Err is Human, to Self-correct Divine: Examining Classroom Recorded Speaking Activity Data to Support ESL Self-correction as Noticing. American Association for Applied Linguistics Conference, March 2009. 2009.

McLaren, Bruce; Bollen, Lars; Walker, Erin; Harrer, Andreas; Sewall, Jonathan. Cognitive Tutoring of Collaboration: Developmental and Empirical Steps Towards Realization. Computer Supported Collaborative Learning Conference. 2005. 2005.

McLaren, Bruce; Koedinger, Kenneth; Schneider, Michael; Harrer, Andreas; Bollen, Lars. Toward Cognitive Tutoring in a Collaborative, Web-Based Environment. the Workshop on Adaptive Hypermedia and Collaborative Web-Based Systems (AHCW-04), Munich, Germany, July 2004. 2004.

McLaren, Bruce; Koedinger, Kenneth; Schneider, Michael; Harrer, Andreas; Bollen, Lars. Bootstrapping Novice Data: Semi-Automated Tutor Authoring Using Student Log Files. Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes, Seventh International Conference on Intelligent Tutoring Systems (ITS-2004), Maceio, Brazil, August 2004. 2004.

McLaren, Bruce; Lim, Sung-Joo; Gagnon, France; Yaron, David; Koedinger, Kenneth. Studying the Effects of Personalized Language and Worked Examples in the Context of a Web-Based IntelligentTutor. M. Ikeda, K.D. Ashley, Kevin, & T-W. Chan (Eds), 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Lecture Notes in Computer Science, 4053 (pp. 318-328). Berlin: Springer. (Finalist for the Best Paper Award). 2006.

McLaren, Bruce; Lim, Sung-Joo; Yaron, David; Koedinger, Kenneth. Can a Polite Intelligent Tutoring System Lead to Improved Learning Outside of the Lab? In R. Luckin, K.R. Koedinger, Kenneth, & J. Greer (Eds). 13th International Conference on Artificial Intelligence in Education (AIED 2007), IOS Press, (p. 443-440). 2007.

McLaren, Bruce; Lim; Koedinger, Kenneth. When and How Often Should Problem Solutions be given to Students? New Results and a Summary of the Current State of Research. B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), 30th Annual Conference of the Cognitive Science Society (pp. 2176-2181). Austin, TX: Cognitive Science Society. 2008.

"McLaren, Bruce; Lim; Koedinger, Kenneth. When is assistance helpful to learning? Results in combining worked examples and intelligent tutoring. B. Woolf, E. Aimeur, R. Nkambou, S. Lajoie (Eds), 9th International Conference on Intelligent Tutoring Systems (ITS-08), Lecture Notes in Computer Science, 5091 (pp. 677-680). Berlin: Springer.

. 2008."

McLaren, Bruce; Roll, Ido; Aleven, Vincent; Koedinger, Kenneth. Modeling and tutoring help seeking with a cognitive tutor. 12th Biennial Conference for Research on Learning and Instruction (EARLI). Budapest, Hungary, August, 2007. 2007.

McLaren, Bruce; Rummel, Nikol; Pinkwart, Niels; Tsovaltzi, Dimitra; Harrer, Andreas; Scheuer, Oliver. Learning Chemistry through Collaboration: A Wizard-of-Oz Study of Adaptive Collaboration Support. the Workshop on Intelligent Support for Exploratory Environments (ISSE 08) at the European Conference on Technology Enhanced Learning (EC-TEL 2008), Maastricht, the Netherlands, September 17, 2008. 2008.

McLaren, Bruce; Rummel, Nikol; Tsovaltzi, Dimitra; Braun, Isabel; Scheurer, Oliver; Harrer, Andreas; Pinkwart, Niels. The CoChemEx Project: Conceptual chemistry learning through experiment ation and adaptive collaboration. Workshop on ‘Emerging Technologies for Inquiry Based Learning in Science’, AIED, pp. 36-48. 2007.

McLaren, Bruce; Scheuer, Oliver; DeLaat, Maarten; Hever, Rakheli; DeGroot, Reuma; Rose, Carolyn. Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions. the 13th International Conference on Artificial Intelligence in Education (AIED 2007), IOS Press, (p. 141-147). 2007.

Mostow, Jack; Beck, Joseph. Refined micro-analysis of fluency gains in a Reading Tutor that listens: Wide reading beats rereading -- but not by much. Thirteenth Annual Meeting Society for the Scientific Study. 2006.

Mostow, Jack; Beck, Joseph; Cen, Hao; Cuneo; Gouvea. An Educational Data Mining Tool to Browse Tutor-Student Interactions: Time Will Tell!. Workshop on Educational Data Mining at AAAI Conference. 2005. 2005.

Mostow, Jack; Zhang, Xiaonan. Analytic comparison of three methods to evaluate tutorial behaviors. 1st International Conference on Educational Data Mining, 2008. [full paper]. 2008.

Murray, M. Charles; VanLehn, Kurt. Effects of dissuading unnecessary help requests while providing proactive help. G. McCalla, C. K. Looi, B. Bredeweg & J. Breuker (Eds.), Artificial Intelligence in Education (pp. 887-889). Amsterdam, Netherlands: IOS Press. 2005.

Murray, R. Charles; Mostow, Jack. A Comparison of Decision-Theoretic, Fixed-Policy and Random Tutorial Action Selection. 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. p 114-123. 2006.

Nokes, Timothy; Ross, Brian. Near-Miss versus surface-different comparisons in analogical learning and generalization. 29th Annual Meeting of the Cognitive Science Society. (CogScie 2007). 2007.

Nuzzo-Jones, Goss; Walonoski, Jason; Heffernan, Neil; Livak, Thomas. The eXtensible Tutor Architecture: A New Foundation for ITS. 12th Annual Conference on Artificial Intelligence in Education. 2005. 2005.

Nwaigwe, Adaeze; Koedinger, Kenneth; VanLehn, Kurt; Hausmann, Robert; Weinstein, Anders. Exploring Alternative Methods for Error Attribution in Learning Curves Analysis in Intelligent Tutoring Systems. International Conference on Artificial Intelligence in Education 2007. 2007.

Ogan, Amy; Aleven, Vincent; Jones, Christopher. Improving Intercultural Competence by Predicting in French Film. G. Richards (Ed.), World Conference on E-Learning in Corporate, Government, Healthcar. 2005. Pages 3101-3106. 2005.

Ogan, Amy; Aleven, Vincent; Jones, Christopher. Culture in the Classroom: Challenges for Assessment in Ill-Defined Domains. Workshop Proceedings on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems, 2006. 2006.

Ogan, Amy; Aleven, Vincent; Jones, Christopher. Pause, predict and ponder: Use of narrative videos to improve cultural discussion and learning. M. Czerwinski, A.M. Lund & D.S. Tan (Eds), 2008 Conferecne on Human Factors in Computing Systems, CHI 2008, Florence Italy. 2008.

Ogan, Amy; Jones, Christopher; Aleven, Vincent. Focusing attention on critical moments: Evaluation of a system for teaching intercultural competence. European Computer Assisted Language Learning. 2006.

Ogan, Amy; Jones, Christopher; Aleven, Vincent. Intelligent Tutoring in a Cultural Discussion Forum. European Computer Assisted Language Learning (EuroCALL 2007) Ulster, Northern Ireland, September 2007. 2007.

Ogan, Amy; Jones, Christopher; Aleven, Vincent; Walker, Erin; Wylie, Ruth; Jones, Christopher. A Tense Situation: Applying Cognitive Tutor Methodology to Ill-Defined Domains. European Computer Assisted Language Learning 2006. 2006.

Ogan, Amy; Walker, Erin; Aleven, Vincent; Jones, Christopher. Using a Peer Moderator to Support Collaborative Cultural Discussion. Appeared in the Culturally Aware Tutoring Systems Workshop at ITS 2008. 2008.

Ogan, Amy; Walker, Erin; Jones, Christopher; Aleven, Vincent. Toward supporting collaborative discussion in an ill-defined domain. B.P. Woolf, E. Aimeur, R. Nkambou & S.P. Lajoie, (Eds.), 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). Springer Lecture Notes in Computer Science, 825-827. 2008.

Ogan, Amy; Wylie, Ruth; Walker, Erin. Defining the ill-defined: Modeling student behaviour in making aspectual distinctions. 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. 2006.

Ogan, Amy; Wylie, Ruth; Walker, Erin. The challenges in adapting traditional techniques for modeling student behaviors in ill-defined domains. Workshop Proceedings on Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. 2006.

Pappuswamy, Umarani; Bhembe, Dumiszewe; Jordan, Pamela; VanLehn, Kurt. A supervised clustering method for text classification. A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing: 6th International Conference, CICLing: Vol. 3406. (pp. 692-702). Berlin: Springer-Verlag Berlin & Heidelberg GmbH & Co. K. 2005.

Pappuswamy, Umarani; Bhembe, Dumiszewe; Jordan, Pamela; VanLehn, Kurt. A multi-tier NL-knowledge clustering for classifying students’ essays. I. Russell & Z. Markov (Eds.), Eighteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS05) (pp. 566-571). Menlo Park, CA: AAAI Press. 2005.

Pappuswamy, Umarani; Jordan, Pamela; VanLehn, Kurt. Resolving Discourse Deictic Anaphors in Tutorial Dialogues. C. Sassen, A. Benz, & P. Kühnlein (Eds.), Constraints in Discourse (pp. 96-103). Dortmund University, Germany. 2005.

Pavlik, Phillip. Understanding the effectiveness of direct instruction methods. 24th Annual Meeting of the California Association for Behavior Analysis, Burlingame, CA. 2006.

Pavlik, Phillip. Transfer effects in Chinese vocabulary learning. R. Sun (Ed.), Twenty-Eighth Annual Conference of the Cognitive Science Society (pp. 2579). Mahwah, NJ: Lawrence Erlbaum. 2006.

Pavlik, Phillip. Understanding why practice should be fast and accurate. 33rd Annual Meeting of the Association for Behavior Analysis, San Diego, CA. (May, 2007). 2007.

Pavlik, Phillip; Bolster, Thomas; Wu, Sue-Mei; Koedinger, Kenneth; MacWhinney, Brian. Using optimally selected drill practice to train basic facts. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Pavlik, Phillip; Cen, Hao; Koedinger, Kenneth. Performance Factors Analysis - A New Alternative to Knowledge Tracing. 14th International Conference on Artificial intelligence in Education (AIED), July 6-10, 2009, Brighton, England. to appear.

Pavlik, Phillip; Cen, Hao; Koedinger, Kenneth. Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models. 2nd International Conference on Educational Data Mining (EDM 2009), Cordoba, Spain, July 1-3, 2009. to appear.

Pavlik, Phillip; Cen, Hao; Wu, Lili; Koedinger, Kenneth. Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor. R. S. J. d. Baker & J. E. Beck (Eds.), 1st Annual Educational Datamining Conference, 2008. [full paper], 77-86. 2008.

Pavlik, Phillip; Presson, Nora; Dozzi, Giancarlo; Wu, Sue-Mei; MacWhinney, Brian; Koedinger, Kenneth. The FaCT (Fact and Concept Training) System: A new tool linking cognitive science with educators. D.s. McNamara & J.G. Trafton (Eds.), 29th Annual Meeting of the Cognitive Science Society, Austin, TX: Cognitive Science Society, 1379-1384. 2007.

Pavlik, Phillip; Presson, Nora; Koedinger, Kenneth. Optimizing knowledge component learning using a dynamic structural model of practice. R. Lewis & T. Polk (Eds.), Eigth International Conference of Cognitive Modeling. Ann Arbor: University of Michigan, 47-52. 2007.

Perfetti, Charles. Instructional interventions based on theory-targeted learning: Examples from second language learning. Society for Research on Educational Effectiveness, SREE, Washington, D.C. February. 2009.

Pinkwart, Niels; Aleven, Vincent; Ashley, Kevin; Lynch, Collin. Toward Legal Argument Instruction with Graph Grammars and Collaborative Filtering Techniques. 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, 227-236. 2006.

Pinkwart, Niels; Ashley, Kevin; Aleven, Vincent; Lynch, Collin. Graph Grammars: An ITS Technology for diagram representations. 21st International FLAIRS Conference, May 15-17, 2008, Coconut Grove, Florida. 2008.

Pinkwart, Niels; Lynch, Collin; Ashley, Kevin; Aleven, Vincent. Re-evaluating LARGO in the classroom: Are diagrams better than text for teaching argumentation skill. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Pino, Juan; Eskenazi, Maxine. Measuring hint level in open cloze questions. FLAIRS 2009. 2009.

Pino, Juan; Heilman, Michael; Eskenazi, Maxine. A Selection Strategy to Improve Cloze Question Quality. Workshop on Intelligent Tutoring Systems for Ill-Defined Domains. 9th International Conference on Intelligent Tutoring Systems, 22-34. 2008.

Prata, David; Baker, Ryan; Costa, Evandro; Rose, Carolyn; Cui, Yue. Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments. 2nd International Conference on Educational Data Mining (EDM 2009), Cordoba, Spain, July 1-3, 2009. to appear.

Presson, Nora. An adaptive tutor for explicit instruction of French grammatical gender cues. The Nature and Development of L2 French, Southampton, UK. 2008.

Presson, Nora; MacWhinney, Brian. Explicitness and category breadth improve grammar learning and generalization. Paper 7th International Symposium on Bilingualism, Utrecht, Netherlands. to appear.

Rau, Martina; Aleven, Vincent; Rummel, Nikol, Tunc-Pekkan, Zelha; Pacilio, Laura. How to schedule multiple graphical representations? A classroom experiment with an intelligent tutoring system for fractions. Under review.

Rau, Martina; Aleven, Vincent; Rummel, Nikol. Blocked versus Interleaved Practice With Multiple Representations in an Intelligent Tutoring System for Fractions. 10th International Conference of Intelligent Tutoring Systems (ITS), 413-422. 2010.

Rau, Martina; Aleven, Vincent; Rummel, Nikol. Intelligent Tutoring Systems with Multiple Representations and Self-Explanation Prompts Support Learning of Fractions. 14th International Conference on Artificial intelligence in Education (AIED), 441-448. 2009.

Razzaq, Leena; Feng, Mingyu; Nuzzo-Jones, Goss; Heffernan, Neil; Koedinger, Kenneth. Blending Assessment and Instructional Assisting. 12th Artificial Intelligence in Education (AIED) Confernce, 2005. Pages 555-562. 2005.

Reichle, Erik; Tokowicz, Natasha; Liu, Ying; Perfetti, Charles. Using ERP to Examine When the Eyes Move During Reading Thirteenth Annual Meeting Society for the Scientific Study of Reading. July 5-8, 2006. Vancouver, Canada. 2006.

Renkl, Alexander; Schwonke, Rolf; Wittwer, Jorg; Krieg; Aleven, Vincent; Salden, Ron. Faded worked-out examples in an intelligent tutoring system: Do they further improve learning? Paper presented at the 12th Biennial Conference for Research on Learning and Instruction (EARLI). Budapest, Hungary, August, 2007. 2007.

Resnick, Lauren. How (Well Structured) Talk Builds the Mind. National Academies Eighth Olympiad of the Mind Symposium, Washington, DC. 2007.

Resnick, Lauren, Lauren; Leinhardt, Gaea; Petrosky, Anthony. Disciplinary literacy: Cognitive apprenticeship for secondary school teachers and students. 2007 meeting of the American Educational Research Association, Chicago, IL. 2007.

Ringenberg, Michael. A Student model based on Item Response Theory for a tutorial dialogue agent. AIED2007, Young Researchers Track. 2007.

Ringenberg, Michael; VanLehn, Kurt. Scaffolding Problem Solving with Annotated Worked-Out Examples to Promote Deep Learning. Intelligent Tutoring Systems: Eighth International Conference (ITS 2006), Jhongli, Taiwan. Springer-Verlag Lecture Notes in Computer Science. pages 625-634. 2006.

Ringenberg, Michael; VanLehn, Kurt. Does solving ill-defined physics problems elicit more learning than conventional problem solving? In B. P. Woolf, E. Aimeur, R. Nkambou & S. Lajoie (Eds) Doctoral Consortium, Intelligent Tutoring Systems: 9th International Conference, ITS2008. 2008.

Rodrigo, Mercedes; Anglo; Sugay, Norma; Baker, Ryan. Use of Unsupervised Clustering to Characterize Learner Behaviors and Affective States while Using an Intelligent Tutoring System. International Conference on Computers in Education, 49-56. 2008.

Rodrigo, Mercedes; Baker, Ryan. Coarse-Grained Detection of Student Frustration in an Introductory Programming Course. Conference manuscript under review. submitted.

Rodrigo, Mercedes; Baker, Ryan; D'Mello, Sidney; Gonzalez, Celeste; Lagud, Maria; Lim, Sheryl; Macapanpan, Alexis; Pascua, Sheila; Santillano, Jerry; Sugay, Jessica; Tep, Sinath; Viehland, Norma. Comparing learners’ affect while using an intelligent tutoring system and a simulation problem solving game. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008), 40-49. 2008.

Rodrigo, Mercedes; Baker, Ryan; Jadud, Matthew; Amarra, Anna; Dy, Thomas; Espejo-Lahoz, Maria; Lim, Sheryl; Pascua, Sheila; Sugay, Jessica; Tabanao, Emily. Affective and Behavioral Predictors of Novice Programmer Achievement. Conference manuscript under review. submitted.

Rodrigo, Mercedes; Baker, Ryan; Lagud, Maria; Lim, Sheryl; Macapanpan, Alexis; Pascua, Sheila; Santillano, Jerry; Sevilla; Sugay, Norma; Tep; Viehland. Affect and Usage Choices in Simulation Problem Solving Environments. Artificial Intelligence in Education 2007, 145-152. 2007.

Rodrigo, Mercedes; Rebolledo-Mendez; Baker, Ryan; duBoulay; Sugay, Norma; Lim, Sheryl; Espejo-Lahoz; Luckin. The Effects of Motivational Modeling on Affect in an Intelligent Tutoring System. International Conference on Computers in Education. 2008.

Roll, Ido; Aleven, Vincent; Koedinger, Kenneth. Promoting Effective Help-Seeking Behavior Through Declarative Instruction. International Conference on Intelligent Tutoring Systems (ITS), 2004. Pages 857-859. 2004.

Roll, Ido; Aleven, Vincent; Koedinger, Kenneth. Instruments and challenges in assessing help-seeking knowledge and behavior. Workshop on Metacognition and Self-regulated Learning in Educational Technologies in conjunction with the 9th International Conference on Intelligent Tutoring Systems (ITS) 2008. Montreal, Canada. 2008.

Roll, Ido; Aleven, Vincent; McLaren, Bruce; Koedinger, Kenneth. Can help seeking be tutored? Searching for the secret sauce of metacognitive tutoring. R. Luckin, K.R. Koedinger, Kenneth, & J. Greer (Eds), International Conference on Artificial Intelligence in Education 2007. IOS Press. (p. 203-210). 2007.

Roll, Ido; Aleven, Vincent; McLaren, Bruce; Ryu, Eunjeong; Baker, Ryan; Koedinger, Kenneth. The Help Tutor: Does Metacognitive Feedback Improve Students’ Help-Seeking Actions, Skills and Learning? In M. Ikeda, K.D. Ashley, Kevin, & T-W. Chan (Eds), 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Lecture Notes in Computer Science, 4053 (pp. 360-369). Berlin: Springer. 2006.

Roll, Ido; Baker, Ryan; Aleven, Vincent; Koedinger, Kenneth. What goals do students have when choosing the actions they perform? Proceedings of the Sixth International Conference on Cognitive Modeling. 2004, 380-381. Mahwah, NJ: Lawrence Erlbaum. 2004.

Roll, Ido; Baker, Ryan; Aleven, Vincent; Koedinger, Kenneth. A Metacognitive ACT-R Model of Students' Learning Strategies in Intelligent Tutoring Systems. Seventh International Conference of Intelligent Tutoring Systems. 2004. Pages 854-856. Berlin: Springer-Verlag. 2004.

Roll, Ido; Baker, Ryan; Aleven, Vincent; McLaren, Bruce; Koedinger, Kenneth. Modeling Students’ Metacognitive Errors in Two Intelligent Tutoring Systems. L. Ardissono, P. Brna, & A. Mitrovic (Eds.), 10th International Conference on User Modeling (UM'2005) (pp. 379-388). Berlin: Springer-Verlag. 2005.

Roll, Ido; Ryu, Eunjeong; Sewall, Jonathan; Leber, Brett; McLaren, Bruce; Aleven, Vincent; Koedinger, Kenneth. Towards Teaching Metacognition: Supporting Spontaneous Self-Assessment. M. Ikeda, K.D. Ashley, Kevin, & T. W. Chan (Eds.). 8th International Conference on Intelligent Tutoring Systems, 738-740. Berlin: Springer Verlag. 2006.

Roll,Ido; Aleven, Vincent; Koedinger, Kenneth. Designing structured invention tasks to prepare for future learning [abstract]. 30th annual conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. 2008.

Roll,Ido; Aleven, Vincent; Koedinger, Kenneth. Helping Students Know ‘Further’ – Increasing Flexibility of Students’ Knowledge Using Symbolic Invention Tasks. Submitted to the annual conference of the Cognitive Science Society, 2009. submitted.

Rose, Carolyn. Facilitating reliable content analysis of corpus data with automatic and semi-automatic text classification technology. EPFL switzerland. 2005.

Rose, Carolyn; Aleven, Vincent; Carey, Regan; Robinson, Allen. A First Evaluation of the Instructional Value of Negotiable Problem Solving Goals on the Exploratory Learning Continuum . 12th International Conference on Artificial Intelligence in Education. 2005. 2005.

Rose, Carolyn; Donmez, Pinar. TagHelper: An application of text classification technology to automatic and semi-automatic modeling of collaborative learning interactions. AIED 2005 Workshop on Representing and Analyzing Collaborative Interactions: What works? When does it work? To what extent?. 2005.

Rose, Carolyn; Donmez, Pinar; Cohen, William; Koedinger, Kenneth; Heffernan, Neil. Automatic and Semi-Automatic Skill Coding With a View Towards Supporting On-Line Assessment. 12th International Conference on Artificial Intelligence in Education. 2005. 2005.

Rose, Carolyn; Pai, Carol; Arguello, Jaime. Enabling Non-linguists to Author Advanced Conversational Interfaces Easily, Proceedings of FLAIRS 05. . 2005.

"Rotaru, Mihai; Litman, Diane. The Utility of a Graphical Representation of Discourse Structure in Spoken Dialogue Systems. 45th Annual Meeting of the Association for Computational Linguistics (ACL), June, 2007

. 2007."

Rummel, Nikol; Diziol, Dejana; Spada, Hans. Förderung mathematischer Kompetenz durch kooperatives Lernen: Erweiterung eines intelligenten Tutorensystems [Promoting mathematical competency through collaborative learning: Extension of an intelligent tutoring system]. 5th Conference of the "Fachgruppe Medienpsychologie der Deutsche Gesellschaft für Psychologie" [German Psychological Association]. Dresden, Germany. 2007.

Rummel, Nikol; Diziol, Dejana; Spada, Hans. Analyzing the effects of scripted collaboration in a computer-supported learning environment by integrating multiple data sources. Annual Conference of the American Educational Research Association (AERA) 2008. New York City, NY, USA. 2008.

Rummel, Nikol; Diziol, Dejana; Spada, Hans; McLaren, Bruce. Scripting collaborative problem solving with the Cognitive Tutor Algebra: A Way to promote learning in mathematics. 12th meeting of the European Association for Research on Learning and Instruction (EARLI-07). Budapest, August 28 - September 1, 2007. 2007.

Rummel, Nikol; Diziol, Dejana; Spada, Hans; McLaren, Bruce; Walker, Erin; Koedinger, Kenneth. Flexible support for collaborative learning in the context of the Algebra I Cognitive Tutor. Workshop Seventh International Conference of the Learning Sciences (ICLS), Bloomington, IN, USA. 2006.

Rummel, Nikol; Hauser, Sabine; Spada, Hans. How does net-based interdisciplinary collaboration change with growing domain expertise? Proceedings of the Conference on Computer Supported Collaborative Learning (CSCL-07). Rutgers University. 2007.

Rummel, Nikol; Spada, Hans; Diziol, Dejana. Can collaborative extensions to the Algebra I Cognitive Tutor enhance robust learning? An in vivo experiment. Annual Conference of the American Educational Research Association (AERA-07). Chicago, IL, USA, April 2007. 2007.

Rummel, Nikol; Spada, Hans; Diziol, Dejana. Evaluating collaborative extensions to the Cognitive Tutor Algebra in an in vivo experiment: Lessons learned. European Association for Research on Learning and Instruction (EARLI-07). Budapest, August 28 - September 1, 2007. 2007.

Rummel, Nikol; Spada, Hans; Hauser, Sabine. Learning to collaborate in a computer-mediated setting: Observing a model beats learning from being scripted. Seventh International Conference of the Learning Sciences (ICLS). Bloomington, IN, USA., P. 634. 2006.

Salden, Ron; Aleven, Vincent; Renkl, Alexander; Schwonke, Rolf. Worked examples and tutored problem solving: Redundant or synergistic forms of support?. 30th Annual Meeting of the Cognitive Science Society, Washington DC, USA, July 2008, 589-594. 2008.

Salden, Ron; Aleven, Vincent; Renkl, Alexander; Schwonke, Rolf. Worked examples and the assistance dilemma. Abstract in Symposium: Confronting the Assistance Dilemma: Is it Better to Give Than Receive? (AERA 2008). 2008.

Salden, Ron; Aleven, Vincent; Renkl, Alexander; Schwonke, Rolf; Wittwer, Jorg; Kreig, Carmen. Does Learning from Examples Improve Tutored Problem Solving? Proceedings of the 28th Annual Meeting of the Cognitive Science Society, p. 2602. Poster. 2007.

Salden, Ron; Aleven, Vincent; Renkl, Alexander; Wittwer, Jorg. Does Learning from Examples Improve Tutored Problem Solving. 14th Biannual Conference of the European Association for Research on Learning and Instruction (EARLI), August 28-September 1, 2007, Budapest, Hungary. 2006.

Salden, Ron; Koedinger, Kenneth; Aleven, Vincent; McLaren, Bruce. Does Cognitive Load Theory Account for Beneficial Effects of Worked Examples in Tutored Problem Solving? Proceedings of the 3rd International Cognitive Load Theory Conference (CLT-09). Heerlen, the Netherlands, March 2-4, 2009. 2009.

Schwarz, B. B., Asterhan, C. S. C., Wang, C., Chiu, M. M., Ching, C. C., Walker, E. Koedinger, K., Rummel, N., & Baker, M. Adaptive human guidance of computer-mediated group work. To appear in the Proceedings of the 2010 International Conference of the Learning Sciences – ICLS 2010. in press

Schwarz, Baruch Asterhan, Christa & Gil, Julia. Human guidance of synchronous e-discussions: The effects of different moderation scripts on peer argumentation. In C. O'Malley, D. Suthers, P. Reimann & A. Dimitracopoulou (Eds), Computer Supported Collaborative Learning Practices: CSCL2009 Conference Proceedings (pp. 497-506). 2009

Schwonke, Rolf; Ertelt, Anna; Renkl, Alexander; Aleven, Vincent; Salden, Ron. Reducing extraneous demands in learning from tutored problem solving and embedded worked examples. 3rd International Cognitive Load Theory Conference (CLT-09). Heerlen, the Netherlands, March 2-4, 2009. 2009.

Schwonke, Rolf; Wittwer, Jorg; Aleven, Vincent; Salden, Ron; Krieg, Carmen; Renkl, Alexander. Can tutored problem solving benefit from faded worked-out examples?. European Cognitive Science Conference, Delphi, Greece, May, 2007, (pp.59-64). 2007.

Shih, Benjamin; Koedinger, Kenneth; Scheines, Richard. A Response time model for bottom-out hints as worked examples. 1st International Conference on Educational Data Mining, 2008. [full paper]. 2008.

Singh, Ajit; Gordon, Geoffrey. Relational learning via collective matrix factorization. 14th Intl. Conf. on Knowledge Discovery and Data Mining (KDD), 2008. 2008.

Siskin, Claire. Revolution Templates for Language Learning (Courseware Showcase). CALICO Symposium, Honolulu. 2006.

Siskin, Claire. Revolution for Non-Programmers, or Yes, There Is Life After HyperCard!. NEALLT Conference, Philadelphia. 2006.

Siskin, Claire. Misconceptions, myths, and metaphors in CALL research. TESOL: CALL IS Acadmeic Session. 2006.

Siskin, Claire. Revolution for low-cost data collection in CALL. Computer Assisted Language Instruction Consortium Conference (CALICO). 2007.

Siskin, Claire; Asay, Devin. Rapid Creation of Internet-based Multimedia Applications without Brower Hassles. CALICO Symposium, Honolulu. 2006.

Tsovaltzi, Dimitra; McLaren, Bruce; Rummel, Nikol; Scheuer, Oliver; Harrer, Andreas; Pinkwart, Niels; Braun, Isabel. Using an Adaptive Collaboration Script to Promote Conceptual Chemistry Learning. B. Woolf, E. Aimeur, R. Nkambou, S. Lajoie (Eds), 9th International Conference on Intelligent Tutoring Systems (ITS-08), Lecture Notes in Computer Science, 5091 (pp. 709-711). Berlin: Springer. 2008.

Tsovaltzi, Dimitra; McLaren, Bruce; Rummel, Nikol; Scheuer, Oliver; Harrer, Andreas; Pinkwart, Niels; Braun, Isabel. CoChemEx: Supporting conceptual chemistry learning via computer-mediated collaboration scripts. P. Dillenbourg and M. Specht (Eds.), Third European Conference on Technology Enhanced Learning (EC-TEL 2008), Lecture Notes in Computer Science 5192 (pp. 437-448). Berlin: Springer. 2008.

Tunç-Pekkan, Zelha; Zeylikman, Lyubov; Aleven, Vincent; Rummel, Nikol. Fifth Graders’ Conception of Fractions on Numberline Representations. The annual meeting of North American Chapter of the International Group for the Psychology of Mathematics Education, Columbus, Ohio. 2010.

Tunc-Pekkan, Zelha; Rau, Martina; Aleven, Vincent; Rummel, Nikol. External Representations and Fractional Knowledge. Third Annual inter-Science of Learning Center (iSLC) Conference For Students and Postdoctoral Fellows at the Science of Learning Centers, Boston, MA. 2010.

Turner, Terence; Macasek, Michael; Nuzzo-Jones, Goss; Heffernan, Neil; Koedinger, Kenneth. The Assistment Builder: A Rapid Development Tool for ITS. 12th International Conference on Artificial Intelligence in Education. 2005. 2005.

van de Sande, Brett; Hausmann, Robert. An Analysis of Student Learning Using the Andes Homework System. AAPT Summer Meeting, Greensboro, NC, July 2007. 2007.

van de Sande, Brett; Hausmann, Robert. Does an intelligent tutor homework system encourage beneficial collaboration. Central Pennsylvania Section of the American Association of Physics Teachers (CPS/AAPT), April, 2008, Lock Haven University of Pennsylvania, Lock Haven, PA. 2008.

van de Sande, Brett; Hausmann, Robert. Does an intelligent tutor homework system encourage beneficial collaboration. joint Spring Meeting of the Ohio Section of the American Physical Society (OS/APS) and the Western Pennsylvania American Association of Physics Teachers (WPA/AAPT), March 2008, Youngstown State University, Ohio. 2008.

van de Sande, Brett; Hausmann, Robert. Does an intelligent tutor homework system encourage beneficial collaboration?. the winter meeting of the American Association of Physics Teachers (AAPT), Baltimore, MD. 2008.

van de Sande, Brett; Shelby; Treacy, Don; VanLehn, Kurt; Wintersgill, Mary. Andes: An Intelligent Tutor for Introductory Physics Homework. AAPT Summer Meeting, Syracuse NY. 2006.

van de Sande, Brett; VanLehn, Kurt; Hausmann, Robert; Treacy, Don; Shelby. Andes: An Intelligent Homework System for Introductory Physics. winter meeting of the American Association of Physics Teachers, Seattle, WA. 2007.

VanLehn, Kurt. Explaining the assistance/load/difficulty duality in terms of meta-cognitive learning strategies. Abstract in Symposium: Confronting the Asssistance Dilemma: Is it Better to Give Than Receive? (AERA 2008). 2008.

VanLehn, Kurt; Bhembe, Dumiszewe; Min Chi; Lynch, Collin; Schulze, Kay; Shelby, Robert; Taylor, Linwood. Implicit versus explicit learning of strategies in a non-procedural cognitive skill. J. C. Lester, R. M. Vicari, & F. Paraguacu, (Eds.), Intelligent Tutoring Systems: 7th International Conference (pp. 521-530). Berlin: Springer-Verlag Berlin & Heidelberg GmbH & Co. K. 2004.

VanLehn, Kurt; Jordan, Pamela. When is tutorial dialogue more effective than less interactive instruction. Abstract in Symposium: Intelligent Tutoring Systems: What Do We Do Next? (AERA, 2008). 2008.

VanLehn, Kurt; Jordan, Pamela; Litman, Diane. Developing pedagogically effective tutorial dialogue tactics: Experiments and a testbed. SLaTE2007 Workshop on Speech and Language Technology in Education, Farmington, PA (October 2007). 2007.

VanLehn, Kurt; Lynch, Collin; Schulze, Kay; Shapiro, Jay; Shelby, Robert; Taylor, Linwood; Treacy, Don; Weinstein, Anders; Wintersgill, Mary. The Andes physics tutoring system: Five years of evaluations. G. McCalla, C. K. Looi, B. Bredeweg & J. Breuker (Eds.), Artificial Intelligence in Education. (pp. 678-685) Amsterdam, Netherlands: IOS Press. Winner of a Best Paper Award of this conference. 2005.

Walker, Erin. Mutual Peer Tutoring: A Collaborative Addition to the Cognitive Tutor: Algebra-1. C-K. Looi et al. (Eds.). 12th International Conference on Artificial Intelligence in Education, p. 979. IOS Press, 2005. 2005.

Walker, Erin; Koedinger, Kenneth; McLaren, Bruce; Rummel, Nikol. Cognitive Tutors as Research Platforms: Extending an Established Tutoring System for Collaborative and Metacognitive Experimentation. M. Ikeda, K.D. Ashley, Kevin, & T-W. Chan (Eds), 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Lecture Notes in Computer Science, 4053 (pp. 207-216). Berlin: Springer. 2006.

Walker, Erin; McLaren, Bruce; Rummel, Nikol; Koedinger, Kenneth. Who says three's a crowd? Using a cognitive tutor to support peer tutoring. R. Luckin, K.R. Koedinger, Kenneth, & J. Greer (Eds), 13th International Conference on Artificial Intelligence and Education. 2007. IOS Press. (pp. 399-406). 2007.

Walker, Erin; Ogan, Amy. Peer Moderation in Cultural Discussion Forums. European Computer Assisted Language Learning (EuroCALL 2007) Ulster, Northern Ireland, September 2007. 2007.

Walker, Erin; Ogan, Amy; Jones, Christopher; Aleven, Vincent. Two Approaches for Providing Adaptive Support in an Ill-Defined Domain. Proceedings of the "Intelligent Tutoring Systems for Ill-Defined Domains: Assessment and Feedback in Ill-Defined Domains" Workshop. 9th International Conference on Intelligent Tutoring Systems (ITS) 2008. 2008.

Walker, Erin; Ogan, Amy; Wylie, Ruth. A Tense Situation: Applying Cognitive Tutor Methodology to Ill-Defined Domains. European Computer Assisted Language Learning (EuroCALL 2006) Granada, Spain, September 2006. 2006.

Walker, Erin; Rummel, Nikol; Koedinger, Kenneth. Adaptive Domain Support for Computer-Mediated Peer Tutoring. Appeared in ICLS 2008 as part of the symposium New Challenges in CSCL: Towards adaptive script support, edited by Nikol Rummel, Nikol and Armin Weinberger. 2008.

Walker, Erin; Rummel, Nikol; Koedinger, Kenneth. To tutor the tutor: Adaptive domain support for peer tutoring. B.P. Woolf, E. Aimeur, R Nkambou, and S.P. Lajoie, (Eds.), 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008), Springer Lecture Notes in Computer Science, 626-635. 2008.

Walker, Erin; Rummel, Nikol; Koedinger, Kenneth. Modeling Helping Behavior in an Intelligent Tutor for Peer Tutoring. 14th International Conference on Artificial intelligence in Education (AIED), July 6-10, 2009, Brighton, England. 2009.

Walker, Erin; Rummel, Nikol; Koedinger, Kenneth. Beyond Explicit Feedback: New Directions in Adaptive Collaborative Learning Support. Short Conference on Computer Supported Collaborative Learning (CSCL-09). to appear.

Walker, Erin; Rummel, Nikol; Koedinger, Kenneth. The influence of correct and erroneous worked examples on learning from peer tutoring. To appear in EARLI 2009 as part of the symposium: Vivo experimentation on worked examples across domains. to appear.

Walker, Erin; Rummel, Nikol; McLaren, Bruce; Koedinger, Kenneth. The student becomes the master: Integrating peer tutoring with cognitive tutoring. C.A. Chinn, G. Erkens & S. Puntambekar (Eds.) Conference on Computer Supported Collaborative Learning (CSCL-07), Vol. 8, pp. 750-752. International Society of the Learning Sciences, Inc. ISSN 1819-0146. 2007.

Walonoski, Jason; Heffernan, Neil. Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems. 8th International Conference on Intelligent Tutoring Systems (ITS-2006), Jhongli, Taiwan, June 26-30, 2006. 2006.

Wang, Hao-Chuan; Kumar, Rohit; Rose, Carolyn; Li; Chang. A Hybrid Ontology Directed Feedback Generation Algorithm for Supporting Creative Problem Solving Dialogues. International Joint Conference on Artificial Intelligence. 2007.

Wang, Hao-Chuan; Rose, Carolyn. A Process analysis of idea generation and failure. 29th Annual Meeting of the Cognitive Science Society. (CogSci 2007). 2007.

Wang, Hao-Chuan; Rose, Carolyn; Cui, Yue; Chang, Chun-Yen; Huang, Chun-Chieh; Li, Tsai-Yen. Thinking Hard Together: the Long and Short of Collaborative Idea Generation in Scientific Inquiry. Conference on Computer Supported Collaborative Learning (CSCL-07). Rutgers University. 2007.

Wang, Hao-Chuan; Rose, Carolyn; Li; Chang. Providing Support for Creative Group Brainstorming: Taxonomy and Technologies. Workshop Proceedings on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems, 2006, pp 74-82. 2006.

Wittwer, Jorg; Renkl, Alexander. Do instructional explanations foster learning from worked-out examples? A cognitive load perspective. 3rd International Cognitive Load Theory Conference (CLT-09). Heerlen, the Netherlands, March 2-4, 2009. 2009.

Wu, Sue-Mei. "Chinese Online Module: A Cognitive Language Learning Infrastructure". The Annual Meeting of Chinese Language Teachers Association (CLTA/ ACTFL), November 18-20, 2005, Baltimore, Maryland. 2005.

Wu, Sue-Mei. Interdisciplinary Collaboration for Chinese as a Foreign Language: Running In-Vivo Learning Experiments in Chinese Language Courses. CLTA/ACTFL), Nashville, Tennessee. 2006.

Wu, Sue-Mei. Chinese Cognitive CALL Environment Design: Content and Exercises. Fourth International Conference and Workshops on Technology and Chinese Language Teaching (TCLT4). University of Southern California, Los Angeles. 2006.

Wu, Sue-Mei. Chinese Online: A Hybrid Experience. 5th International Conference and Workshops on Technology and Chinese Teaching in the 21st Century (TCLT5). pp. 296-302. Macau: University of Macau. 2008.

Wu, Sue-Mei; Haney, Mark. Robust Chinese E-learning: Integrating the 5 Cs Principles with Content and Technology. 4th International Conference on Internet Chinese Education. 2005. 2005.

Wu, Sue-Mei; Haney, Mark. Empowering Online Language Learning: The Chinese LearnLab in the Pittsburgh Science of Learning Center. Annual Symposium of Computer Assisted Language Instruction Consortium (CALICO 2006). University of Hawaii. 2006.

Wylie, Ruth. Are we asking the right questions? Understanding which tasks lead to the robust learning of English grammar. Young Researchers Track paper at the 13th International Conference on Artificial Intelligence in Education (2007). 2007.

Wylie, Ruth; Koedinger, Kenneth; Mitamura, Teruko. Practice makes Perfect? Structuring Practice Opportunities for Learning in an ESL Grammar Tutor. Computer Assisted Language Instruction Consortium (CALICO). March 10-14, 2009. 2009.

Wylie, Ruth; Koedinger, Kenneth; Mitamura, Teruko. Is Self-Explanation Always Better? The Effects of Adding Self-Explanation Prompts to an English Grammar Tutor. Cognitive Science. July 29 - August 1, 2009. to appear.

Wylie, Ruth; Koedinger, Kenneth; Mitamura, Teruko. Self-Explaining Language: Effects of Adding Self-Explanation Prompts to an ESL Grammar Tutor. European Association for Research on Learning and Instruction (EARLI), August 25-29, 2009. to appear.

Wylie, Ruth; Mitamura, Teruko; Rankin, James; Koedinger, Kenneth. Developing Tutoring Systems for Classroom and Research Use: A Look at Two English Article Tutors. Computer Assisted Language Instruction Consortium Conference (CALICO). 2007.

Wylie, Ruth; Mitamura, Teruko; Rankin; Koedinger, Kenneth. Doing more than Teaching Students: Opportunities for CALL in the Learning Sciences. SLaTE Workshop on Speech and Language Technology in Education. Farmington, Pennsylvania. October 1-3, 2007. 2007.

Yang, Chin-Lung; Perfetti, Charles. Reading skill and the acquisition of high quality representations for new words. Thirteenth Annual Meeting Society for the Scientific Study of Reading, Vanncouver, Canada. 2006.

Yaron, David. The ChemCollective: Virtual Labs and Scenario-Based Learning for Introductory Chemistry . Nineteenth Biennial Conference on Chemical Education in West Lafayette, Indiana, p 621. 2006.

Yaron, David. Digital libraries to support problem solving and conceptual learning in introductory chemistry. Gordon Conference for Physics Research and Education, June, 2008, Smithfield, RI. 2008.

Yaron, David; Cuadros, Jordi; Karabinos, Michael; Leinhardt, Gaea; Evans, Karen. Virtual Laboratories and Scenes to Support Chemistry Instruction. About Invention and Impact: Building Excellence in Undergraduate STEM (Science, Technology, Engineering, and Mathematics) Education, Proceedings from National Science Foundation Course, Curriculum, and Laboratory Improvement (NSF-CCLI) program conference, Arlington, Virginia, 2004, being edited and prepared by NSF. 2005.

Yaron, David; Cuadros, Jordi; Karabinos, Michaeletal. Using digital libraries to build educational communities: The ChemCollective. American Chemistry Society National Meeting, San Diego, March 2005. 2005.

Yaron, David; Davenport, Jodi; Karabinos, Michael; Leinhardt, Gaea; Bartolo; Portman; Sadoway; Carter; Ashe. Cross-disciplinary molecular science education in introductory science courses: An NSDL MatDL Collection. ACM/IEEE-CS Joint Conference on Digital Libraries, Pittsburgh, PA USA. Association for Computing Machinery, Inc. (ACM). 2008.

Yaron, David; Evans, Karen; Leinhardt, Gaea; Karabinos, Michaeletal. Using the field of chemistry to guide in the development of an on-line stoichiometry course. American Chemical Society National Meeting, Washington DC, August 2005. P 306?. 2005.

Yaron, David; Karabinos, Michael; Davenport, Jodi; Leinhardt, Gaea. Virtual lab activities for introductory chemistry. Biennial Conference on Chemical Education, Purdue University, West Layefette, IN. 2006.

Yaron, David; Leinhardt, Gaea; Evans, Karen; Cuadros, Jordi; Karabinos, Michael; McCueandDennis. Creation of an online stoichiometry course that melds scenario based learning with virtual labs and problem-solving tutors. CONFCHEM. Online Conference, Spring 2006. 2006.

Youngs, Bonnie. Ruminations of a hybrid course instructor. Computer Assisted Language Instruction Consortium Conference (CALICO), San Macos, TX. 2007.

Zhang, Xiaonan; Mostow, Jack; Beck, Joseph. All in the (word) family: Using learning decomposition to estimate transfer between skills in a reading tutor that listens. Workshop on Educational Data Mining (AIED 2007). 2007.

Zhang, Xiaonan; Mostow, Jack; Beck, Joseph. A Comparison of three methods to evaluate tutorial behaviors. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008). 2008.

Zhang, Yanhui. SLA research for empirically-driven innovations in CSL studies. American Council on the Teaching of Foreign Languages (ACTFL) Annual Meeting, 2007. 2007.

Baker, Ryan; Barnes, Tiffany; Beck, Joseph. Educational Data Mining 2008: 1st International Conference on Educational Data Mining, Proceedings. Montreal, Quebec, Canada. June 2008. 2008.

== Poster Presentations ==

Anthony, Lisa. Exploration of the Effects of Handwriting on Learning in Algebra Equation Solving. Human-Computer Interaction Institute 12th Anniversary, Carnegie Mellon University. 2006.

Anthony, Lisa. Exploration of the Effects of Handwriting on Learning in Algebra Equation Solving. Science of Learning Centers Symposium, Atlanta, Georgia. 2006.

Ayers, Elizabeth; Nugent, Rebecca; Dean, Nema. Skill set profile clustering based on weighted stuent responses. 1st International Conference on Educational Data Mining, 2008. [Poster-young researchers' track]. 2008.

Booth, Julie; Koedinger, Kenneth; Siegler, Robert. The effect of corrective and typical self-explanation on algebraic problem solving. Science of Learning Centers Awardee’s Meeting in Washington, DC, October, 2007. 2007.

Booth, Julie; Koedinger, Kenneth; Siegler, Robert. Using self-explanation to improve algebra learning. B.C. Love, K. McRae, & V.M. Sloutsky (Eds.), 30th Annual Cognitive Science Society, p. 2395. Jaustin, TX: Cognitive Science Society. [abstract]. 2008.

Booth, Julie; Olsen, Jennifer. Encoding of equation features relates to conceptual and procedural knowledge of algebra. meeting of the Society for Research in Child Development, Denver, CO. 2009.

Brown, Jonathan; Eskenazi, Maxine. Retrieval of Authentic Documents for Reader-Specific Lexical Practice. InSTIL/ICALL Symposium. 2004. 2004.

Butcher, Kirsten; Aleven, Vincent. Visual-verbal coordination: Diagram interaction promotes robust learning in geometry. Science of Learning Centers Annual Meeting, Arlington, VA. 2007.

Butcher, Kirsten; Aleven, Vincent. Concept training and deep knowledge assessment: Using CTAT in the classroom. Open Learning Interplay Symposium 2008, Carnegie Mellon University, Pittsburgh, PA. 2008.

Butcher, Kirsten; Bhushan, Sonal. Using strand maps to engage digital library users with science content (Poster presentation). 5th ACM/IEEE-CS joint conference on Digital libraries, p. 371. New York: Association for Computing Machinery. 2005.

Butcher, Kirsten; Bhushan, Sonal. Learning with scientific visualizations: Effects of background knowledge and interactivity. American Educational Research Association 2005. 2005.

Chi, Min; Jordan, Pamela; VanLehn, Kurt; Hall, Brian. Reinforcement learning-based feature selection for developing pedagogically effective tutorial dialogue tactics. 1st International Conference on Educational Data Mining, 2008. [best Poster-young researchers' track award]. 2008.

Corbett, Albert; Wagner, Angela; Chao, Chih-yu; Lesgold, Sharon; Steven, Scott; Ulrich, Harry. Student Question-Asking Behavior in a Classroom Evaluation of the ALPS Learning Environment. 12th Annual Conference on Artificial Intelligence in Education. 2005. 2005.

Davenport, Jodi; Klahr, David; Koedinger, Kenneth. The influence of external representations on chemistry problem solving. Forty-seventh Annual Meeting of the Psychonomic Society in Houston, Texas. November 2006. 2006.

Davenport, Jodi; Yaron, David; Klahr, David; Koedinger, Kenneth. Coordinating chemistry concepts with problem solving to enhance learning. Open Learning Interplay Symposium in Pittsburgh, PA, March 2008. 2008.

Davenport, Jodi; Yaron, David; Koedinger, Kenneth; Klahr, David. Development of Conceptual Understanding and Problem Solving Expertise in Chemistry. 30th Annual Meeting of the Cognitive Science Society, July 2008 [Poster]. 2008.

Diziol, Dejana; Rummel, Nikol; Spada, Hans. Introducing collaboration to the Algebra Cognitive Tutor: Differential effects on three robust learning measures. 1st Inter-Science of Learning Center Student and Post-Doc (iSLC) 2009. Pittsburgh, PA, USA. 2008.

Dunlap, Susan; Friedline, Benjamin; Juffs, Alan; Perfetti, Charles. Lexical quality of English second language learners: Effects of focused training on orthographic encoding skill. 2nd annual meeting of the iSLC, Seattle, Washington, February, 2009. 2009.

Dunlap, Susan; Liu, Ying; Chen; Perfetti, Charles. Classroom learners of Chinese as a second language: Testing online study methods. Pitt-CMU Conference, Pittsburgh Pennsylvania. 2005.

Dunlap, Susan; Perfetti, Charles. Effects of explicit instruction on Chinese character learning. Georgetown University Round Table on Languages and Linguistics, Washington, D.C., March 2009. 2009.

Dunlap, Susan; Perfetti, Charles; Liu, Ying; Wu, Sue-Mei. Rules and exceptions: Strategies for learning vocabulary in Chinese as a second language. meeting of the American Educational Research Association, Chicago, IL., 2007. 2007.

Feeney, Chrstine; Heilman, Michael. Automatically Generating and Validating Reading-Check Questions. Ninth International Conference on Intelligent Tutoring Systems. (June, 2008). Poster. 2008.

Feng, Mingyu; Heffernan, Neil; Beck, Joseph; Koedinger, Kenneth. Can we predict which groups of questions students will learn from? 1st International Conference on Educational Data Mining, 2008. [Poster-young researchers' track]. 2008.

Greeno, James; MacWhinney, Brian. Perspectives in reasoning about quantities. annual meeting of the Cognitive Science Society, Vancouver, BC. 2006.

Greeno, James; MacWhinney, Brian. Learning and cognition as perspective taking: Conceptual alignment in learning environments. International Conference of the Learning Sciences, Bloomington, IN. 2006.

Hausmann, Robert. The effect of generation on robust learning. annual meeting of the Science of Learning Centers, Washington, D.C. 2007.

Hausmann, Robert; Nokes, Timothy; VanLehn, Kurt; van de Sande, Brett. Collaborative dialogue while studying worked-out examples. International Conference on Artificial Intelligence in Education (AIED 2009), Brighton, England. 2009.

Hausmann, Robert; van de Sande, Brett; VanLehn, Kurt. The content of self-explanations while studying incomplete worked-out examples. 30th meeting of the Cognitive Science Society, Washington, DC., July 2008. 2008.

Hausmann, Robert; VanLehn, Kurt. Self-explaining in the classroom: Learning curve evidence. Physics Education Research Conference, Greensboro, NC. 2007.

Hausmann, Robert; VanLehn, Kurt. A test of the interaction hypoThesis: Joint-explaining vs. self-explaining. D. McNamara & G. Trafton (Eds.), 29th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum. 2007.

Hausmann, Robert; VanLehn, Kurt. A test of the interaction hypoThesis: Joint-explaining vs. self-explaining. Physics Education Research Conference, Greensboro, NC. 2007.

Heilman, Michael; Eskenazi, Maxine. Self-assessment in vocabulary tutoring. 9th International Conference on Intelligent Tutoring Systems (ITS) (June, 2008), 656-658. Springer Berlin/Heidelberg. 2008.

Heilman, Michael; Zhao, Le; Pino, Juan; Collins-Thompson, Kevyn; Callan, Jamie; Eskenazi, Maxine; Perfetti, Charles; Juffs, Alan. Providing Appropriate Texts for Language Learners. IES Research Conference (IES), 2008. 2008.

Juffs, Alan; Wilson, Lois; Eskenazi, Maxine; Callan, Jamie; Brown, Jonathan; Collins-Thompson, Kevyn; Heilman, Michael; Pelletreau, Timothy, Timothy; Sanders, James. Robust learning of vocabulary: investigating the relationship between learner behavior and the acquisition of vocabulary. 40th Annual TESOL International Conference, 2006. 2006.

Katz, Sandra. A Comparison of three modes of reflective dialogue. American Association of Physics Teachers (AAPT) meeting, 2006. 2006.

Katz, Sandra; Connelly, John; Wilson, Christine. Out of the lab and into the classroom: An Evaluation of Reflective Dialogue in Andes. Physics Education Research Conference (PERC 2007), Greensboro, NC. 2007.

Katz, Sandra; Connelly, John; Wilson, Christine; Goedde. Post-Practice Dialogues in an Intelligent Tutoring System for College-Level Physics. AAPT 2006. Poster. 2006.

Liu, Ying; Guan, Connie; Chan, Derek; Wu, Sue-Mei; Perfetti, Charles. Writing to foster reading in Chinese. Second Annual Inter-Science of Learning Center Student and Post-Doc Conference. University of Washington, Seattle, WA. February 5-7. 2009.

Liu, Ying; Guan; Chan; Wu, Sue-Mei; Perfetti, Charles. The Effects of Character-writing on Reading Skill Development: An Experiment in Chinese. Third International Conference on Cognitive Science, Moscow, Russia, June 20-26, 2008. 2008.

McLaren, Bruce; Walker, Erin; Koedinger, Ken; Rummel, Nikol; Spada, Hans; Kalchman, Mindy. Improving algebra learning and collaboration through collaborative extensions to the Algebra Cognitive Tutor. Conference on Computer Supported Collaborative Learning (CSCL-05), May 2005, Taipei, Taiwan. 2005.

Mostow, Jack; Beck, Joseph. What, How, and Why should Tutors Log?. 2nd International Conference on Educational Data Mining (EDM 2009), Cordoba, Spain, July 1-3, 2009. 2009.

Nokes, Timothy; VanLehn, Kurt. Bridging principles and examples through analogy and explanation. P. A. Kirschner, F. Prins, V. Jonker, G. Kanselaar, G. (Eds.), Eighth International Conference for the Learning Sciences, ICLS 2008. Vol. 3, 100-102. ISLS, The Netherlands. 2008.

Nokes, Timothy; VanLehn, Kurt; Belenky. Coordinating principles and examples through analogy and explanation. Thirtieth Annual Conference of the Cognitive Science Society: Washington, DC. 2008.

Pavlik, Phillip. Classroom Testing of a Discrete Trial Practice System. 34th Annual Meeting of the Association for Behavior Analysis, Chicago, IL, (May, 2008). 2008.

Pavlik, Phillip; Cen, Hao; Wu, Lili; Koedinger, Kenneth. Automatic determination of skill models from existing tutor data. Institute of Education Science Research Conference (IES), Washington, D.C. 2008.

Presson, Nora; MacWhinney, Brian. An adaptive tutor for explicit instruction of French grammatical gender cues. Poster annual meeting of the Institute of Education Sciences, Washington DC. 2008.

Presson, Nora; MacWhinney, Brian. Contrasting explicit and implicit instruction for grammatical categorization. IES Research Conference (IES), 2008. 2008.

Rau, Martina; Aleven, Vincent; Rummel, Nikol. Blocked versus Interleaved Practice with Multiple Graphical Representations of Fractions. Paper presented at the International EARLI Special Interest Group on Text and Graphics Comprehension, Tübingen, Germany. 2010.

Rau, Martina; Aleven, Vincent; Rummel, Nikol. Supporting Learning with Multiple Graphical Representations with Intelligent Tutoring Technology. Paper presented at the International EARLI Special Interest Group on Instructional Design and Learning with Computers, Ulm, Germany. 2010.

Rau, Martina; Aleven, Vincent; Rummel, Nikol. Blocked versus Interleaved Practice With Multiple Representations in an Intelligent Tutoring System for Fractions. Paper presented at the 10th International Conference of Intelligent Tutoring Systems. 2010.

Rau, Martina; Aleven, Vincent; Rummel, Nikol. Intelligent Tutoring Systems with Multiple Representations and Self-Explanation Prompts Support Learning of Fractions. Paper presented at the 14th International Conference on Artificial Intelligence in Education. 2009.

Rau, Martina; Aleven, Vincent; Rummel, Nikol. Understanding Fractions with Multiple Graphical Representations in Intelligent Tutoring Systems. Poster session at the annual IES research conference, Washington, DC. 2009.

Salden, Ron; Aleven, Vincent; Renkl, Alexander. Can tutored problem solving be improved by learning from examples? Proceedings of the 29th Annual Meeting of the Cognitive Science Society. (p. 1847). (CogSci 2007). 2007.

Salden, Ron; Aleven, Vincent; Schwonke, Rolf; Renkl, Alexander. Are worked examples and tutored problem solving synergistic forms of support?. 8th International Conference of the Learning Sciences (ICLS), June 2008. 2008.

van de Sande, Brett; Shelby; Treacy, Don; VanLehn, Kurt. Changing Student Attitudes using Andes, An Intelligent Homework System. AAPT Winter Meeting, Seattle WA, January 2007. 2007.

van de Sande, Brett; Shelby; Treacy, Don; VanLehn, Kurt; Wintersgill, Mary. Andes: An Intelligent Tutor Homework System. AAPT Summer Meeting, Greensboro, NC, July 2007. 2007.

VanLehn, Kurt; Koedinger, Kenneth; Skogsholm, Alida; Nwaigwe, Adaeze; Hausmann, Robert; Weinstein, Anders; Billings, Benjamin. What’s in a step? Toward general, abstract representations of tutoring system log data. C. Conati & K. McCoy (Eds). User Modelling 2007. 2007.

Vercellotti, Mary Lou; de Jong, Nel. “I prefer go”: English L2 Verb Complement Errors. Georgetown University Round Table, Washington, D.C., March 2009. 2009.

Vercellotti, Mary Lou; de Jong, Nel. “I always dessert cake to diet”: Elicited Imitation as an L2 task. Second Annual Inter-Science of Learning Center Conference, Seattle, WA, February 2009. 2009.

Wang, Hao-Chuan; Joshi; Rose, Carolyn. A Feature Based Approach for Leveraging Context for Classifying Newsgroup Style Discussion Segments. Association for Computational Linguistics (Poster). 2007.

Wang, Yi Chia; Joshi, Mahesh; Rose, Carolyn; Fischer, Frank; Weinberger, Armin; Stegmann, Karsten. Context Based Classification for Automatic Collaborative Learning Process Analysis [Poster]. AIED 2007. 2007.

Wylie, Ruth. Small words, big challenges: Identifying the difficulties in learning the English article system. IES Research Conference, Washington, DC, june, 2007 [pre-doctoral student Poster]. 2007.

Wylie, Ruth. Making a priori predictions about English as a Second Language grammar learning. IES Research Conference, Washington, DC, June 2008. [pre-doctoral student Poster]. 2008.

Zhang, Xiaonan; Mostow, Jack; Duke, Nell; Trotochaud, Christina; Valeri, Joseph; Corbett, Albert. Mining free-form spoken responses to tutor prompts. 1st International Conference on Educational Data Mining, 2008. [Poster-young researchers' track]. 2008.

Wang, Hao-Chuan; Rose, Carolyn. Supporting collaborative idea generation: A closer look using statistical process analysis techniques. AIED 2007 (Poster). 2007.

== Technical Reports ==

Anthony, Lisa; Yang, Jie; Koedinger, Kenneth. Entering Mathematical Equations Multimodally: Results on Usability and Interaction Design. Technical Report CMU-HCII-06-101, 15 Mar 2006. 2006.

Anthony, Lisa; Yang, Jie; Koedinger, Kenneth. How Handwritten Input Helps Students Learning Algebra Equation Solving. Technical Report CMU-HCII-08-100, 1 Mar 2008. 2008.

Matsuda, Noboru; Cohen, William; Sewall, Jonathan; Koedinger, Kenneth. Applying Machine Learning to Cognitive Modeling for Cognitive Tutors. Technical report CMU-ML-06-105, School of Computer Science, Carnegie Mellon University. 2006.

Matsuda, Noboru; Cohen, William; Sewall, Jonathan; Koedinger, Kenneth. What characterizes a better demonstration for cognitive modeling by demonstration?. Technical report CMU-ML-06-106, School of Computer Science, Carnegie Mellon University. 2006.

McLaren, Bruce. Lessons in Machine Ethics from the Perspective of Two Computational Models of Ethical Reasoning; AAAI Fall 2005 Symposium, Washington, D. C. "Papers from the AAAI Fall Symposium," Technical Report FS-05-06, pp. 70-77. 2005.

Singh, Ajit; Gordon, Geoffrey. Relational Learning via Collective Matrix Factorization. Tech report CMU-ML-08-109. 2008.

== Thesis ==

Anthony, Lisa. Developing Handwriting-based Intelligent Tutors to Enhance Mathematics Learning. Ph.D. Thesis, Human-Computer Interaction Institute, School of Computer Science, Carnegie Mellon University. CMU-HCI-08-105. 2008.

Diziol, Dejana. Development of a collaboration script to improve students` algebra learning when solving problems with the Algebra I, Cognitive Tutor. Diploma Thesis. Albert-Ludwigs-Universität Freiburg, Germany: Institute of Psychology, June 2006. 2006.

Hausmann, Robert. Elaborative and Critical Dialog: Two Potentially Effective Problem-Solving and Learning Interactions. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, University of Pittsburgh, 2005. 2005.

Ringenberg, Michael. Scaffolding Problem Solving With Embedded Examples to Promote Deep Learning. Submitted in partial fulfillment of the requirments for the degree of Master of Sciences, University of Pittsburgh, 2006. 2006.

== Thesis Proposals ==

Ayers, Elizabeth . Predicting Performance and Creating Better Student Proficiency Models by Improving Skill Codings. PIER Thesis Proposal. 2007.

Cen, Hao. Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning. Thesis Proposal, 2007. 2007.

Walker, Erin. Automated Adaptive Support for Peer Tutoring. PhD Thesis Proposal: Human Computer Interaction Institute, Carnegie Mellon University. 2009.

== Tutorials ==

Aleven, Vincent; McLaren, Bruce; Sewall, Jonathan. Tutorial on Rapid Development of Intelligent Tutors using the Cognitive Tutor Authoring Tools (CTAT). 6th IEEE International Conference on Advanced Learning Technologies, ICALT 2006, Kerkrade, The Netherlands. 2006.

Baker, Ryan; Yacef, Kalina; Beck, Joseph; Koedinger, Kenneth. Educational Data Mining (EDM). Tutorial conducted at AIED 2009. 2009.

== Workshops ==

Aleven, Vincent; Ashley, Kevin; Lynch, Collin; Pinkwart, Niels. Workshop on Applications in Ill-Defined Domains. (AIED 2007). 2007.

Aleven, Vincent; Ashley, Kevin; Lynch, Collin; Pinkwart, Niels. Workshop on Intelligent Tutoring Systems for Ill-Defined Domains: Assessment and feedback in ill-defined domains. (ITS 2008). 2008.

Aleven, Vincent; Roll, Ido. Analyzing patterns of help-seeking behavior using cognitive modeling and tree diagrams. Presentation in symposium, "Understanding the Complex Nature of Self-Regulatory Processes During Learning with Computer-based Learning Environments". AERA, 2009. 2009.

Asay, Devin; Siskin,Claire. Moving ahead with Revolution. Workshop Computer Assisted Language Instruction Consortium Conference (CALICO), San Francisco, CA, (March, 2008). 2008.

Asay, Devin; Siskin,Claire; Siskin,Claire. Getting started with Revolution. Workshop Computer Assisted Language Instruction Consortium Conference (CALICO), San Francisco, CA, (March, 2008). 2008.

Baker, Ryan; Aleven, Vincent; Koedinger, Kenneth; Rodrigo, Mercedes; Heffernan, Neil; Corbett,Albert; Roll,Ido. Gaming the System: Evidence from Data Mining and Human Observation on Affect, Attitudes, and Learning. Technology, Instruction, Cognition, and Learning Symposium. (invited presentation). 2008.

Baker, Ryan; Corbett, Albert; Aleven, Vincent. Determining when an error is actually a slip. Presentation in "Educational Data Mining: Seeing How Students Really Err" Symposium at the 13th Biennial Conference of the European Association for Research on Learning and Instruction. 2009.

Baker, Ryan; Koedinger, Kenneth. Educational Data Mining: Opportunities for the International Internet Classroom. AAAI Fall Symposium: Education Informatics: Steps Towards the International Internet Classroom. 2008.

Hausmann, Robert; Nokes, Timothy; VanLehn, Kurt; Gershman, Sophia. The impact of prompting on self-explanation and robust learning. Symposium accepted to European Association for Research on Learning and Instruction (EARLI, Aug 2009). to appear.

Johnson, Lewis; VanLehn, Kurt. Scalability Issues in AIED. Workshop conducted at the 14th International Conference on Artificial Intelligence in Education (AIED), July 6-10, 2009, Brighton, England. 2009.

Karabinos, Michael; Cuadros, Jordi; Yaron, David. Using and Authoring Virtual Labs and Scenario based Activities in Your Classroom and Laboratory. ChemEd, Vancouver, BC, August 2005. 2005.

Klahr, David. Learning & Development, Primary & Secondary Processes, Instruction & Learning. Invited Presidential Symposium, Cognitive Development Society Biennial Meeting. Santa Fe, NM. October, 2007. 2007.

Koedinger, Kenneth. Confronting the Assistance Dilemma: Is it Better to Give Than to Receive. Learning and Instruction Symposium (AERA 2008). 2008.

Koedinger, Kenneth. Fostering Learning in the Networked World: Presenting the 21st-Century Cyber Learning Opportunity and Challenge for the National Science Foundation. SIG-Advanced Technologies for Learning. AERA symposium, 2009. 2009.

McLaren, Bruce. The Pittsburgh Science of Learning Center: Learning Studies and Technology in Actual Classroom Settings. Kaleidoscope Symposium. Oberhausen, Germany. 2006.

McLaren, Bruce; Rummel, Nikol. Adapting Assistance to the Student(s): Preliminary Ideas from Individual and Collaborative Computer-Supported Learning Contexts. Symposium Session “The Assistance Dilemma in CSCL”, Computer-Supported Collaborative Learning (CSCL), June 8-13, 2009, Rhodes Greece. 2009.

Pavlik, Phillip; Presson, Nora; Hora. Using the FaCT System (Fact and Concept Training System) for Classroom and Laboratory Experiments. Workshop First Annual Inter-Science Of Learning Center Conference, Pittsburgh, PA. 2008.

Perfetti, Charles. Development of Word Meanings and Reading Skill Symposium. 15th Annual Meeting of the Society for the Scientific Study of Reading, Asheville, NC (July 2008). 2008.

Pinkwart, Niels. Chair of Intelligent Tutoring Systems Symposium. 21st International FLAIRS Conference, May 15-17, 2008, Coconut Grove, Florida. 2008.

Roll, Ido; Aleven, Vincent. Workshop on Meta-Cognition and Self-Regulated Learning in Educational Technologies. (ITS 2008). 2008.

Rose, Carolyn. Making authoring of conversational interfaces accessible. Workshop on Authoring Tools for Advanced Learning Systems with Standards, November 2005. 2005.

VanLehn, Kurt; Hausmann, Robert; Craig, Scotty. The role of the self in self-explanation. Symposium 12th Biennial Conference for Research on Learning and Instruction, Budapest, Hungary, 2007. 2007.

VanLehn, Kurt; Hausmann, Robert; Craig, Scotty. Is the “self” of self-explanation important? In vivo experiments. Symposium 2007 meeting of the American Educational Research Association, Chicago, IL. 2007.

Weinberger, Armin; Clark, Douglas; Dillenbourg, Pierre; Diziol, Dejana; Sampson, Victor; Stegmann, Karsten; Rummel, Nikol; Hong, Fabrice; Spada, Hans; McLaren, Bruce. Orchestrating Learning Activities on the Social and the Cognitive Level to Foster CSCL. Symposium at Conference on Computer Supported Collaborative Learning, (CSCL-07). 2007.

== Invited Talks ==

Aleven, Vincent. CTAT: Efficiently building real-world intelligent tutoring systems through programming by demonstration. 22nd International FLAIRS Conference, May 29-21, 2009. Invited talk. 2009.

Aleven, Vincent; Evenson, Shelly; Butcher, Kirsten. Improved Interaction Design in a Cognitive Tutor for Geometry. Carnegie Mellon University: Human-Computer Interaction Institute 12th Anniversary Celebration. April 20, 2006. 2006.

Allen; Jones. French Online and the Open Learning Initiative. Digital Stream Conference: Emerging Technologies in Teaching Languages and Culture, Monterey, California. March 2006. 2006.

Anthony, Lisa. Exploration of the Effects of Handwriting on Learning in Algebra Equation Solving. ACM Multimedia EMME Workshop, Augsburg, Germany. 2007.

Anthony, Lisa. User Science and Experiences Research group seminar. IBM Almaden Research Center, San Jose, CA. Invited talk. 2007.

Anthony, Lisa . EARLI 2009 Symposium on Worked Examples, Amsterdam, the Netherlands. . 2009.

Ashley; . Some Thoughts on Using Computers to Teach Argumentation. Intelligent Tutoring Systems Invited Talk. 21st International FLAIRS Conference, May 15-17, 2008, Coconut Grove, Florida. 2008.

Assay; O'Neil Christine. Webware: Rapid Creation of Internet-based Multimedia Applications Without Web Browser Hassles. . 2006.

Baker, Ryan. Detecting and Responding to Gaming the System in Cognitive Tutors. Carnegie Learning, Inc., Pittsburgh, PA. April 3, 2008. (invited seminar). 2008.

Baker, Ryan. Using Data Mining to Better Understand Learning and Learners: Key Challenges and Directions. Department of Computer Science, University of Sherbooke. June 17, 2008. (invited seminar). 2008.

Baker, Ryan. Towards Understanding Why Students Game the System. Department of Educational and Counseling Psychology, McGill University. June 18, 2008. (invited seminar). 2008.

Baker, Ryan. Towards Understanding Why Students "Game the System" Within Educational Technology. University of Memphis. Mar 12, 2009. (invited seminar). 2009.

Balass; Bolger; Perfetti, Charles. The Role of Definition and Sentence Context in Vocabulary Learning. Thirteenth Annual Meeting Society for the Scientific Study of Reading. July 5-8, 2006. Vancouver, Canada. 2006.

Butcher, Kirsten. Society for the Advancement of Native Americans & Chicanos in Science (SACNAS) invited talk. . 2008.

Butcher, Kirsten. University of Utah invited talk. . 2007.

Butcher, Kirsten. Indiana University, Bloomington Indiana invited talk. . 2007.

Butcher, Kirsten. Kent State University, Ohio, invited talk. . 2007.

Butcher, Kirsten . Visual interaction and robust learning. Talk presented at the International Workshop on Spatial Cognition and Learning, University of Freiburg, Freiburg, Germany, September, 2008. 2008.

Butcher, Kirsten; Aleven, Vincent. Visual interaction in intelligent tutoring: Support for robust learning. Research presentation for visiting educators and officials from Singapore’s Ministry of Education, Carnegie Mellon University, Pittsburgh, PA. 2008.

Chan. Learning a tonal language by attending to the tone: an in-vivo experiment. Talk given at the Pittsburgh Science of Learning Center Chinese Learnlab Symposium, Carnegie Mellon University, Oct 19, 2007. 2007.

De Jong, Nel. Approaches to the study of second language acquisition. Guest lecture at the CUNY Graduate Center (invited by Prof. Den Dikken and Prof. Otheguy), December 2007. 2007.

De Jong, Nel. Oral fluency development in ESL classrooms. Guest lecture at the CUNY Graduate Center (invited by Prof. Klein), November 2007. 2007.

De Jong, Nel. Oral fluency development in a second language. Presentation given at the Cognitive Approaches to Second Language Acquisition research group at the University of Amsterdam, January 2008. 2008.

De Jong, Nel. The study of oral fluency development in ESL. Presentation given at the Colloquium on Teaching and Learning World Languages, March 2008, at Queens College of CUNY. 2008.

De Jong, Nel. Pre-training formulaic sequences and its effect on oral fluency. Talk given at the SLA lab meeting, CUNY Graduate Center, April 24, 2009. 2009.

De Jong, Nel . Developing oral fluency with the 4/3/2 task. Presentation given at the Multimedia Showcase, University of Pittsburgh, September 2006. 2006.

De Jong, Nel; Haldeman, Laura. Training formulaic sequences has mixed short-term effects on L2 oral fluency. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009. to appear.

Dunlap, Susan. Lexical quality of English second language learners: Effects of focused training on orthographic encoding skill. Brown Bag Presentation for the Cognitive Psychology Program, University of Pittsburgh, February, 2009. 2009.

Dunlap, Susan. Learning L2 vocabulary from semantic cues: A PSLC LearnLab study of implicit versus explicit training. Presentation to the Pitt-CMU Conference, Pittsburgh, September 2006. 2006.

Dunlap, Susan . What are some effective ways to support learning of new vocabulary in L2?: Evidence from some LearnLab studies. Brown Bag Presentation for Cognitive Psychology Program, University of Pittsburgh, PA. 2006.

Dunlap, Susan . Rules and exceptions: Semantic cues for learning new vocabulary in Chinese as a second language. Presentation at PSLC Chinese LearnLab Symposium "Bridging Chinese Pedagogy, Research, and Technology," Carnegie Mellon University, Pittsburgh. 2007.

Dunlap, Susan; Friedline; Juffs, Alan; Perfetti, Charles. Effects of a spelling intervention with learners of English as a second language. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009. to appear.

Eskenazi, Maxine. Acoustical Society of America Meeting: Speech and Education, Hawaii. . 2006.

Frishkoff; Schreiber. Research to Practice: A Bridge Worth Crossing. Talk presented at the Annual Meeting of the American Psychological Association (APA) Session: APA/IES Postdoctoral Education Research Training. Washington, D.C., August 15, 2005. 2005.

Gadgil, Soniya; Nokes, Tim. Analogical scaffolding in collaborative learning. Second Annual Inter-Science of Learning Center Student and Post-Doc Conference, Seattle, WA. 2009.

Hausmann, Bob; Chi, Micki. The impact of constructive dialog on collaborative learning and problem solving performance. Paper presented at the Festschrift for Lauren Resnick entitled “Talk and Dialogue: How Discourse Patterns Support Learning.”. 2005.

Hausmann, Bob; Nokes, Tim. Evidence of transfer in a Physics 1 Course: An educational data-mining project. Second Annual Inter-Science of Learning Center Student and Post-Doc Conference. Seattle, WA. 2009.

Heilman, Michael; Eskenazi, Maxine. Authentic, Individualized Practice for English as a Second Language Vocabulary. Presented at Interfaces of Intelligent Computer-Assisted Language Learning Workshop at the Ohio State University, Columbus, OH. 2006.

Juffs, Alan. Opportunities and Challenges in Teaching Vocabulary Using CALL in an Intensive English Program. February 22, 2008. Ontario Institute for Studies in Education, University of Toronto, Canada. Invited talk. 2008.

Juffs, Alan. Vocabulary acquisition in English as a second language: Refining theory and practice in an Intensive English Program. Keynote address given at Second Language Acquisition and Teaching (SLAT) Roundtable, University of Arizona, March 2007. 2007.

Juffs, Alan; Friedline, Ben. L1 Influence, morphological (in)sensitivity and L2 lexical development: Evidence form production data. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009. to appear.

Klahr, David. Cognitive Science & Science Instruction: Pasteur's Quadrant in the Learning Sciences. Invited Master Lecture: SRCD 2007 Biennial Meeting. Boston, MA March 2007. 2007.

Klahr, David. Cognitive Science & Early Science Education. Invited Presentation at Seminar Series on Developmental Science and Early Schooling. Frank Porter Graham Child Development Institute. University of North Carolina, Chapel Hill, NC March 2007. 2007.

Klahr, David; Chen. Remote Transfer of Scientific Reasoning and Problem-Solving Strategies in Children and Adults. Presentation at Symposium on Learning and Transfer: Application of Developmental Psychology Research to Educational Issues. SRCD 2007 Biennial Meeting. Boston, MA March 2007. 2007.

Koedinger, Ken. Twenty-First National Conference on Artificial Intelligence. “Cognitive Tutors and Opportunities for Convergence of Human and Machine Learning Theory”. Plenary speaker. Boston, Massachusetts, July, 2006. 2006.

Koedinger, Ken. Studying Robust Learning through Rigorous Experiments in Real Classrooms. Askwith Education Forum at the Harvard Graduate School of Education. Harvard University. 2007.

Koedinger, Ken. Korean Academy of Science and Technology. Conference on Learning. Plenary speaker. Seoul, Korea, November, 2006. 2006.

Litman, Diane. Detecting and Adapting to Student Uncertainty in a Spoken Tutorial Dialogue System. Invited Talk at Affective Language in Human and Machine Symposium, AISB Convention, Aberdeen, Scotland, (April, 2008). 2008.

Matsuda, Noboru. Using Simulated Student to build Cognitive Tutors and beyond – Cognitive Modeling with Programming by Demonstration (2006). Department of Computer Science Colloquium, Northern Illinois University, August 2006, IN. 2006.

Matsuda, Noboru. Building Robust Learning Theories for Robust Learning (2006). International Symposium on e-Learning, Osaka Prefecture University, May 2006, Osaka, Japan. 2006.

Matsuda, Noboru. Beyond Building Cognitive Tutors by Demonstration – When SimStudent helps building a bridge between technology and education. School of Education, Stanford University. June 2007, Palo Alto, CA. 2007.

Matsuda, Noboru. Building Cognitive Model for Cognitive Tutors by Demonstration (2006). Seminar series on e-Learning, Kumamoto University, May 2006, Kumamoto, Japan. 2006.

Matsuda, Noboru . SimStudent: Teaching a smart machine to learn how people learn. Human Computer Interaction Graduate Program, Iowa State University. April 2008, Ames, IA. 2008.

Matsuda, Noboru; Cohen; Koedinger, Ken. Building Cognitive Tutors with Programming by Demonstration. International Conference on Inductive Logic Programming, Technische Universitat Munchen. 2005. Pages pp. 41-46. 2005.

McLaren, Bruce. Kaleidoscope Symposium, Oberhausen, Germany, July 2006. Title of talk: "The Pittsburgh Science of Learning Center: Learning Studies and Technology in Actual Classroom Settings.". 2006.

Nokes, Tim. Taking cognitive science to school: How cognitive science can improve conceptual learning in physics classrooms. Learning Sciences and Policy Brown Bag Series, College of Education, University of Pittsburgh: Pittsburgh, PA. 2008.

Nokes, Tim. Taking cognitive science to school: How cognitive science can improve conceptual learning in physics classrooms. Learning Sciences and Policy Brown Bag Series, University of Pittsburgh: Pittsburgh, PA, December 2008. Invited talk. 2008.

Nokes, Tim. Taking cognitive science to school: How cognitive science can improve student learning in physics classrooms. Paper to be presented to the annual meeting of the Eastern Psychological Association, March 2009, Pittsburgh, PA. 2009.

Perfetti, Charles. Reducing the complexities of reading comprehension: A Simplying framework. Presented at the Institute of ducation Sciences Research Conference, June 7-9, 2009, Washington DC. 2009.

Pino; Eskenzi. L1 Effects in students' answers to word recall questions and cloze questions. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009. to appear.

Presson , Nora. Explicit Instruction of Cues to Grammar: Prototypes or Exemplars. Presented at the 1st annual iSLC Student / Postdoc Conference, Pittsburgh, PA. 2008.

Rodrigo; Baker, Ryan; Sugay; Tabano. Monitoring novice programmer affect and behaviors to identify learning bottlenecks. Presentation at Philippine Computing Society Congress 2009. 2009.

Rodrigo; M.M.T.; Baker, Ryan; R.S.J.d.; Abalos; N.; Bacuyag; K.; Basuel; B.; Bautista; M.; Cortez; M.; Dulla; G.; Elomina; S.; Gineta; M.A.; Rara; A.; Rodriguez; R.; Sanggalang; J.; Sugay; J.; Tan; A.K.; Tan; M.; Trajano; E.; Uy; F.; Victorino; N.; Villaflor; K. . A comparison of learners’ affect and behaviors while using an intelligent tutor and an educational game. Presentation at Philippine Computing Society. 2009.

Roll, Ido. Teaching for learning versus teaching for retention. 2nd Inter-Science of Learning Centers Conference, February 2009. Seattle, WA. 2009.

Roll, Ido. Can Help-Seeking Be Taught Using Tutoring Systems? Searching For the Secret Sauce of Meta-cognitive Tutoring. Department of Education, Haifa University, December 2007. Invited talk. 2007.

Roll, Ido. Modeling and scaffolding general learning skills with intelligent tutoring systems. Department of Management Information Systems. Haifa University, December 2007. Invited talk. 2007.

Roll, Ido. Debugging the Learning Process: Can Tutoring Systems Teach General Learning Skills? Department of Computer Science, Worcester Polytechnic Institute. July 2007. Invited talk. 2007.

Roll, Ido. Teaching for learning versus teaching for retention. Presentation at the 2nd Inter-Science of Learning Centers Conference, 2009. Seattle, WA. 2009.

Rose, Carolyn. Towards Adaptive Support for On-line Learning, Technology-integrated Science and Engineering Education. (TechSEE) Keynote Speech Taipei May 2006. 2006.

Salden, Ron. Life, the Universe, and Worked Examples in Cognitive Tutors. AI Seminar of the Intelligent Systems Program (ISP) at the University of Pittsburgh, USA, October 24, 2008. 2008.

Sewall; Bett. Cognitive Tutor Authoring Tools and Pittsburgh Science of Learning Center. Software & Information Industry Association Ed Tech Business Forum, December 2008. 2008.

Siskin, Claire. Presentation of the software component at the “Multimedia Showcase” sponsored by the Robert Henderson Media Center at the University of Pittsburgh. . 2005.

VanLehn, Kurt. “When is tutorial dialogue more effective than cheaper instruction. ” Serious Games Workshop, Institute for Creative Technology, Marina del Rey, CA, August 2006. 2006.

VanLehn, Kurt. The Pittsburgh Science of Learning Center: Studying robust learning in LearnLab classrooms. International Conference on Cognition and Neural Science, Boston, MA, May 2006. 2006.

VanLehn, Kurt. When Is Tutorial Dialogue More Effective Than Less Interactive Instruction. American Educational Research Association, New York, NY, March 28, 2008. 2008.

VanLehn, Kurt. Intelligent Tutoring Systems: What Do We Do Next. Fordham University, New York, NY, March 27, 2008. 2008.

VanLehn, Kurt. Pittsburgh Science of Learning Center (PSLC). International Conference of the Learning Sciences (ICLS). Bloomington, IN, USA. 2006.

VanLehn, Kurt. The Pittsburgh Science of Learning Center: Studying robust learning in LearnLab classrooms. International Conference on Cognition and Neural Science. Boston, MA. 2006.

VanLehn, Kurt. The interaction plateau: Answer-based tutoring < Step-based tutoring = Natural tutoring. Keynote talk, Intelligent Tutoring Systems, July, 2008. 2008.

VanLehn, Kurt. Designing for conceptual understanding: College physics. Open Learning Interplay 2008, Pittsburgh, PA, March 10, 2008. 2008.

Vercelloti . Choosing a verb complement: Use and accuracy in English L2. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009. to appear.

Wu, Sue-mei. Literacy Promotion and Grammar Consolidation in an Intermediate Chinese Curriculum. Presentation at the annual meeting of the Chinese Languages Teachers Association (CLTA)/American Council on the Teaching of Foreign Languages (ACTFL) Conference. Nov 20- 23, 2008. Orlando, Florida. 2008.

Wu, Sue-mei. Robust Learning of Language and Cultural Literacy in Chinese Online. Presented at the Multimedia Showcase. September 25, 2008, University of Pittsburgh, Pittsburgh, PA. 2008.

Wu, Sue-mei. The PSLC Chinese LearnLab Online project. The Opening Learning Interplay Symposium: The Evolution of Open Learning. March 10-12, 2008. Carnegie Mellon University, Pittsburgh, PA. 2008.

Wylie, Ruth. Does Self-Explanation Always Help?: The effects of adding self-explanation prompts to an English as a Second Language grammar tutor. Second Annual Inter-Science of Learning Center Student and Post-Doc Conference. February 5-7, 2009. 2009.

Wylie, Ruth; Koedinger, Ken; Mitamura, Teruko. Would someone explain this? Adding self-explanation to an English Article Tutor. Presentation at PSLC/ELI Symposium on Research in an Intensive English Program, University of Pittsburgh, June 2009. to appear.

Wylie, Ruth; Mitamura, Teruko; Rankin . From Practice to Production: Developing Tutoring Systems for English Article Use. Presentation at the Three Rivers Teachers of English to Speakers of Other Languages (3RTESOL) Conference. Pittsburgh, Pennsylvania. October 28, 2006. 2006.

Wylie, Ruth; Mitamura, Teruko; Rankin; Koedinger, Ken. Two Tutors, One Goal: Two tutoring systems for teaching English articles. University of Pittsburgh’s Multimedia Showcase. Pittsburgh, Pennsylvania. September 27, 2006. 2006.

Wylie, Ruth; Mitamura, Teruko; Rankin; Koedinger, Ken; MacWhinney. Developing Intelligent Tutoring Systems for Language Learning. Science of Learning Center Symposium at the Society for Neuroscience conference. Atlanta, Georgia. October 13, 2006. 2006.

Yaron, Dave; Karabinos; Davenport; Leinhardt. Virtual lab activities for introductory chemistry labs. American Chemical Society Annual Meeting, San Francisco, September 2006. 2006.

Yaron, Dave; Karabinos; Davenport; Leinhardt. Virtual labs and scenario-based activities for introductory chemistry. American Chemical Society - Penn-Ohio Regional Meeting, Theil College, Greenville, PA, October 2006. 2006.

Yaron, Dave; Karabinos; Leinhardt; Davenport; greeno. Making the implicit explicit in the teaching of chemical equilibrium. Gordon Conference on Chemical Education Research and Practice, invited paper. 2007.

Yaron, Dave; Leinhardt; Karabinos et al. Virtual labs and scenario-based learning for introductory chemistry. Pacifichem, Hawaii, December 2005. 2005.

Yu. Designing systematic exercises to generate learning: How exercises should be developed for optimal effectiveness. Chinese Language Teachers Association (CLTA/ ACTFL), November 18-20, 2005, Baltimore, Maryland. 2005.

Zhang. Awareness of Chinese CALL Learners. The Annual Meeting of Chinese Language Teachers Association (CLTA/ ACTFL), November 18-20, 2005, Baltimore, Maryland. 2005.

Zhang. The Development of Morphological Awareness and Literacy Skills in Young Heritage Chinese Learners. The Annual Meeting of Chinese Language Teachers Association (CLTA/ACTFL). 2006.

PSLC People

2011-09-14T13:32:46Z

Mbett: /* Post Docs */

== '''The Executive Committee''' ==

=== Directors ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [http://pact.cs.cmu.edu/koedinger.html '''Ken Koedinger'''] || Carnegie Mellon University || Human-Computer Interaction Institute
|-
| '''Charles Perfetti''' || University of Pittsburgh || Psychology, LRDC Director
|}

=== Managing Director ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Michael Bett''' || Carnegie Mellon University || Human-Computer Interaction Institute
|}

=== Members ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aleven, Vincent || Carnegie Mellon University || Human-Computer Interaction
|-
| Eskenazi, Maxine || Carnegie Mellon University || Language Technologies Institute
|-
| Fiez, Julie || University of Pittsburgh || Psychology
|-
| Gordon, Geoff || Carnegie Mellon University || Machine Learning
|-
| Klahr, David || Carnegie Mellon University || Psychology
|-
| Lovett, Marsha || Carnegie Mellon University || Psychology
|-
| Nokes, Tim || University of Pittsburgh || LRDC
|-
| Resnick, Lauren || University of Pittsburgh || Learning Research and Development Center
|-
| Rose, Carolyn || Carnegie Mellon University || Human-Computer Interaction Institute/Language Technologies Institute
|}

== Advisory Board ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aronson, Joshua || New York University || Applied Psychology
|-
| Atkinson, Robert || Arizona State University || Division of Psychology in Education
|-
| Azevedo, Roger || University of Memphis || Psychology
|-
| Biswas, Gautam || Vanderbilt University || Computer Science and Computer Engineering
|-
| Collins, Allan || Northwestern University || Education and Social Policy
|-
| Dede, Christopher || Harvard University || Technology in Education
|-
| Feuer, Michael || George Washington University || Graduate School of Education and Human Development
|-
| Goldman, Susan || University of Illinois || Psychology
|-
| Goldstone, Rob || Indiana University || Psychology
|-
| Griffiths, Tom || Berkeley || Psychology
|-
| Lesgold, Alan || University of Pittsburgh || School of Education
|-
| McNamara, Danielle || University of Memphis || Psychology
|-
| Li, Ping || Penn State University || Psychology
|-
| Minstrell, Jim || FACET Innovations, LLC Seattle, WA ||
|-
| Schauble, Leona || Vanderbilt University || Teaching & Learning
|-
| Smith, Marshall (Mike) S.|| ||
|}

== Graduate Students ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Adam Skory || Carnegie Mellon ||
|-
| Benjamin Friedline || University of Pittsburgh || Linguistics
|-
| Colleen Davy || Carnegie Mellon || Psychology
|-
| Garbiel Parent || Carnegie Mellon || Language Technologies Institute
|-
| (Derek) Ho Leung Chan || University of Pittsburgh || Linguistics
|-
| Leida Tolentino || University of Pittsburgh || Psychology
|-
| Nora Presson || Carnegie Mellon || Psychology
|-
| Ruth Wylie || Carnegie Mellon || Human Computer Interaction Institute
|-
| Susan Dunlap || University of Pittsburgh || Psychology
|-
| Yun (Helen) Zhao || Carnegie Mellon || Second Language Acquisition
|-
| Benjamin Shih || Carnegie Mellon ||
|-
| Collin Lynch || University of Pittsburgh ||
|-
| Erik Zawadzki || Carnegie Mellon ||
|-
| Nan Li || Carnegie Mellon ||
|-
| Amy Ogan || Carnegie Mellon ||
|-
| Dan Belenky || University of Pittsburgh || Psychology
|-
| Matthew Easterday || Carnegie Mellon ||
|-
| Soniya Gadgil || University of Pittsburgh || Psychology
|-
| Yanhui Zhang || Carnegie Mellon ||
|-
| Dejana Diziol || Freiburg ||
|-
| Elizabeth Ayers || Carnegie Mellon ||
|-
| Elsa Golden || Carnegie Mellon ||
|-
| April Galyardt || Carnegie Mellon || Statistics
|-
| Jamie Jirout || Carnegie Mellon || Psychology
|-
| Martina Rau || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tom Lauwers || Carnegie Mellon ||
|-
| Tracy Sweet || Carnegie Mellon || Statistics
|-
| Kevin Del Rosa || Carnegie Mellon ||
|-
| Turadg Aleahmad || Carnegie Mellon || Human Computer Interaction Institute
|-
| Gahgene Gweon || Carnegie Mellon ||
|-
| Anagha Kulkarni (Joshi) || Carnegie Mellon ||
|-
| Bryan Matlen || Carnegie Mellon || Psychology
|-
| Sung-Young Jung || University of Pittsburgh ||
|-
| Gustavo Santos || Carnegie Mellon ||
|-
| Hao-Chuan Wang || Carnegie Mellon ||
|-
| Indrayana Rustandi || Carnegie Mellon ||
|-
| Jessica Nelson || University of Pittsburgh || Psychology
|-
| Rohit Kumar || Carnegie Mellon ||
|-
| Roxana Gheorghiu || University of Pittsburgh ||
|-
| Tamar Degani || University of Pittsburgh || Psychology
|-
| Yan Mu || Carnegie Mellon || Psychology
|-
| Elijah Mayfield || Carnegie Mellon ||
|-
| Erin Walker || Carnegie Mellon ||
|-
| Iris Howley || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tracy Clark || Univeristy of Pennslyvania ||
|-
| Laurens Feestra || Netherlands ||
|-
| Maaike Waalkens || Netherlands ||
|-
| Mary Lou Vercellotti || University of Pittsburgh || Linguistics
|-
| Nozomi Tanaka || University of Pittsburgh || Linguistics
|-
| Eliane Stampfer || Carnegie Mellon || Human Computer Interaction Institute
|-
| Katherine Martin || University of Pittsburgh || Linguistics
|}

== Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Amy Ogan || Carnegie Mellon || HCII
|-
| Laura Halderman || University of Pittsburgh ||
|-
| Seiji Isotani || Carnegie Mellon University || HCII
|-
| John Connelly || Carnegie Learning ||
|-
| Amy Crosson || University of Pittsburgh || LRDC
|-
| Min Chi || Stanford || MLD
|-
| Ido Roll || University of British Columbia ||
|-
| Stephanie Siler || Carnegie Mellon || Psychology
|-
| Zelha Tunc-Pekkan || Carnegie Mellon || HCII
|-
| Fan Cao || University of Pittsburgh ||
|-
| Suzanne Adlof || University of Pittsburgh ||
|-

| Candace Walkington || University of Wisconson ||
|-
| Matthew Bernacki || University of Pittsburgh ||
|-
| Gregory Dyke || Carnegie Mellon ||
|-
| Sherice Clarke || University of Pittsburgh ||
|-
| Oscar Saz || Carnegie Mellon University || LTI
|-
| Michael Yudelson || Carnegie Mellon University ||

|}

== Former Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Hua Ai || Georgia Institute of Technology || LTI
|-
| Alicia Chang || University of Delaware || Postdoctoral Researcher
|-
| Connie Guan Qun || University of Pittsburgh ||
|-
| Chin-LungYang || University of Pittsburgh ||
|-
| Scotty Craig || University of Memphis|| Research Assistant Professor, Institute for Intelligent Systems
|-
|}

== Faculty ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Al Corbett || Carnegie Mellon || HCII
|-
| Alan Juffs || University of Pittsburgh || Linguistics
|-
| Brian Junker || Carnegie Mellon || Statisics
|-
| Brian MacWhinney || Carnegie Mellon || Psychology
|-
| Bruce McLaren || Carnegie Mellon || HCII
|-
| Carolyn Rosé || Carnegie Mellon || LTI/HCII
|-
| Charles Perfetti || University of Pittsburgh || LRDC
|-
| Christa Asterhan || Hebrew University ||
|-
| David Klahr || Carnegie Mellon || Psychology
|-
| David Yaron || Carnegie Mellon || Chemistry
|-
| Geoff Gordon || Carnegie Mellon || Machine Learning
|-
| Jack Mostow || Carnegie Mellon || Robotics
|-
| Jim Greeno || University of Pittsburgh || Instruction and Learning
|-
| John Stamper || Carnegie Mellon || HCII
|-
| Ken Koedinger || Carnegie Mellon || HCII
|-
| Kirsten Butcher || University of Utah || Instructional Design & Educational Technology
|-
| Kurt VanLehn || Arizona State University || Computer Science and Engineering
|-
| Lauren Resnick || University of Pittsburgh || LRDC
|-
| Louis Gomez || University of Pittsburgh || School of Education
|-
| Marsha Lovett || Carnegie Mellon || Eberly Center
|-
| Mary Catherine O'Connor || Boston University || School of Education
|-
| Matthew Kam || Carnegie Mellon || HCII
|-
| Maxine Eskenazi || Carnegie Mellon || LTI
|-
| Nel de Jong || Vrije Universiteit Amsterdam ||
|-
| Niels Pinkwart || Clausthal University of Technology ||
|-
| Nikol Rummel || Ruhr-Universität Bochum || Psychology
|-
| Noboru Matsuda || Carnegie Mellon || HCII
|-
| Phil Pavlik || Carnegie Mellon || HCII
|-
| Richard Scheines || Carnegie Mellon || Philosphy
|-
| Ryan Baker || WPI ||
|-
| Sandy Katz || University of Pittsburgh || LRDC
|-
| Sarah Michaels || Clark University || Education
|-
| Teruko Matamura || Carnegie Mellon || LTI
|-
| Tim Nokes || University of Pittsburgh ||
|-
| Vincent Aleven || Carnegie Mellon || LTI
|-
| William Cohen || Carnegie Mellon || ML
|-
| Ma. Mercedes T. Rodrigo || Ateneo de Manila University
|| Information Systems and Computer Science

|}

== Staff ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [[User:Alida|Alida Skogsholm]] || Carnegie Mellon University || DataShop Manager
|-
| Bob Hausmann || Carnegie Learning ||
|-
| Brett Leber || Carnegie Mellon University || DataShop/CTAT
|-
| Christy McGuire || Edalytics ||
|-
| Cressida Magaro || Carnegie Mellon University ||
|-
| Dorolyn Smith || University of Pittsburgh ||
|-
| Duncan Spencer || Carnegie Mellon University || DataShop
|-
| Gail Kusbit || Carnegie Mellon University || Research Manager
|-
| Jo Bodnar || Carnegie Mellon University ||
|-
| John Kowalski || Carnegie Mellon University ||
|-
| Jonathan Sewall || Carnegie Mellon University ||
|-
| Kevin Willows || Carnegie Mellon University ||
|-
| Mark Haney || University of Pittsburgh ||
|-
| Martin van Velsen || Carnegie Mellon University ||
|-
| Michael Bett || Carnegie Mellon University || Managing Director
|-
| Mike Karabinos|| Carnegie Mellon University ||
|-
| Ross Strader || Carnegie Mellon University ||
|-
| Sandy Demi || Carnegie Mellon University || DataShop/CTAT
|-
| Scott Silliman || University of Pittsburgh || OLI
|-
| Shanwen Yu || Carnegie Mellon University || DataShop
|-
| Steve Ritter || Carnegie Learning || Founder
|-
| Thomas Harris || Edalytics ||
|-
| Tristan Nixon || Carnegie Learning ||
|}

PSLC People

2011-09-14T13:27:16Z

Mbett: /* Post Docs */

== '''The Executive Committee''' ==

=== Directors ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [http://pact.cs.cmu.edu/koedinger.html '''Ken Koedinger'''] || Carnegie Mellon University || Human-Computer Interaction Institute
|-
| '''Charles Perfetti''' || University of Pittsburgh || Psychology, LRDC Director
|}

=== Managing Director ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Michael Bett''' || Carnegie Mellon University || Human-Computer Interaction Institute
|}

=== Members ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aleven, Vincent || Carnegie Mellon University || Human-Computer Interaction
|-
| Eskenazi, Maxine || Carnegie Mellon University || Language Technologies Institute
|-
| Fiez, Julie || University of Pittsburgh || Psychology
|-
| Gordon, Geoff || Carnegie Mellon University || Machine Learning
|-
| Klahr, David || Carnegie Mellon University || Psychology
|-
| Lovett, Marsha || Carnegie Mellon University || Psychology
|-
| Nokes, Tim || University of Pittsburgh || LRDC
|-
| Resnick, Lauren || University of Pittsburgh || Learning Research and Development Center
|-
| Rose, Carolyn || Carnegie Mellon University || Human-Computer Interaction Institute/Language Technologies Institute
|}

== Advisory Board ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aronson, Joshua || New York University || Applied Psychology
|-
| Atkinson, Robert || Arizona State University || Division of Psychology in Education
|-
| Azevedo, Roger || University of Memphis || Psychology
|-
| Biswas, Gautam || Vanderbilt University || Computer Science and Computer Engineering
|-
| Collins, Allan || Northwestern University || Education and Social Policy
|-
| Dede, Christopher || Harvard University || Technology in Education
|-
| Feuer, Michael || George Washington University || Graduate School of Education and Human Development
|-
| Goldman, Susan || University of Illinois || Psychology
|-
| Goldstone, Rob || Indiana University || Psychology
|-
| Griffiths, Tom || Berkeley || Psychology
|-
| Lesgold, Alan || University of Pittsburgh || School of Education
|-
| McNamara, Danielle || University of Memphis || Psychology
|-
| Li, Ping || Penn State University || Psychology
|-
| Minstrell, Jim || FACET Innovations, LLC Seattle, WA ||
|-
| Schauble, Leona || Vanderbilt University || Teaching & Learning
|-
| Smith, Marshall (Mike) S.|| ||
|}

== Graduate Students ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Adam Skory || Carnegie Mellon ||
|-
| Benjamin Friedline || University of Pittsburgh || Linguistics
|-
| Colleen Davy || Carnegie Mellon || Psychology
|-
| Garbiel Parent || Carnegie Mellon || Language Technologies Institute
|-
| (Derek) Ho Leung Chan || University of Pittsburgh || Linguistics
|-
| Leida Tolentino || University of Pittsburgh || Psychology
|-
| Nora Presson || Carnegie Mellon || Psychology
|-
| Ruth Wylie || Carnegie Mellon || Human Computer Interaction Institute
|-
| Susan Dunlap || University of Pittsburgh || Psychology
|-
| Yun (Helen) Zhao || Carnegie Mellon || Second Language Acquisition
|-
| Benjamin Shih || Carnegie Mellon ||
|-
| Collin Lynch || University of Pittsburgh ||
|-
| Erik Zawadzki || Carnegie Mellon ||
|-
| Nan Li || Carnegie Mellon ||
|-
| Amy Ogan || Carnegie Mellon ||
|-
| Dan Belenky || University of Pittsburgh || Psychology
|-
| Matthew Easterday || Carnegie Mellon ||
|-
| Soniya Gadgil || University of Pittsburgh || Psychology
|-
| Yanhui Zhang || Carnegie Mellon ||
|-
| Dejana Diziol || Freiburg ||
|-
| Elizabeth Ayers || Carnegie Mellon ||
|-
| Elsa Golden || Carnegie Mellon ||
|-
| April Galyardt || Carnegie Mellon || Statistics
|-
| Jamie Jirout || Carnegie Mellon || Psychology
|-
| Martina Rau || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tom Lauwers || Carnegie Mellon ||
|-
| Tracy Sweet || Carnegie Mellon || Statistics
|-
| Kevin Del Rosa || Carnegie Mellon ||
|-
| Turadg Aleahmad || Carnegie Mellon || Human Computer Interaction Institute
|-
| Gahgene Gweon || Carnegie Mellon ||
|-
| Anagha Kulkarni (Joshi) || Carnegie Mellon ||
|-
| Bryan Matlen || Carnegie Mellon || Psychology
|-
| Sung-Young Jung || University of Pittsburgh ||
|-
| Gustavo Santos || Carnegie Mellon ||
|-
| Hao-Chuan Wang || Carnegie Mellon ||
|-
| Indrayana Rustandi || Carnegie Mellon ||
|-
| Jessica Nelson || University of Pittsburgh || Psychology
|-
| Rohit Kumar || Carnegie Mellon ||
|-
| Roxana Gheorghiu || University of Pittsburgh ||
|-
| Tamar Degani || University of Pittsburgh || Psychology
|-
| Yan Mu || Carnegie Mellon || Psychology
|-
| Elijah Mayfield || Carnegie Mellon ||
|-
| Erin Walker || Carnegie Mellon ||
|-
| Iris Howley || Carnegie Mellon || Human Computer Interaction Institute
|-
| Tracy Clark || Univeristy of Pennslyvania ||
|-
| Laurens Feestra || Netherlands ||
|-
| Maaike Waalkens || Netherlands ||
|-
| Mary Lou Vercellotti || University of Pittsburgh || Linguistics
|-
| Nozomi Tanaka || University of Pittsburgh || Linguistics
|-
| Eliane Stampfer || Carnegie Mellon || Human Computer Interaction Institute
|-
| Katherine Martin || University of Pittsburgh || Linguistics
|}

== Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Laura Halderman || University of Pittsburgh ||
|-
| Seiji Isotani || Carnegie Mellon University || HCII
|-
| John Connelly || Carnegie Learning ||
|-
| Amy Crosson || University of Pittsburgh || LRDC
|-
| Min Chi || Stanford || MLD
|-
| Ido Roll || University of British Columbia ||
|-
| Stephanie Siler || Carnegie Mellon || Psychology
|-
| Zelha Tunc-Pekkan || Carnegie Mellon || HCII
|-
| Fan Cao || University of Pittsburgh ||
|-
| Suzanne Adlof || University of Pittsburgh ||
|-

| Candace Walkington || University of Wisconson ||
|-
| Matthew Bernacki || University of Pittsburgh ||
|-
| Gregory Dyke || Carnegie Mellon ||
|-
| Sherice Clarke || University of Pittsburgh ||
|-
| Oscar Saz || Carnegie Mellon University || LTI
|-
| Michael Yudelson || Carnegie Mellon University ||

|}

== Former Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Hua Ai || Georgia Institute of Technology || LTI
|-
| Alicia Chang || University of Delaware || Postdoctoral Researcher
|-
| Connie Guan Qun || University of Pittsburgh ||
|-
| Chin-LungYang || University of Pittsburgh ||
|-
| Scotty Craig || University of Memphis|| Research Assistant Professor, Institute for Intelligent Systems
|-
|}

== Faculty ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Al Corbett || Carnegie Mellon || HCII
|-
| Alan Juffs || University of Pittsburgh || Linguistics
|-
| Brian Junker || Carnegie Mellon || Statisics
|-
| Brian MacWhinney || Carnegie Mellon || Psychology
|-
| Bruce McLaren || Carnegie Mellon || HCII
|-
| Carolyn Rosé || Carnegie Mellon || LTI/HCII
|-
| Charles Perfetti || University of Pittsburgh || LRDC
|-
| Christa Asterhan || Hebrew University ||
|-
| David Klahr || Carnegie Mellon || Psychology
|-
| David Yaron || Carnegie Mellon || Chemistry
|-
| Geoff Gordon || Carnegie Mellon || Machine Learning
|-
| Jack Mostow || Carnegie Mellon || Robotics
|-
| Jim Greeno || University of Pittsburgh || Instruction and Learning
|-
| John Stamper || Carnegie Mellon || HCII
|-
| Ken Koedinger || Carnegie Mellon || HCII
|-
| Kirsten Butcher || University of Utah || Instructional Design & Educational Technology
|-
| Kurt VanLehn || Arizona State University || Computer Science and Engineering
|-
| Lauren Resnick || University of Pittsburgh || LRDC
|-
| Louis Gomez || University of Pittsburgh || School of Education
|-
| Marsha Lovett || Carnegie Mellon || Eberly Center
|-
| Mary Catherine O'Connor || Boston University || School of Education
|-
| Matthew Kam || Carnegie Mellon || HCII
|-
| Maxine Eskenazi || Carnegie Mellon || LTI
|-
| Nel de Jong || Vrije Universiteit Amsterdam ||
|-
| Niels Pinkwart || Clausthal University of Technology ||
|-
| Nikol Rummel || Ruhr-Universität Bochum || Psychology
|-
| Noboru Matsuda || Carnegie Mellon || HCII
|-
| Phil Pavlik || Carnegie Mellon || HCII
|-
| Richard Scheines || Carnegie Mellon || Philosphy
|-
| Ryan Baker || WPI ||
|-
| Sandy Katz || University of Pittsburgh || LRDC
|-
| Sarah Michaels || Clark University || Education
|-
| Teruko Matamura || Carnegie Mellon || LTI
|-
| Tim Nokes || University of Pittsburgh ||
|-
| Vincent Aleven || Carnegie Mellon || LTI
|-
| William Cohen || Carnegie Mellon || ML
|-
| Ma. Mercedes T. Rodrigo || Ateneo de Manila University
|| Information Systems and Computer Science

|}

== Staff ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| [[User:Alida|Alida Skogsholm]] || Carnegie Mellon University || DataShop Manager
|-
| Bob Hausmann || Carnegie Learning ||
|-
| Brett Leber || Carnegie Mellon University || DataShop/CTAT
|-
| Christy McGuire || Edalytics ||
|-
| Cressida Magaro || Carnegie Mellon University ||
|-
| Dorolyn Smith || University of Pittsburgh ||
|-
| Duncan Spencer || Carnegie Mellon University || DataShop
|-
| Gail Kusbit || Carnegie Mellon University || Research Manager
|-
| Jo Bodnar || Carnegie Mellon University ||
|-
| John Kowalski || Carnegie Mellon University ||
|-
| Jonathan Sewall || Carnegie Mellon University ||
|-
| Kevin Willows || Carnegie Mellon University ||
|-
| Mark Haney || University of Pittsburgh ||
|-
| Martin van Velsen || Carnegie Mellon University ||
|-
| Michael Bett || Carnegie Mellon University || Managing Director
|-
| Mike Karabinos|| Carnegie Mellon University ||
|-
| Ross Strader || Carnegie Mellon University ||
|-
| Sandy Demi || Carnegie Mellon University || DataShop/CTAT
|-
| Scott Silliman || University of Pittsburgh || OLI
|-
| Shanwen Yu || Carnegie Mellon University || DataShop
|-
| Steve Ritter || Carnegie Learning || Founder
|-
| Thomas Harris || Edalytics ||
|-
| Tristan Nixon || Carnegie Learning ||
|}

Juffs - Feature Focus in Word Learning

2011-09-09T19:07:49Z

Mbett: Reverted edits by Ularedmond (Talk); changed back to last version by Mbett

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! PI
| Ben Friedline, Alan Juffs
|-
! Start date
| September 2009
|-
! End date
| July 2010
|-
! Learnlab
| English
|}

== L2 learning of derived words ==
Benjamin Friedline and Alan Juffs

'''Background'''

Inflected and derived words occur frequently in the English language and serve important functions in everyday communication. The term derived word refers to the combination of a base word with a derivational affix. For instance, if the derivational affix –ness is added to the base kind (adjective), the word kindness (noun) is derived. The affix –ness is very productive and can be added to many words to derive novel words such as darkness, awareness, and illness. Other derivational affixes such as –ity are not as productive as –ness and can be used to form a limited number of words, such as ¬purity and scarcity. The term inflected word refers to the combination of a base word and an inflectional affix. For example, if the inflectional affix –ed is added to the base word walk (verb), the resulting word is walked (verb). The addition of the –ed affix is very productive in the formation of the past tense even though it does not apply to a number of irregular past tense forms, such as drove, ate, and sat.

Importantly, the differences in productivity of each type of affix have led some researchers to conclude that there are differences in how they are processed by native speakers. In Words and Rules (WR) theory, Pinker and Ullman (2002) argue that irregular inflected forms (e.g., drove) are stored in the lexicon (or mental dictionary) as whole words, whereas regular forms (e.g., walked) are generated by a regular rule. This theory has also been applied to the processing of derived words in two recent psychological investigations (cf. Alegre & Gordon, 1999; Hagiwara et al., 1999). The main point behind both of these studies is to illustrate that derived words can be either rule-governed (e.g., words with –ness) or stored as whole words in the lexicon (e.g., words with –ity as in purity).
[http://www.bestessays.com custom pappers]
In the area of second language acquisition, adult second language (L2) learners often fail to attain native-like proficiency when producing derived and inflected words in an L2. Lardiere (1998), for instance, showed that second language learners still make errors with inflectional morphology even after many years of exposure to English. In Lardiere’s (1998) study, she recorded and analyzed naturalistic conversations from a Chinese learner of American English. The results of this study indicated that the learner supplied the inflectional affix –ed correctly in only 34% of obligatory contexts even after 18 years of exposure to English. Additionally, in terms of derived words, a recent study by Juffs and Friedline (2010) revealed that intermediate L2 learners often made errors in the production of derived words such as those in examples (1) and (2).
(1) We have one different [difference].
(2) I like doing something music [musical].
In example (1) the learner uses the adjective form different instead of the grammatically correct form difference, which is a noun. In example (2) the learner uses the noun form music in a position that requires the adjective form musical.

The preponderance of such errors in L2 speech has led some researchers to conclude that L2 learners are permanently impaired on the production of derived and inflected words because they do not have access to the same rule-based mechanisms that are present during L1 acquisition (Jiang, 2004; Felser & Clahsen, 2009; Silva & Clahsen, 2008). This hypothesis is formally known as the Fundamental Difference Hypothesis (FDH; Bley-Vroman, 1989), and it has received support from a number of recent studies in the field of second language acquisition. Silva and Clahsen (2008), for instance, use evidence from a masked-priming experiment to compare native speakers to adult L2 learners on a series of morphological priming tasks. The results from this study indicated full priming effects (e.g., darkness primes dark) for native speakers on both inflections and derivations, but only partial priming effects for L2 learners on derivations and no priming effects for L2 learners on inflections. Silva and Clahsen (2008) argue that the limited priming effects (or complete lack thereof) indicate that L2 learners lack rule-based mechanisms and do not know that ¬–ness can be affixed to many adjectives to derive nouns such as darkness, awareness, and illness. This lack of rule-based mechanisms may mean that adult L2 learners memorize all words as unanalyzed chunks of language, without realizing that dark and darkness or walk and walked are intimately related in both form and meaning.

The research by Silva and Clahsen (2008) makes an important contribution to a theory of second language acquisition because it provides a possible explanation for why L2 learners have difficulties with derived and inflected words (i.e., they cannot access rule-based mechanisms). However, this research is limited because it assumes that L2 learners are permanently impaired when compared to native-speakers on all types of rule-based inflectional and derivational morphological processes. This assumption is at odds with findings from studies such as the morpheme order studies (e.g., Bailey, Madden, & Krashen, 1974), critical period studies (e.g., Johnson & Newport, 1989), and studies on the role of morpheme salience in L2 acquisition (e.g., Ellis, 2006) in that these past studies indicate that certain morphemes, such as progressive, may be more easily acquired than others. Johnson and Newport (1989), for instance, claim that the progressive morpheme may not be subject to critical period effects.

Additionally, the role of a learner’s first language may also influence the relative difficulty of a particular morpheme. Potential L1 effects have been discussed in a number of recent SLA studies (e.g., Juffs & Friedline, 2010; White, 2003). Before we conclude that L2 learners are equally impaired in all areas of morphological knowledge, further research is needed to identify if certain morphological structures are easier to acquire than others, how exactly L2 morphological knowledge diverges from native-speaker knowledge, and how a learner’s first language might influence L2 morphological knowledge. The goal of the present research is to answer these questions as they relate to derivational morphology.

'''Research Questions'''

Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for learning through normal language exposure? Do learners have a representational problem or a processing problem? Specifically, what instructional interventions can be designed to overcome observed processing differences in L1 and L2 morphology?

'''Research plan'''

For year 1, the goal of the research is to analyze the knowledge components of ESL learners to lay the groundwork for a hypothesis-based intervention. The research will systematically investigate the components of L2 learners’ knowledge of English derivational morphology to address the following questions:

1) What are the components of L2 derivational knowledge?
2) Are these components different from L1 derivational knowledge?
3) Does L1 matter for the acquisition of derived words in an L2?

'''Methodology'''

To answer these questions, Friedline has developed a series of tasks that will be used to assess what native English speakers and second language learners know about derived words. These tasks included lexical decision, semantic relatedness, and morphological decomposition. Each of these tasks contained several conditions that tested different components of morphological knowledge. Studies on the acquisition of L1 morphological knowledge (e.g., Carlisle, 2000; Carlisle & Fleming, 2003) were consulted in order to develop these conditions. Each condition is outlined below.

''Lexical decision task''

Explanation: In this task, students were asked to rate words from 1 (not a word) or 6 (definitely a word). All words were morphologically complex (e.g., base + affix). Some of the words were real words in English, while other words were not real words in English. The purpose of this task was to assess if native-speakers were sensitive to the effects of semantic blocking and affix ordering. There were four conditions in this task. The conditions are listed below along with an example to illustrate the types of words that were presented in each condition.

Condition 1: Real words
Example: The suffix –able is added to verbs to derive adjectives such as workable or comfortable. A response of 4, 5, or 6 would be counted as accurate.

Condition 2: Semantic blocking
Example: Even though you can add the affix –able to many verbs to derive adjectives, there are some verbs like arrivable and departable look that do not normally take the suffix –able to form adjectives. A response of 1, 2, or 3 would be counted as accurate.

Condition 3: Correct affix ordering
Example: There are some bases that can take two affixes. You can add the affix –able to the verb respect to derive the adjective respectable. Then, you can add the affix –ity to respectable to derive the noun respectability. A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Incorrect affix ordering
Example: In a word like respectability, the word is correct because the affixes are added in the correct order. However, if I add the affix -ity before I add the affix –able, I derive a word like respectitiable. This word is not correct because the affixes are not added in the correct order. A response of 1, 2, or 3 would be counted as accurate.

''Word relatedness task''

Explanation: In this task, students were asked to rate words based on their meaning from 1 (not related) to 6 (definitely related). There were five conditions in this exercise.

Condition 1: No relationship in meaning
Some words are not related in meaning in any way. The words cat and bus are not related in meaning in any way. A response of 1, 2, or 3 would be counted as accurate.

Condition 2: Relationship in meaning
Other words are related in meaning. For instance, bank and money are related in that a bank is a place where you deposit your money. A response of 4, 5, or 6 would be counted as accurate.

Condition 3: Relationship in meaning with different affixes.
This condition contained words with suffixes that were related in meaning. For example, productive (adj.) and production (n.) both share the base produce (v.). A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Relationship in orthography only, not meaning
There are some words that may look like they are related in meaning because they share the same initial letters. In this condition, students saw words like permanence and permission. These words share the letters p-e-r-m, but are unrelated in meaning. A response of 1, 2, or 3 would be counted as accurate.

Condition 5: Relationship in affix only, not meaning
In the final condition, students were presented with words that shared the same affix, but were unrelated in meaning. For example, the words reality and curiosity are unrelated in meaning, but share the affix –ity. A response of 1, 2, or 3 would be counted as accurate.

''Word Analysis Task''

Explanation: On the Word Analysis Task, students were asked to provide the base word of the word provided. Some of these words consisted of a base and an affix such as musician, which has music as a base. Other words, however, could not be broken down into a base and a affix. For instance, dollar cannot be broken down into doll + ar because dollar is a base form. Accuracy was computed for decomposable and non-decomposable words.

Native speakers piloted these tassk in the fall of 2009, and preliminary results are reported for each task in the tables below. A pull out from the ELI in Spring 2010 will collect learner data.

''Participants''

These tasks were administered to native speakers and L2 learners during the fall 2009 and spring 2010 semesters. A total of 23 native-English speakers participated in the study. All of the native speakers were undergraduates at the University of Pittsburgh. Ninety ESL learners participated in this study from three different levels of language proficiency: beginner (n=26), intermediate (n=36), and advanced (n=28). These learners were enrolled in an intensive English program at the University of Pittsburgh.

'''Descriptive Results'''

''Lexical Decision Task''

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Real words
| 93%
|-
! Condition 2: Semantic blocking
| 81%
|-
! Condition 3: Correct affix ordering
| 93%
|-
! Condition 4: Incorrect Affix ordering
| 95%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Real words
| 89%
|-
! Condition 2: Semantic blocking
| 48%
|-
! Condition 3: Correct affix ordering
| 66%
|-
! Condition 4: Incorrect Affix ordering
| 53%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Real words
| 90%
|-
! Condition 2: Semantic blocking
| 59%
|-
! Condition 3: Correct affix ordering
| 69%
|-
! Condition 4: Incorrect Affix ordering
| 70%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Real words
| 96%
|-
! Condition 2: Semantic blocking
| 53%
|-
! Condition 3: Correct affix ordering
| 77%
|-
! Condition 4: Incorrect Affix ordering
| 81%
|}

''Word relatedness task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: No relationship in meaning
| 92%
|-
! Condition 2: Relationship in meaning
| 92%
|-
! Condition 3: Relationship in meaning with different affixes
| 97%
|-
! Condition 4: Relationship in orthography only
| 89%
|-
! Condition 5: Relationship in affix only
| 90%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: No relationship in meaning
| 91%
|-
! Condition 2: Relationship in meaning
| 84%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 73%
|-
! Condition 5: Relationship in affix only
| 74%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: No relationship in meaning
| 89%
|-
! Condition 2: Relationship in meaning
| 87%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 70%
|-
! Condition 5: Relationship in affix only
| 69%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: No relationship in meaning
| 95%
|-
! Condition 2: Relationship in meaning
| 79%
|-
! Condition 3: Relationship in meaning with different affixes
| 96%
|-
! Condition 4: Relationship in orthography only
| 81%
|-
! Condition 5: Relationship in affix only
| 89%
|}

''Word Analysis Task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Decomposable
| 85%
|-
! Condition 2: Non-decomposable
| 92%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Decomposable
| 59%
|-
! Condition 2: Non-decomposable
| 73%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Decomposable
| 61%
|-
! Condition 2: Non-decomposable
| 84%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Decomposable
| 65%
|-
! Condition 2: Non-decomposable
| 81%
|}

'''Discussion of descriptive statistics'''

''Beginning L2 Learners (Level 3)''

The results of this study indicate that level 3 learners from the ELI at the University of Pittsburgh often have problems when processing derived words. Firstly, the lexical decision task may indicate that beginning learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Sixteen of 26 level 3 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 18 of 26 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 12 out of 26 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, almost all of the level 3 learners (22 of 26) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Intermediate L2 Learners (Level 4)''

For Level 4 L2 learners, the lexical decision task may indicate that even intermediate-level learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Seventeen of 36 level 4 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 21 of 36 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 14 out of 36 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, more than two-thirds of the level 4 learners (25 of 36) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Advanced L2 Learners (Level 5)''

For level 5 L2 learners, the lexical decision task may indicate that even advanced learners are not sensitive to constraints on the use of morphemes such as –able and –ness. For instance, the learners in the present study often judge words such as smileable and leavable to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Fourteen of 28 level 5 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for leavable, 19 of 28 learners said that this was a real word, but only 4 of 23 natives considered leavable to be a real English word. Second, the word relatedness task seems to indicate that advanced learners still rely to some degree on orthographic/phonological overlap when processing the meaning of words. Put another way, some advanced learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 11 out of 28 level 3 learners said that the word pair constantly-conservative were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at level 5 have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, many of the errors on the word analysis task were errors on words that involved a significant orthographic and sometimes phonological change to the base. For instance, 19 of 28 level 5 students incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

'''General Discussion'''

This section reports the results of this study in connection with the four original research questions. The first question was primarily concerned with determining the knowledge components of second language derivational knowledge. Based on the results of Study 1, second language learners knew the following about derived words in English:
(1) Knowledge of highly frequent derived words.
(2) Knowledge that derived words can be broken down into bases and affixes.
At the same time, the results of Study 1 also provided some indication of areas of weakness in L2 derivational knowledge. Knowledge components that second language learners may have lacked are listed below:
(1) Knowledge of constraints on affix attachment or affix ordering.
(2) Knowledge that overlap in orthography/phonology does not imply overlap in meaning.
(3) Knowledge that derivation sometimes involves phonological changes to a base word.

The second research question asked whether the components of L2 derivational knowledge were different than the components of L1 derivational knowledge. The results of study 1 indicate that L2 derivational knowledge is significantly different (p < .05) from native speaker knowledge. In short, native speakers demonstrated knowledge of derivational morphology that non-natives were shown to lack. For instance, on the lexical decision task natives (accuracy = 95%) clearly knew when affix ordering constraints had been violated, whereas non-natives (accuracy = 69%) demonstrated limited knowledge of these constraint violations.
The remaining two research questions pertained to: 1) influences from linguistic background and 2) influences from English language proficiency. In large part, the results from Study 1 suggest that linguistic background and proficiency made little difference in how language learners performed on tasks related to derivational morphology. In short, such factors have no statistically significant effect (p > .05) on how second language learners perform on tasks related to word-relatedness or word analysis. Nonetheless, there is some evidence from the lexical decision task that group and proficiency may matter for performance on grammaticality judgments in that learners with Korean and Romance language backgrounds tended to outperform learners from Arabic and Chinese language backgrounds on words that violated constraints on English word formation.

In terms of second language acquisition theory, the results of Study 1 may indicate that non-native speakers have little difficulty recognizing high frequency derived words (e.g., darkness), but they have significant difficulty when confronted with words that do not exist in English (e.g., arrivable) or words that involve complex morphological operations such as affix ordering (e.g., thoughtfulness vs. thoughtnessful). Recent work in psycholinguistics may provide a partial explanation for these findings. That is, research on the processing and storage of derived words shows that derived words may be either stored in lexical memory or else produced by a generative rule-governed mechanism (e.g., Alegre & Gordon, 1999; Hagiwara et al., 1999). The data from L2 learners presented here may imply that learners excel at recognizing highly frequent derived words, but are in a sense ‘impaired’ when using rule-based mechanisms to generate (or in this case recognize) that constraints on affix attachment or ordering are being violated. These findings are also consistent with Silva and Clahsen’s (2008) findings from priming experiments involving native and non-native performance on derived words. More specifically, Silva and Clahsen (2008) argue that limited priming effects on derived words among L2 learners evinces impairment to rule-based mechanisms, meaning that L2 learners must rely largely on lexical memory when acquiring derived words in English.

**Additional statistical results and key theoretical discussion is forthcoming in the first author's doctoral dissertation.**

'''Next steps'''

For the 2010-2011 academic year, I am developing a morphology intervention based on the results of the study I completed this year. This will be an in vivo study that I will pilot in the fall 2010 semester and run in the ELI classrooms during the spring 2011 semester. The design of this study includes a pretest, an intervention, and a post-test to assess gains in morphological knowledge. The intervention portion of this study will teach: 1) constraints on affix attachment (e.g., affix ordering) and 2) relational knowledge between base words and related derived words (e.g., creation and creative are related to the base create), which are areas of weakness for adult second language learners based on the results of Study 1. Key research questions for this project include the following: 1) Does instruction on derived words enhance L2 sensitivity to constraints on affix attachment?, 2) What type of instruction works best for teaching constraints on derived words?, and 3) Is L2 knowledge of derived words fundamentally different than that of native speakers? This project directly relates to the "Focus on valid features in word learning" CF goal as well as the "learner background" goal.

''Robust learning of derivational morphology''

One of the core components of the PSLC theory of robust learning is foundational skill building. Foundational skill building refers to the knowledge or skill that “must be mastered in order to provide for subsequent learning” (http://learnlab.org/clusters). The findings from Study 1 relate to this construct in that they provide direct evidence of the knowledge components of derivational morphology that adult second language learners have not yet mastered in relation to adult native-speaker peers. Study 1 does not directly explore the learning processes involved in learning derivational morphology; however, it does provide a foundation for the design of an intervention study that directly investigates such processes. Study 2 builds on Study 1 in the design of an intervention study that seeks to identify how different types of instruction (conditions in PSLC terminology) contribute to the mastery of the knowledge components underlying derived word knowledge. More specifically, Study 2 compares traditional output-based instruction (Swain, 1985) with input-processing instruction (VanPatten, 1996) as the learning conditions for knowledge components underlying derived word knowledge. In terms of the broader PSLC theoretical framework, Study 2 seeks to identify the contributions of different instructional methods to the robust learning of derivational morphology. In terms of the cognitive factors thrust goals, this project most directly relates to to the "Focus on valid features in word learning.”

''Project plan for AY 2010-2011''

1) September 2010 – Complete materials for intervention study (study 2)

2) October 2010 – Defend dissertation overview based on this research

3) October 2010 – Pilot test pretest materials with a pull-out sample from the ELI

4) November 2010 – Analyze results from pilot study and determine appropriate course of action for morphology intervention.

5) Spring 2011 – Implement morphology intervention in the ELI classroom.

6) Summer 2011 - Analyze data and begin to write dissertation

7) Fall 201l - Work on dissertation

'''Selected References'''

Alegre, M., & Gordon, P. (1999). Rule-based versus associative processes in derivational morphology. Brain and Language, 68, 347-354.

Bailey, N., Madden, C., & Krashen, S. (1974). Is there a "natural sequence" in adult second language learning? Language Learning, 24(2), 234-243.

Bley-Vroman, R. (1989). The logical problem of second language learning. In S. Gass & J. Schachter (Eds.), Linguistic Perspectives on Second Language Acquisition. Cambridge: Cambridge University Press.

Carlisle, J.F. (2000). Awareness of the structure and meaning of morphologically complex words: Impact on reading. Reading and Writing, 12(3-4), 169-190.

Carlisle, J. F., & Fleming, J. (2003). Lexical processing of morphologically complex words in the elementary years. Scientific Studies of Reading, 7(3), 239-253.

Ellis, N. C. (2006). Selective attention and transfer phenomena in L2 acquisition: Contingency, cue competition, salience, interference, overshadowing, blocking, and perceptual learning. Applied Linguistics, 27(2), 164-194.

Felser, C., & Clahsen, H. (2009). Grammatical processing of spoken language in child and adult language learners. Journal of Psycholinguistic Research, 38(3), 305-319.

Gonnerman, L. M., Seidenberg, M. S., & Andersen, E. S. (2007). Graded semantic and phonological similarity effect in priming: Evidence for a distributed connectionist approach to morphology. Journal of Experimental Psychology, 136(2), 323-345.

Hagiwara, H., Sugioka, Y., Ito, T., Kawamura, M., & Shiota, J.-i. (1999). Neurolinguistic evidence for rule-based nominal suffixation. Language, 75(4), 739-763.

Hay, J. (2002). From speech perception to morphology: Affix ordering revisited. Language, 72(3), 527-555.

Hay, J. B., & Baayen, R. H. (2005). Shifting paradigms: gradient structure in morphology. TRENDS in Cognitive Science, 9(7), 342-348.

Jiang, N. (2004). Morphological insensitivity in second language processing. Applied Psycholinguistics, 25, 603-634.

Johnson, J. S., & Newport, E. L. (1989). Critical period effects in second language learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology, 21, 60-99.

Friedline, B., & Juffs, A. (2010). L1 influence, morphological (in)sensitivity and L2 lexical development: Evidence from production data. Unpublished manuscript, University of Pittsburgh, PA.

Lardiere, D. (1998). Dissociating syntax from morphology in a divergent L2 end-state grammar. Second Language Research, 14(4), 359-375.

Lardiere, D. (2006). Ultimate attainment in second language acquisition: a case study. New York: Routledge

Marslen-Wilson, W. D., Bozic, M., & Randall, B. (2008). Early decomposition in visual word recognition: Dissociating morphology, form, and meaning. Language and Cognitive Processes, 23(3), 394-421.

Nation, I. S. P. (2001). Learning vocabulary in another language. New York: Cambridge University Press.

Pinker, S., & Ullman, M. T. (2002). The past and future of the past tense. TRENDS in Cognitive Science, 6(11), 456-463.

Silva, R., & Clahsen, H. (2008). Morphologically complex words in L1 and L2 processing: Evidence from masked priming experiments in English. Bilingualism: Language and Cognition, 11(2), 245-260.

masked priming experiments in English. Bilingualism: Language and Cognition, 11(2), 245-260.

Swain, M. (1985). Communicative competence: Some roles of comprehensible input and comprehensible output in its development. In S. M. Gass & C. G. Madden (Eds.), Input in second language acquisition (pp.

235-253). Rowley, MA: Newbury Hours.

VanPatten, B. (1996). Input processing and grammar instruction in second language acquisition. Norwood, NJ: Ablex.

White, L. (2003). Fossilization in steady state L2 grammars: Persistent problems with inflectional morphology. Bilingualism: Language and Cognition, 6(2), 129-141.

'''Publications''' (All of these papers cite the original PSLC award (years 1-5) SBE-354420.)

Friedline, B. & Juffs, A. (2010). L1 influence, morphological (in)sensitivity and L2 lexical development: Evidence from production data. The University of Pittsburgh. (Revise and resubmit).

Juffs, A., Friedline, B., Wilson, L., Eskenazi, M. & Heilman, M. (2010). Activity theory and computer assisted learning of English vocabulary. The University of Pittsburgh. (Revise and resubmit).

Friedline, B., & Shirai, Y. (2010). Animacy and second language acquisition of English relative clauses. The University of Pittsburgh. (In preparation for peer review).

'''Conference presentations''' (This presentation cites the original PSLC award (years 1-5) SBE-354420.)

Friedline, B., & Juffs, A. L1 influences on the development of L2 morphosyntactic features. Pennsylvania Association of Applied Linguistics Conference (PAALC). State College: Pennsylvania State University. January 2010.

Klahr - TED

2011-09-09T19:07:31Z

Mbett: Reverted edits by Ularedmond (Talk); changed back to last version by Cmagaro

== Project Overview ==

TED (Training in Experimental Design)

Co-PI: Dr. David Klahr, Carnegie Mellon University, Department of Psychology 
Co-PI: Dr. Stephanie Siler, Carnegie Mellon University, Department of Psychology

== Abstract ==

The aim of this project on Training in Experimental Design (TED) is to develop a computer-based intelligent tutoring system to improve science instruction in late elementary through middle school grades. The proposed intervention focuses on the conceptual understanding and procedural skills of designing and interpreting scientific experiments. The primary support of the project has come from the Institute of Education Sciences over the past 6 years: the specific aspect of TED that is supported by the PSLC's cognitive thrust involves a professional development component, in which the TED researchers work with a teacher in the Pittsburgh Public School's new Science and Technology Academy to develop effective implementations of TED, and to create plausible comparisons between TED's method and content, and the method and content of "normal" teacher delivered instruction on experimental design. Although the long term aim of the TED project is to transform TED-1 from a non-adaptive "straight line" instructional delivery system into an intelligent adaptive tutor (TED-2), the current PSLC project involves empirical studies of comparisons between human and TED-1, delivering a non-adaptive lesson plan.

== Background and Significance ==

A thorough understanding of the “Control of Variables Strategy" (CVS) is essential for doing and understanding experimental science, whether it is school children studying the effect of sunlight on plant growth, or consumers attempting to assess reports of the latest drug efficacy study. As fundamental to the scientific enterprise as CVS is, it tends to be taught in a shallow manner. Typically, only its procedures are explicitly taught, while the conceptual basis for why those procedures are necessary and sufficient for causal inference is seldom addressed. Brief statements about experimental design procedures, without subsequent instruction about the rationale for such designs, appear to be the norm in science textbooks, though there are notable exceptions (e.g., Hsu, 2002). Even when students are provided with rich interactive contexts in which to design experiments (Kali, & Linn, 2008; Kali, Linn, & Roseman, 2008), the focus for any particular module is on using experimentation to advance domain knowledge, rather than on CVS per se as a domain-general acquisition.

The consequences of meager levels of CVS instruction are clear. A substantial body of research evidence shows that, absent explicit instruction, CVS is not easily learned. For example, Kuhn, Garcia-Mila, Zohar and Andersen’s (1995) classic study demonstrated that—in a variety of scientific discovery tasks in which participants explored the effects of several variables—even after 20 sessions spread over 10 weeks, fewer than 25% of 4th grader’s inferences were valid. Other studies of children’s understanding of evidence generation and interpretation (e.g., Amsel & Brock, 1996; Chen & Klahr, 1999; Bullock & Ziegler, 1996; Dean & Kuhn, 2007; Klahr, 2000; Klahr & Nigam, 2004; Schauble, Klopfer, & Raghavan, 1991, Strand-Cary & Klahr, 2008) reveal their fragile initial grasp of CVS, and the difficulty that they have in learning it.

Over the past three years, our project has developed computerized instruction (“TED” for “Training in Experimental Design”) based on the method of “direct” CVS instruction found effective by Klahr and colleagues (e.g., Chen & Klahr, 1999; Klahr & Nigam, 2004; Strand-Cary & Klahr, 2008). We recently compared student learning and transfer from TED-delivered instruction to the same instruction delivered by human tutors using physical materials and found no outcome differences. However, “direct” CVS instruction has never been compared to lessons aimed at teaching CVS; thus, we wanted to know how TED-delivered instruction compared to a teacher-delivered lesson on CVS in a school curriculum. Findings that TED-delivered instruction leads to higher transfer rates than the control lesson would be practical justification for incorporating TED within the science curriculum.

Furthermore, in prior analyses, we found that students’ complete causal explanations of CVS (e.g., in which they explained that an unconfounded experiment was “good” because only the target variable could cause any differences in outcomes or explained that an experiment was not good because variables other than the target variable could also cause differences in outcomes, and so it’s not possible to know whether the target variable was causal) given during the “direct” instruction were significantly correlated with transfer performance. Thus, we were interested in whether the addition of more directive questions in TED, asking students to identify the potential causal factors in experiments, would lead to improved transfer performance. Increases in transfer rates support the hypothesis that understanding the determinacy/indeterminacy of outcomes is a key knowledge component for CVS transfer.

== Glossary ==

<ul><li>CVS: control of variables strategy. The procedures for designing simple, unconfounded experiments by varying only one thing at a time and keeping all others constant.</li>
<li>Target varialble: the factor whose values are varied in order to determine whether or not that factor is causal with respect to the outcome.</li>
<li>Direct Instruction vs Discovery Learning: two important, contentious, and extraordinarily difficult to define constructs unless clear operational descriptions are provided that fully convey the instructional context for each. See Tobias & Duffy (2009) and Strand-Cary & Klahr, 2008, for a full discussion. </li></ul>

== Research Questions ==

How does the TED tutor—even in its “non-intelligent” state—compare with an alternative lesson on CVS delivered by a live teacher in terms of CVS transfer rates, student enjoyment of the lesson, and of science more generally?

Does making a hypothesized “key” knowledge component more explicit via asking targeted questions lead to improved transfer performance? That is, does also asking students to identify the possible causal factors in a confounded experiment (i.e., the reason why it is necessary to control variables) promote far-transfer above telling students?

== Independent Variables ==

The study compared three conditions in which two classes of 8th-grade students at a local school, both taught by the same teacher, received instruction in CVS in their regular science classrooms. The first independent variable manipulated was the instructional lesson; in one classroom, students received a teacher-delivered CVS lesson from the CPO science textbook (Condition 1), and in the other classroom students received TED-delivered CVS instruction (Conditions 2.1 and 2.2).
<ul><li>(Condition 1) The control condition, in which students participated in a whole-class teacher-directed lesson with a laboratory component, as aligned with their science textbook and curriculum.</li></ul>
The second independent variable, manipulated within the two TED conditions, is the addition of causal questions.
<ul><li>(Condition 2.1) “Explanation only” (EO) TED condition, in which students used a computerized tutor to cover the same content, i.e., to learn how and why to create controlled experiments.</li>
<li>(Condition 2.2.) “Added Questions” (AQ) TED condition, in which students used a computerized tutor that had been slightly modified to include an additional question during each exercise that asked them to identify what variables, if any, could affect experimental results.</li></ul>

== Dependent Variables ==

<ul><li>Ramps test (TED conditions only), in which students design four experiments, each to test a different variable. Because both the domain and task demands are the same as those of instruction, this is considered a measure of near transfer.</li>
<li>“Story” test (all conditions), in which students design 3 experiments and evaluate 3 experiments in three different contexts (designing rockets, baking cookies, and selling drinks). Because this assessment covers problems that have similar task demands, but with formatting and domains that differ from the content of the lesson, we consider this to be an assessment of far-transfer.</li>
<li>“Standardized question” test (all conditions), comprised of four CVS problems gleaned from age-appropriate standardized tests (TIMSS and NAEP). Because the items on this assessment differed from instruction both in the domains and task demands, we consider this to be an assessment of distant-transfer.</li></ul>

=== Study Procedure ===

''Table 1. Study Procedure.''
<table width="900" cellspacing="1" cellpadding="1" border="1"><tr><td> </td><td style="text-align: center;">Control</td><td style="text-align: center;">TED</td></tr><tr><td>Story Pre-Test</td><td colspan="2" style="text-align: center;">(One day before intervention) 
(computerized; 6 questions: 3 design; 3 evaluate + explanations)</td></tr><tr><td>Intervention</td><td>Day 1: <ul><li>Intro to lesson (including statement of CVS logic)</li><li>Intro to experiment (exp question, hypothesis)</li><li>Ran (hands-on) ramps experiment</li><li> Discussed of results/confounds (CVS logic)</li></ul>
Day 2
<ul><li>Re-run controlled exp</li>
<li>Summarized experiment (class)</li></ul>

</td><td>Day 1:
<ul><li>Video intro to lesson </li><li>Ramps intro and pretest </li><li>Explicit instruction (EO or AQ) experiment #1</li></ul>
Day 2
<ul><li>Explicit instruction (experiments 2 & 3)</li><li>Video summary</li><li>Ramps posttest</li></ul></td></tr><tr><td>Immediate Story Post-Test</td><td style="text-align: center;" colspan="2">(Day 2) 
(computerized; 6 questions: 3 design; 3 evaluate + explanations)
</td></tr><tr><td> Immediate Standardized posttest</td><td style="text-align: center;" colspan="2">(Day 2) 
(paper/pencil; 4 questions)
</td></tr><tr><td style="text-align: center;" colspan="3">(Three weeks later…after some intervening class instruction that touched on CVS.) </td></tr><tr><td> Motivational survey</td><td colspan="2"> (paper/pencil: Students were asked how much they enjoyed different portions of instruction and if the lesson affected their interest in science)</td></tr><tr><td>Story follow-up </td><td style="text-align: center;" colspan="2">(paper/pencil) </td></tr><tr><td>Standardized follow-up</td><td style="text-align: center;" colspan="2">(paper/pencil) </td></tr></table>

== Findings ==

=== TED: Explanation only vs. Additional Questions comparisons. ===

==== Immediate near-transfer performance ====
Ramp posttest (near transfer) performance: Comparing TED-AQ students to TED-EO students, there was a significant ramps pretest by condition interaction (Figure 1 below), where there was a significant relationship between ramps pre and posttest only for students in the EO condition. Thus, the added question aided near transfer performance, especially for lowest-scoring students.

''Figure 1. Ramps post by pre and condition.'' 
[[Image:Wiki_figure1.jpg]]

==== Immediate far-transfer performance ====
As shown in Figure 2 (and Table 2), TED students who were asked the additional question did not significantly out-perform students who were not asked this question on the immediate Story posttest, ''F''(1, 8) = 0.16, ''p'' = .70. Similarly, there was no difference in performance on the immediate standardized posttest, ''F''(1, 8) = 1.64, ''p'' = .24.

''Table 2. Means (and standard deviations) by test type and time.''
<table width="500" cellspacing="1" cellpadding="1" border="1"><tr><td> </td><td style="text-align: center;" colspan="2">Immediate </td><td style="text-align: center;" colspan="2">Follow-Up </td></tr><tr><td> Condition</td><td style="text-align: center;"> Story</td><td style="text-align: center;"> Standardized</td><td style="text-align: center;">Story </td><td> Standardized</td></tr><tr><td> Additional Question</td><td> 4.17 (1.33)</td><td>2.00 (0.89) </td><td>5.17 (1.60) </td><td>3.40 (1.14) </td></tr><tr><td> Explanation only</td><td> 3.00 (2.45)</td><td> 2.40 (0.89)</td><td>2.75 (2.75) </td><td> 2.80 (1.64)</td></tr></table>

==== Follow-up performance ====
On the follow-up Story posttest, though students in the question condition tended to score higher than those in the explanation-only condition (Table 2), this difference just missed significance, ''F''(1, 7) = 3.44, ''p'' = .11. Story pretest and reading level (where below basic = 0; basic = 1; proficient = 2; advanced = 3) were included in the ANCOVA. As in the immediate standardized posttest, there was no difference between conditions in performance on the follow-up standardized posttest, ''F''(1, 7) = 0.02, ''p'' = .90.

==== Immediate to follow-up gains ====
However, the added-question students showed marginally higher Story test gains from the immediate to follow-up posttest, ''F''(1, 10) = 4.55, ''p'' = .06 (ANCOVA, factoring out Story post; reading was not a significant source of variance, and was removed from the model, ''F''(1, 11) = 5.17, ''p'' = .04, repeated measures ANOVA). Students who answered the additional questions gained significantly between the immediate and follow-up posttests, whereas the performance of students in the explanation-only condition did not differ from immediate to follow-up post.

''Figure 2. Story performance by time and condition.'' 
[[Image:Wiki_figure2.jpg]]

=== Control vs. TED ===

Next, we will discuss comparisons of TED to Control instruction. First, it is important to note that instruction in the Control condition took significantly longer (40 minutes) than in the TED conditions. Furthermore, students in the TED conditions took significantly less time than students in the Control conditions to complete the immediate Story posttest (6.88 and 9.85 minutes, respectively, ''p'' < .01).

==== Immediate performance ====
Story posttest performance: Students in the TED conditions scored significantly higher on the immediate Story posttest (Table 3 and shown in Figure 3) than students in the Control condition, ''F''(1, 22) = 4.78, ''p'' = .04, factoring out Story pretest and reading level, both of which were significantly correlated with Story posttest score.

''Table 3. Means (and standard errors) for immediate posttests.''
<table width="500" cellspacing="1" cellpadding="1" border="1">
<tr><td>Condition</td><td>Story Post</td><td>Adjusted Story Posta</td><td>Standardized</td><td>Adjusted Standardizedb</td></tr><tr><td>Control</td><td>2.53 (2.23)</td><td>2.58 (0.45)</td><td>2.57 (1.22)</td><td>2.60(0.27)</td></tr><tr><td>TED</td><td>3.50 (2.10)</td><td>4.08 (0.51)</td><td>2.18 (0.87)</td><td>2.14 (0.31)</td></tr></table>
a adjusted for Story pretest, reading level 
b adjusted for Story pretest (reading level was not significantly related to standardized test)

However, there were no differences on the four standardized items (Table 3), ''F''(1, 22) = 1.25, ''p'' = .28, again factoring out both Story pretest and reading level.

''Table 4. Means (and standard errors) for follow-up posttests.''
<table width="500" cellspacing="1" cellpadding="1" border="1">
<tr><td>Condition</td><td>Story</td><td>Adjusted Storya</td><td>Standardized</td><td>Adjusted Standardizedb</td></tr><tr><td>Control</td><td>3.21 (2.12)</td><td>3.13 (0.49)</td><td>2.71 (0.99)</td><td>2.74 (0.27)</td></tr><tr><td>TED</td><td>4.20 (2.35)</td><td>4.32 (0.58)</td><td>3.00 (1.41)</td><td>2.96 (0.34)</td></tr></table>
aadjusted for Story pretest, reading level 
badjusted for Story pretest (reading level was not significantly related to measure

==== Follow-up performance ====
On the Story follow-up paper-pencil test taken three weeks after instruction, though TED students tended to score higher than Control students (Table 4), this difference was no longer significant, ''F''(1, 20) = 2.44, ''p'' = .13. Both Control and TED-AQ students (though not TED-EO students) gained significantly between the immediate and follow-up Story test. These gains may be due to teacher-reported intervening incidental in-class discussions of CVS in subsequent lessons on scientific method.

As with the immediate test, on the standardized follow-up test, there was no difference between conditions (Table 4), ''F''(1, 20) = 0.25, ''p'' = .62.

==== Motivational Survey ====
Finally, there was no difference between the Control and TED conditions in students’ reports of the effect of the lesson on how much they like science (Figure 3).

''Figure 3. Frequency of responses to “How would you say doing this lesson made you feel about science (check one)?”'' 
[[Image:Wiki_figure3.jpg]]

== Explanation ==

In summary, comparing the control group with the TED groups, students in the TED conditions performed significantly higher on the immediate far-transfer Story post-test, but there were no differences on the distant-transfer “standardized” immediate or follow-up posttests. Though students in the TED conditions continued to out-perform their Control counterparts on the follow-up Story posttest, this difference was no longer significant. It should be reiterated, however, that the students in the TED condition spent 40 minutes less in completing their lesson than the control condition, thus achieving greater or equal scores on the post-tests while having spent much less time covering the content. Thus, the TED-delivered instruction was more efficient than Control instruction. Furthermore, there were no differences in students’ report of the impact of the lesson on their liking science, though students in the Control condition performed experiments using expensive ramps apparatuses. Thus, TED instruction faired well against a solid comparison lesson on CVS.

Furthermore, within the TED condition, students given the additional questions requiring them to identify all potential causal variables showed higher near-transfer performance on the ramps posttest. Though they did not demonstrate better performance on the immediate Story posttest, they gained significantly more from the Story posttest to the Story follow-up test. These results support the hypothesis that understanding the determinacy or indeterminacy of experimental designs supports both initial learning of and transfer performance for CVS. However, these results should be replicated before drawing firm conclusions.

== References Cited ==

Amsel, E., & Brock, S. (1996). The development of evidence evaluation skills. Cognitive Development, 11, 523-550. 
Bullock, M. & Ziegler, A. (1996). Thinking scientifically: conceptual or procedural problem? Second International Baltic Psychology Conference, Tallinn, Estonia. 
Dean, D., & Kuhn, D. (2007). Direct instruction vs. discovery: The long view. Science Education, 91, 384-397. 
Hsu, T. (2002). Integrated Physics and Chemistry. Peabody, MA: CPO Science.
Kali, Y. & Linn, M. C. (2008). Technology-Enhanced Support Strategies for Inquiry Learning. In J. M. Spector, M. D. Merrill, J. J. G. Van Merriënboer, & M. P. Driscoll (Eds.), Handbook of Research on Educational Communications and Technology (3rd Edition, pp. 145-161). New York: Lawrence Erlbaum Associates. 
Kali, Y., Linn, M. C., & Roseman, J. E. (Eds.). (2008). Designing Coherent Science Education. New York: Teachers College Press. 
Kuhn, D., Garcia-Mila, M., Zohar, A., & Andersen, C. (1995). Strategies of knowledge acquisition. Monographs of the Society for Research in Child Development, 60(4), Serial no. 245. 
Klahr, D. (2000). Exploring Science: The Cognition and Development of Discovery Processes. Cambridge, MA: MIT Press. 
Klahr, D. & Nigam, M. (2004) The equivalence of learning paths in early science instruction: effects of direct instruction and discovery learning. Psychological Science, 15, 661-667. 
Pottenger & Young (1992). The local environment. Curriculum and Development Group: Honolulu. 
Strand-Cary, M. & Klahr, D. (2008). Developing Elementary Science Skills: Instructional Effectiveness and Path Independence. Cognitive Development, 23, 488–511. 
Schauble, L., Klopfer, L., & Raghavan, K. (1991). Students’ transition from an engineering model to a science model of experimentation. Journal of Research in Science Teaching, 18(9), 859-882. 

== Bibliography ==

The current TED project is the outgrowth of several years of empirical and theoretical work in our lab on different instructional approaches, different instructional media (physical vs virtual instructional materials), different theoretical perspectives on transfer, and the impact of different SES levels on the effectiveness of different types of instruction. Below we list some of our publications related to this project, as well as other papers, some extending and replicating our work (Lorch, et al) and some critiquing our approaches and interpretations (Kuhn, and Dean & Kuhn).

Chen, Z. & Klahr, D., (1999) All Other Things being Equal: Children's Acquisition of the Control of Variables Strategy, Child Development , 70 (5), 1098 - 1120. 
Chen, Z. & Klahr, D., (2008) Remote Transfer of Scientific Reasoning and Problem-Solving Strategies in Children. In R. V. Kail (Ed.) Advances in Child Development and Behavior, Vol. 36. (pp. 419 – 470) Amsterdam: Elsevier. 
Dean, D., & Kuhn, D. (2007). Direct instruction vs. discovery: The long view. Science Education, 91, 384 – 397. 
Klahr, D. (2009) “To every thing there is a season, and a time to every purpose under the heavens”: What about Direct Instruction? In S. Tobias and T. M. Duffy (Eds.) Constructivist Theory Applied to Instruction: Success or Failure? Taylor and Francis. 
Klahr, D. & Li, J. (2005) Cognitive Research and Elementary Science Instruction: From the Laboratory, to the Classroom, and Back. Journal of Science Education and Technology, 4, 217-238. 
Klahr, D. & Nigam, M. (2004) The equivalence of learning paths in early science instruction: effects of direct instruction and discovery learning. Psychological Science, 15, 661-667. 
Klahr, D., Triona, L. M., & Williams, C. (2007) Hands On What? The Relative Effectiveness of Physical vs. Virtual Materials in an Engineering Design Project by Middle School Children. Journal of Research in Science Teaching , 44, 183-203. 
Klahr, D., Triona, L., Strand-Cary, M., & Siler, S. (2008) Virtual vs. Physical Materials in Early Science Instruction: Transitioning to an Autonomous Tutor for Experimental Design. In Jörg Zumbach, Neil Schwartz, Tina Seufert and Liesbeth Kester (Eds) Beyond Knowledge: The Legacy of Competence Meaningful Computer-based Learning Environments. SpringerLink. 
Kuhn, D. (2007). Is direct instruction an answer to the right question? Educational Psychologist, 42(2), 109-113. 
Li, J. & Klahr, D. (2006). The Psychology of Scientific Thinking:Implications for Science Teaching and Learning. In J. Rhoton & P. Shane (Eds.) Teaching Science in the 21st Century. National Science Teachers Association and National Science Education Leadership Association: NSTA Press. 
Li, J., Klahr, D. & Siler, S. (2006). What Lies Beneath the Science Achievement Gap? The Challenges of Aligning Science Instruction with Standards and Tests. Science Educator, 15, 1-12. 
Lorch, R.F., Jr., Calderhead, W.J., Dunlap, E.E., Hodell, E.C., Freer, B.D., & Lorch, E.P. (2008). Teaching the control of variables strategy in fourth grade. Presented at the Annual Meeting of the American Educational Research Association, New York, March 23-28. 
Lorch, E.P., Freer, B.D., Hodell, E.C., Dunlap, E.E., Calderhead, W.J., & Lorch, R.F., Jr. (2008). Thinking aloud interferes with application of the control of variables strategy. Presented at the Annual Meeting of the American Educational Research Association, New York, March 23-28. 
Strand-Cary, M. & Klahr, D. (2008). Developing Elementary Science Skills: Instructional Effectiveness and Path Independence. Cognitive Development, 23, 488–511. 
Toth, E. E., Klahr, D., & Chen, Z. (2000) Bridging Research and Practice: a Cognitively-Based Classroom Intervention for Teaching Experimentation Skills to Elementary School Children. Cognition & Instruction, 18 (4), 423-459. 
Triona, L. & Klahr, D. (2008) "Hands-on science: Does it matter what the student's hands are on in 'hands-on science? The Science Education Review. 
Triona, L. M. & Klahr, D. (2003) Point and Click or Grab and Heft: Comparing the influence of physical and virtual instructional materials on elementary school students’ ability to design experiments Cognition & Instruction, 21, 149-173.

Knowledge component

2011-09-09T19:07:14Z

Mbett: Reverted edits by Ularedmond (Talk); changed back to last version by Koedinger

=== Knowledge Component ===

A knowledge component is a description of a mental structure or process that a learner uses, alone or in combination with other knowledge components, to accomplish [[Step|steps]] in a task or a problem. A knowledge component is a generalization of everyday terms like concept, principle, fact, or skill, and cognitive science terms like [[schema]], production rule, misconception, or facet. When we say a student "has" a knowledge component, it might mean the student can describe it in words (e.g., "Vertical angles are congruent") or it might simply mean that the student behaves as described by the knowledge component, but may not be able to describe it themselves. In this second case, to say the student "has" the knowledge component "If angle A and B are vertical angles and angle A is X degrees, then angle B is X degrees" means the student will behave in accord with it even though they might not be able to state the rule. The first is an "explicit" knowledge component, like a fact or principle, and the second an "implicit" knowledge component , like a skill. Much of what first language learners know about their first language involves implicit knowledge components.

A knowledge component (KC) relates [[Features|features]] to a response where both the features and response(s) can be either external, in the world, like cues in a stimulus and a motor response or internal, in the mind, like inferred features and a new goal.

KCs are "correct" when all of the features are relevant to making the response and none of them are irrelevant. In geometry, for example, the knowledge component "if angles look equal, then conclude they are equal" is incorrect because it includes an irrelevant feature "angles look equal" and is missing a relevant feature like "the angles are at the base of an isosceles triangle". See also [[Feature validity|feature validity]] and [[Refinement|refinement]].

An example of a knowledge component analysis (a kind of [[cognitive task analysis]]) can be found in the description of Julie Booth's study [[Booth|knowledge component construction vs. recall]]. In her case, the key knowledge components are concepts and skills for making decisions during problem solving in the domain of algebra equation solving. She identifies both incorrect knowledge components that students tend to acquire and correct knowledge components that good students eventually acquire.

=== Kinds of knowledge components ===
Mental representations of:
* Domain knowledge
** Facts, concepts, principles, rules, procedures, strategies
* Prerequisite knowledge
** Feature encoding knowledge (see examples in [[Booth|Algebra]] and [[Applying optimal scheduling of practice in the Chinese Learnlab|Chinese radicals]])
* Integrative knowledge
** Schemas or procedures that connect other KCs
* Metacognitive knowledge
** About knowledge, controlling use or acquisition of knowledge (see the [[The Help Tutor Roll Aleven McLaren|help-seeking project]])
*Beliefs & interests
** What one likes, believes

=== Cross-cutting distinctions ===
* Correct vs. incorrect
* Verbal (explicit) vs. non-verbal (implicit)
* Probabilistic vs. discrete

=== Not knowledge components ===
* Any external representation of knowledge
** Like textbook descriptions or an example
* Generic cognitive structures
** Working memory
* Continuous parameters on knowledge representations
** Strength, level of engagement, implicit value of a goal, affect

=== Other uses of "knowledge" ===
“Knowledge” in PSLC is used as in the Cognitive Science and AI traditions. The mind is a knowledge base stored in the brain’s hardware. All competencies and behaviors are determined by “knowledge” in this sense. "Knowledge" in philosophy is “justified true belief" whereas our use of knowledge components includes both incorrect (false) knowledge and implicit (no explicit belief or justification) knowledge. "Knowledge" in education is basic facts (1st level of Bloom’s (1956) taxonomy) whereas knowledge components can be procedures, integrating schemas, complex reasoning strategies, metacognitive skills …, that is, all levels of Bloom’s taxonomy.

=== References ===
* VanLehn, K. (2006). The behavior of tutoring systems. ''International Journal of Artificial Intelligence in Education'', 16 (3), 227-265 [http://www.pitt.edu/~vanlehn/Stringent/Abstracts/06IJAIED.htm Abstract&PDF]

* Koedinger's PSLC Lunch Talk from August, 2006.

* Norma Chang's CMU Psychology PhD Thesis (2006) on surface vs. structural problem variations and resultant acquisition of relevant vs. irrelevant features ("spurious correlations" with surface features).

* Bloom, B. S. (Ed.). (1956). Taxonomy of educational objectives. Handbook 1: Cognitive domain. New York: McKay.

[[Category:Glossary]] [[Category:PSLC_General]] [[Category:DataShop_Glossary]]

Knowledge Construction Dialogues

2011-09-09T19:07:02Z

Mbett: Reverted edits by Ularedmond (Talk); changed back to last version by Koedinger

Knowledge construction dialogues automated, Socratic-style dialogues in which the automated tutor presents a series of questions that guide students in answering a more complex qualitative or quantitative question.

Knowledge construction dialogues are an [[instructional method]] that involves the use of [[reflection questions]].

See the studies by Katz.

[[Category:Glossary]]
[[Category:Independent Variables]]
[[Category:Interactive Communication]]

Koedinger - Discovery of Domain-Specific Cognitive Models

2011-09-09T19:06:43Z

Mbett: Reverted edits by Ularedmond (Talk); changed back to last version by Koedinger

Ken Koedinger and John Stamper

== Project Overview ==
This project will address goal 1 of the CMDM thrust and in particular use DataShop datasets (90 in 5 years) to produce better cognitive models and verify the models with in vivo experiments. Cognitive models drive the great many instructional decisions that automated tutoring currently make, whether it is how to organize instructional messages, sequence topics and problems in a curriculum, adapt pacing to student needs, or select appropriate materials and tasks to adapt to student needs. Cognitive models also appear critical to accurate assessment of self-regulated learning skills or motivational states.
Multiple algorithms have been developed for automated discovery of the attributes or factors that make up a cognitive model (or a "Q matrix") including various Q-matrix discovery algorithms like Rule Space, Knowledge Spaces, Learning Factors Analysis (LFA), exponential-family PCA. This project will create an infrastructure for automatically applying such algorithms to data sets in the DataShop, discovering better cognitive models, and evaluating whether such models improve tutors.

== Planned accomplishments for PSLC Year 6 (Oct 09 to Oct 10) ==
1. Develop code and human-computer interfaces for applying, comparing and interpreting cognitive model discovery algorithms across multiple data sets in DataShop. We will document processes for how the algorithms, like LFA, combine automation and human input to discover or improve cognitive models of specific learning domains.

2. Demonstrate the use of the model discovery infrastructure (#1) for at least two discovery algorithms applied to at least 4 DataShop data sets. We will target at least one math (Geometry area and/or Algebra equation solving), one science (Physics kinematics), and one language (English articles) domain.

3. For at least one of this data sets, work with associated researchers to perform a "close the loop" experiment whereby we demonstrate that a better cognitive model leads to better or more efficient student learning.

== Integrated Research Results ==
Establishing that cognitive models of academic domain knowledge in math, science, and language can be discovered from data would be an important scientific achievement. The achievement will be greater to the extent that the discovered models involve deep or integrative knowledge components not directly apparent in surface task structure (e.g., model discovery in the Geometry area domain isolated a problem decomposition skill). The statistical model structure of competing discovery algorithms promises to shed new light on the nature or extent of regularities or laws of learning, like the power or exponential shape of learning curves, whether the complexity of task behavior is due to human or domain characteristics (the ant on the beach question), whether or not there are systematic individual differences in student learning rates.

Interactive Communication

2011-09-08T12:48:49Z

Mbett: Reverted edits by Rosalynbernard (Talk); changed back to last version by Mbett

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:Ic.JPG]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep/Reflection questions]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, VanLehn & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

*[[Adaptive Assistance for Peer Tutoring (Walker, Rummer, Koedinger)]]

*[[McLaren_et_al_-_Conceptual_Learning_in_Chemistry|Supporting Conceptual Learning in Chemistry through Collaboration Scripts and Adaptive, Online Support (McLaren, Rummel, Harrer, Spada, & Pinkwart)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly, 2005)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly, & Treacy, 2006)]]

*[[Extending Reflective Dialogue Support (Katz & Connelly)|Extending Reflective Dialogue Support (Katz & Connelly, 2007)]]

*[[Self-explanation: Meta-cognitive vs. justification prompts|Self-explanation: Meta-cognitive vs. justification prompts (Hausmann, van de Sande, Gershman, & VanLehn, 2008]])

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in the Refinement & Fluency cluster, and relevant to Knowledge Component analysis]

*[[Plateau_study|What is the optimal level of interaction during learning from problem solving? (Hausmann, van de Sande, & VanLehn, 2008)]]

*[[Ringenberg_Ill-Defined_Physics|Eliciting missing information for solving ill-defined physics problems. (Ringenberg & VanLehn, 2008)]]
[[Category:Cluster]]

Integration of reading, writing and typing in learning Chinese words

2011-09-08T12:48:32Z

Mbett: Reverted edits by Rosalynbernard (Talk); changed back to last version by Liuying

--------
Summary table
*Node Title: Integration of reading, writing and typing in learning Chinese words
*Researchers: Ying Liu, Charles Perfetti, Qun (Connie) Guan, Suemei Wu, Min Wang
*PIs: Ying Liu, Charles Perfetti
*Others who have contributed 160 hours or more:
*Post-Docs: Connie Guan
*Graduate Students: Derek Chan
*Study Start Date Sep 1, 2008
*Study End Date July 31, 2009
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)
*Number of Students: 60
*Planned Participant Hours for the study: 200
*Data in the Data Shop: experiments have not started yet
----
== Abstract ==
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical knowledge components that should be learned and integrated (Perfetti, Liu, and Tan, 2005).
*Working together with the CMU Chinese online course, the present project will compare three methods: handwriting, Pinyin based computer typing, and both. Handwriting focuses on the semantic-orthography connections, whereas pinyin typing focuses on the semantic-phonology connection. We hypothesize that the combination of handwriting and pinyin typing can facilitate the integration of constituents. Theoretical framework and practical suggestions will be given on the learning of Chinese handwriting and typing in a modern technology rich learning environment.

== Glossary ==
Integration; Constituents; Orthography; Phonology; Meaning; Typing; Handwriting

== Research question ==
*How does [[integration]] of language constituents lead to [[robust learning]]?
*Does writing Chinese lead to better integration and more robust Chinese reading?
*Does the combination of writing and typing lead to more robust learning via better integration?

== Background ==
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.

== Dependent variables ==

* Visual recognition (lexical decision, partial character recognition ),handwriting, pinyin visual and auditory skills

== Independent variables ==
*Integration of handwriting vs. Pinyin typing vs. both
[[Image:writingvstyping.jpg]]

== Hypothesis ==
*General: Instructional Events that integrate receptive and productive components lead to robust representations of Chinese characters.
*Specific: Lexical constituents are interconnected in skilled performance and that supporting this interconnection during learning leads to more robust learning. Decomposed feature learning aids the acquisition of constituents and partial connections, but robust learning and fluency depend upon constituent integration. We hypothesize that handwriting plus pinyin typing will provide the most robust integration of perception and production in learning Chinese.

== Expected Findings ==
We predict that in the visual identification task, handwriting group will do better than the typing group, whereas typing group will do better in the auditory identification. In the translation task, the group received the combined method will do better than handwriting only group. We predict the above results because visual recognition task depends more on the orthographic information which is more practiced in the handwriting training. Auditory task depends more on the phonological information on the contrary, which is more practiced in the typing training. The translation task depends more on the integrated representation of Chinese words. When trained on both handwriting and typing, both orthographic and phonological routes are made available to the task.

== Explanation ==
The predicted results will be explained under the general framework of interactive constituency model for learners.
*[[Image:model.jpg]]

== Descendants ==
None.

Instructional Principles and Hypotheses

2011-09-08T12:47:13Z

Mbett:

===Generalization Hierarchy of Principles===

* [[Refinement and Fluency]]
** [[Optimized scheduling]]. Optimized scheduling yields better long-term retention than a practice schedule based on fixed intervals (whether massed or spaced) or intervals self-determined by students (e.g., in flash card use).
** [[Feature focusing]]. Instruction leads to more robust learning when it guides the learner's attention ("focuses") toward relevant features of the material, as opposed to unfocused instruction or instruction that guides attention toward irrelevant features.

* [[Sense making]]
** [[Visual-verbal integration]]. Instruction that includes both visual and verbal information leads to more robust learning than instruction that includes verbal information alone, but only when the instruction supports learners as they coordinate information from both sources and the representations guide student attention to deep features.
** [[Example-rule coordination principle]]. Instruction that combines or helps students' combine learning from examples and learning of or from rules tends to be more effective than instruction that includes the same examples and rules but does not help students combine them.
*** [[Worked example principle]]. In contrast to the traditional approach of giving a list homework (or seatwork) problems for students to solve, students learn more efficiently and more robustly when more frequent study of [[worked examples]] is interleaved with problem solving practice.
*** [[Prompted self-explanation principle]]. When students are given a worked example or text to study, prompting them to self-explain each step of the worked example or each line of the text causes higher learning gains than having them study the material without such prompting.
**** [[Corrective self-explanation]]. Explaining how and why incorrect solutions are incorrect will help students to reject incorrect [[knowledge components]] and, thus, stop using incorrect strategies to solve problems.
*** [[Analogical comparison principle]]. Analogical comparison can facilitate schema abstraction and transfer of that knowledge to new problem. By comparing the commonalities between two examples, students can focus on the causal structure and improve their learning about the concept.

* [[Metacognition and Motivation|Motivation]]
** [[Personalization]]. Matching up the features of an instructional event with students' personal interests, experiences, or typical patterns of language use, may lead to more robust learning compared to when instruction is not personalized.

See also [[:Category:Instructional Principle]]. Other possibilities for principles can be found further below and also at other web sites:
* [http://www.edu-design-principles.org Design Principles Database] maintained by the NSF-funded [http://www.telscenter.org/ TELS (Technology Enhanced Learning in Science)] project
* [http://www.psyc.memphis.edu/learning/principles/ Principles of Learning] from Lifelong Learning at Work and at Home
* [http://ies.ed.gov/ncee/wwc/pdf/practiceguides/20072004.pdf Organizing Instruction and Study to Improve Student Learning], one of the Practice Guides of the Department of Education, Institute for Education Sciences
* [http://www.cmu.edu/teaching/principles/ Principles of Teaching and Learning] from CMU's Eberly Center for Teaching and Learning

===Creating Instructional Principle and Hypothesis Pages===
Each instructional principle page is structured with the following headers:

#Brief statement of the principle
#Description of the principle
##Operational definition
##Examples
#Experimental support
##Laboratory experiment support
##In vivo experiment support
##Level of support (either low, medium, or high) (See the IES practice guide on [http://ies.ed.gov/ncee/wwc/pdf/practiceguides/20072004.pdf "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
##Failed replications (which suggest conditions of application are needed)
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it at least (1) has the structure above (even if all sections aren't filled in -- a template you can copy is provided further below) and (2) points to your study with a brief summary of the results. You should also (3) read the entry carefully and fill in or edit sections so they are consistent with your findings and with relevant theory.

We want to keep the number of principles down, at least at the highest level of generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to literature beyond your own work to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers you have written. By doing so, you can help others (and others can help you) identify relevant research in the field.

====Template====
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
===Level of support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

====Editing instructional principle pages====

An [[:Category:Instructional Principle|instructional principle]] is usually so closely related to an independent variable that it is hard to tell them apart. An instructional principle is a general hypothesis, usually about how one [[instructional method]] is better than some other baseline or control method. For example, Mayer's [[multimedia principle]] states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. When a study varies the instructional method, then the instruction method is a kind of [[:Category:Independent Variables|independent variable]], so in this wiki, they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Use your best judgment.

If you do choose to use separate pages for an instructional principle and a related independent variable, please put "principle" or "hypothesis" in the title of the instructional principle. For instance, the [[Worked example principle]] page is different from but related to the [[worked examples]] page. The [[Prompted self-explanation hypothesis]] page is different from the [[Prompted Self-explanation]] page.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

=== Candidate Instructional Principles ===

The following instructional method or [[:Category:Independent Variables|independent variable]] pages are candidates that you might convert to a structured principle page. See directions on structuring a instructional principle or hypothesis page further below.

* APS LifeLong Learning [http://www.psyc.memphis.edu/learning/principles/ Principles of Learning]. Note, though, that as principles of "learning" (not instruction) these may better belong as "learning processes" pages (see below) rather than as principles of instruction.
**[[Prior Knowledge]]
**[[Experience Alone]]
**[[Practice at Retrieval]]
**[[Learning Epistemologies]]
**[[Variable Learning I]]
**[[Variable Learning II]]
**[[Avoid Passive Learning]]
**[[Process of Remembering]]
**[[Less is More]]

* Cross-cutting all 3 clusters (move above when written as principle/hypoth page)
** [[Tutoring feedback]]
*** [[Peer tutoring]]

* [[Coordinative Learning]] (move above when written as principle/hypoth page)
**[[Visual-verbal integration]] - This has been promoted, but a principle page for the descendant, [[Multimedia principle]], has not yet been created.
***[[Multimedia principle]]

* [[Interactive Communication]] (move above when written as principle/hypoth page)
**[[Collaboration]]
***[[Peer tutoring]]
***[[Collaboration scripts]]
***[[Collaboratively observe]]
**[[Vicarious learning]]
**[[Deep/Reflection questions]]. (NOTE: See the "deep questioning" recommendation in [http://ies.ed.gov/ncee/wwc/practiceguides/)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[deep-level question]]s
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
**[[Instructional explanation]]

*[[Refinement and Fluency]] (move above when written as principle/hypoth page)
**[[Feature focusing]] - This has been promoted, but the descendant, explicit instruction, is not expressed as a hypothesis or principle
***[[Explicit instruction]] - Not clear this leads to a separate principle
**[[Fluency Pressure]]
**[[Feedback Timing]] in matrix, but not in glossary.
**[[Error correction support]]
**[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]

* Unclassified
**[[Assistance]]
**[[Availability]]
**[[Fading]]
**[[Implicit instruction]]
**[[Scaffolding]]
**[[Accurate knowledge estimates principle]]

===Learning Processes===

Here's a list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages. (We should also create a common page structure for them, as we have for instructional principles and studies.)

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A list of learning processes can also be found at [[:Category:Learning Processes]]. (This list should be the same.)

Instructional schedule

2011-09-08T12:46:07Z

Mbett: Reverted edits by Rosalynbernard (Talk); changed back to last version by Mbett

=== Instructional schedule ===

The temporal order of [[learning events]].

[[Category:Glossary]]
[[Category:Independent Variables]]
[[Category:Refinement and Fluency]]

*Balota, D. A., Duchek, J. M., Sergent-Marshall, S. D., & Roediger III, H. L. (2006). Does Expanded Retrieval Produce Benefits Over Equal-Interval Spacing? Explorations of Spacing Effects in Healthy Aging and Early Stage Alzheimer's Disease. Psychology and Aging, 21(1), 19-31.
*Briggs, G. E., & Waters, L. K. (1958). Training and transfer as a function of component interaction. Journal of Experimental Psychology, 56(6), 492-500.
*Carlson, R. A., & Shin, J. C. (1996). Practice schedules and subgoal instantiation in cascaded problem solving. Journal of Experimental Psychology: Learning, Memory, & Cognition, 22(1), 157-168.
*Carlson, R. A., & Yaure, R. G. (1990). Practice schedules and the use of component skills in problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(3), 484-496.
*Ciccone, D. S., & Brelsford, J. W. (1976). Spacing repetitions in paired-associate learning: Experimenter versus subject control. Journal of Experimental Psychology: Human Learning & Memory, 2(4), 446-455.
*Goettl, B. P. (1996). The spacing effect in aircraft recognition. Human Factors, 38(1), 34-49.
*Hansen, D. N., & Dick, W. (1969). Memory factors in computer-controlled maintenance training. Navtradevcen Technical Report, 68, 35.
*Heflin, D. T., & Haygood, R. C. (1985). Effects of scheduling on retention of advertising messages. Journal of Advertising, 14(2), 41-47.
*Hintzman, D. L. (1974). Theoretical implications of the spacing effect. In R. L. Solso (Ed.), Theories in cognitive psychology: The Loyola Symposium. Oxford, England: Lawrence Erlbaum.
*Hintzman, D. L., Summers, J. J., & Block, R. A. (1975). What causes the spacing effect? Some effects of repetition, duration, and spacing on memory for pictures. Memory & Cognition, 3(3), 287-294.
*Lundy, D. H. P. S. U. P. A. U. S., Carlson, R. A., & Paquiot, J. (1995). Acquisition of rule-application skills: Practice schedules, rule types, and working memory. American Journal of Psychology 108(4), 471-497.
*Mizuno, R. (1998). Realization of an effective spaced learning schedule based on a reactivation theory of the spacing effect. Japanese Journal of Educational Psychology, 46(2), 173-183.
*Naylor, J. C., & Briggs, G. E. (1963). Effects of task complexity and task organization on the relative efficiency of part and whole training methods. Journal of Experimental Psychology, 65(3), 217-224.
*Pavlik Jr., P. I. (2005). The microeconomics of learning: Optimizing paired-associate memory. Dissertation Abstracts International: Section B: The Sciences and Engineering, 66(10-B), 5704.
*Pavlik Jr., P. I., & Anderson, J. R. (2004). An ACT-R model of memory applied to finding the optimal schedule of practice. In M. Lovett, C. Schunn, C. Lebiere & P. Munro (Eds.), Proceedings of the Sixth International Conference of Cognitive Modeling (pp. 376-377). Pittsburgh, PA: Carnegie Mellon University/University of Pittsburgh.
*Pimsleur, P. (1967). A memory schedule. The Modern Language Journal, 51(2), 73-75.
*Reichardt, C. S., Shaughnessy, J. J., & Zimmerman, J. (1973). On the independence of judged frequencies for items presented in successive lists. Memory & Cognition, Vol. 1(2), 149-156.
*Scott, J. W. (1967). Brain stimulation reinforcement with distributed practice: effects of electrode locus, previous experience, and stimulus intensity. Journal of Comparative and Physiological Psychology, 63(2), 175-183.
*Shaughnessy, J. J. (1976). Persistence of the spacing effect in free recall under varying incidental learning conditions. Memory & Cognition, 4(4), 369-377.
*Shaughnessy, J. J. (1977). Long-term retention and the spacing effect in free-recall and frequency judgments. American Journal of Psychology, 90(4), 587-598.
*Tsao, J. C. (2000). Timing of treatment and return of fear: Effects of massed, uniform, and expanding schedules on public-speaking anxiety. Dissertation Abstracts International: Section B: The Sciences & Engineering, 60(7-B), 3582.
*Underwood, B. J. (1969). Some correlates of item repetition in free-recall learning. Journal of Verbal Learning & Verbal Behavior, 8(1), 83-94.
*Underwood, B. J. (1970). A breakdown of the total-time law in free-recall learning. Journal of Verbal Learning & Verbal Behavior, Vol. 9(5), 573-580.
*Underwood, B. J., Kapelak, S. M., & Malmi, R. A. (1976). The spacing effect: Additions to the theoretical and empirical puzzles. Memory & Cognition, 4(4), 391-400.

Instructional explanation

2011-09-08T12:45:11Z

Mbett: Reverted edits by Oliverjones (Talk); changed back to last version by Bobhaus

An instructional explanation is part of an instructional process wherein an agent, other than the student, provides an explanation for the student to comprehend. Instructional explanations contain the target knowledge components, which is the goal of the instruction.

Studies using instructional explanations manipulate their presence or absence [http://scholar.google.com/scholar?hl=en&lr=&client=firefox-a&cluster=5293252267894252837 (Schworm & Renkl, 2006)], or how completely they are justified [[Hausmann_Study|Hausmann & VanLehn, 2006]].

The following is an example of an instructional explanation [http://andes3.lrdc.pitt.edu/~bob/mat/Example1.html]:
 "We know that there is an electric field. If there is an electric field, and there is a charged particle located in that region, then we can infer that there is an electric force on the particle. The direction of the electric force is in the opposite direction as the electric field because the charge on the particle is negative."

The instructional explanation was generated by the experimenters, and it explains the justification for choosing a particular direction for the electric field. In contrast, an explanation generated by the student while studying an example of electric fields is not an "instructional explanation." Instead, that is a [[Self-explanation|self-explanation]].

[[Category:Glossary]]
[[Category:Interactive Communication]]
[[Category:Independent Variables]]
[[Category: Hausmann_Study]]

Instructional dimensions root

2011-09-08T12:44:54Z

Mbett: Reverted edits by Oliverjones (Talk); changed back to last version by Koedinger

= Instructional dimensions being explored by PSLC projects =

[This has not been vetted by Ken, Chuck or the EC. Consider it personal opinion. -- Kurt]

Existing PSLC experiments vary values along many instructional dimensions, so to simplify the exposition, the dimensions are grouped into 5 major classes and a 6th miscellaneous class. Each class of dimensions is listed below, with its dimensions beneath it. For each dimension, PSLC studies that compare values along that dimension are listed with it.

== Peer collaboration ==
Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more assistance than working alone, and having a partner plus other scaffolding offer even more assistance.

* When solving problems, does instruction on collaboration help? ([[Hausmann_Diss | Hausmann & Chi]]; [[Rummel_Scripted_Collaborative_Problem_Solving | Rummel, Diziol, McLaren, & Spada]])
* When solving problems, should collaborators have a tutor? ([[Walker_A_Peer_Tutoring_Addition | Walker, McLaren, Koedinger, & Rummel]])
* When studying examples, does collaboration help elicit explanations of steps? ([[Hausmann_Study2|Hausmann & VanLehn]]; [[Craig_observing| Craig Gadgil & Chi]])

== Repetition ==
In many kinds of instruction, similar or even identical tasks occur in sequence, with other tasks intervening. The more similar the tasks and the closer they are together, the easier they are for the student to achieve successfully during training, so the higher that scheduling/repetition is in the assistance ordering.
* How should tasks be scheduled--that is, what order and spacing should be used? ([[Optimizing the practice schedule | Pavlik et al.]]; [[French gender cues | Presson & MacWhinney]]; [[Japanese fluency| Yoshimura & MacWhinney]]; [[Providing optimal support for robust learning of syntactic constructions in ESL | Levin, Frishkoff, De Jong & Pavlik]])
* When time pressure increases, should repetitions use identical or similar tasks? ([[Fostering fluency in second language learning | De Jong & Perfetti]])
* When tasks are adjacent in the sequence, how can this be used to expedite learning? ([[Using syntactic priming to increase robust learning | De Jong, Perfetti & DeKeyser]]; [[Providing optimal support for robust learning of syntactic constructions in ESL | Levin, Frishkoff, De Jong & Pavlik]])

== Modality ==
Both the presentations and the responses from learners can be written, spoken, diagramatic, gestural (e.g., menus), etc. Two modalities of presentation may in general be more assistive than one. However, the assistance scale for this design issue needs exploration.
* When practicing vocabulary, how should the stimulus be presented? ([[Mental rotations during vocabulary training |Tokowicz & Degani]]; [[Co-training of Chinese characters|Liu, Perfetti, Dunlap, Zi & Mitchell]]; [[Learning Chinese pronunciation from a “talking head”|Liu, Massaro, Dunlap, Wu, Chen, Chan & Perfetti]])
* When entering or justifying problem solving steps, are visually contiguous modalities better? ([[Contiguous Representations for Robust Learning (Aleven & Butcher)|Aleven & Butcher]])
* When presenting problems, does adding a diagram help? ([[Visual Representations in Science Learning |Davenport, Klahr & Koedinger]])
* Does handwritten input facilitate algebra learning? ([[A_Multimodal_%28Handwriting%29_Interface_for_Solving_Equations| Anthony, Yang, & Koedinger]])

== Explicitness ==
Should the instruction present knowledge explicitly (typically as text) or let the student infer it from multiple instances? Some of these dimensions do not (yet?) have a clear assistance ordering for their values.
* When learning vocabulary words, should students be able to easily consult definitions? ([[REAP_main | Juffs & Eskenazi]])
* When parts of a word have meaning, should that be taught explicitly? ([[Learning the role of radicals in reading Chinese | Dunlap, Liu, Perfetti & Wu ]])
* When giving a hint during problem solving, how explicit should it be? ([[Ringenberg_Examples-as-Help | Ringenberg & VanLehn]]; [[Help_Lite (Aleven, Roll)|Aleven & Roll]]; [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)|Aleven & Butcher]])

== Does the tutor or the student do it? ==
(This dimension needs a better name) Should the tutor or the student do the steps in solving a problem? Should the tutor or the student explain the steps of a problem’s solution? In general, assistance is higher when the tutor does it than when the student does it.
* Adding example-studying to coached problem. ([[Stoichiometry_Study | McLaren, Koedinger & Yaron]]; [[Effect of adding simple worked examples to problem-solving in algebra learning| Anthony, Yang & Koedinger]]; [[Does learning from worked-out examples improve tutored problem solving? | Renkl, Aleven & Salden]])
* During coached problem solving, who detects the errors? ([[Intelligent_Writing_Tutor | Mitamura & Wylie]]; [[The self-correction of speech errors (McCormick, O’Neill & Siskin) | McCormick, O’Neill & Siskin]])
* During coached problem solving, who decides when to ask for a hint? ([[The_Help_Tutor__Roll_Aleven_McLaren | Roll, Aleven & McLaren]]; [[Student_Uncertainty | Forbes-Riley & Litman]])
* When studying examples, who produces or helps produce the explanations of steps? ([[Hausmann_Study| Hausmann & VanLehn]]; [[Craig_questions | Craig & Chi]]; [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation | Nokes & VanLehn]])
* When studying a film, who identifies the culturally key events? ([[FrenchCulture | Ogan, Aleven & Jones]])
* When answering reflection questions on a problem after solving it, who produces or helps produce the answers? ([[Reflective Dialogues (Katz)| Katz 2006]]; [[Post-practice reflection (Katz)| Katz 2005]]).
* When taking notes on a text, who decides or constrains the notes’ content? ([[Note-Taking_Technologies | Bauer & Koedinger]])

== Miscellaneous ==
These instructional dimensions fall outside the categories listed above.
* When a student explains an example, should the to-be-explained steps be always correct, sometimes incorrect, or tutored? ([[Booth |Booth, Siegler, Koedinger & Rittle-Johnson]]; [[Craig_observing|Craig Gadgil & Chi]])
* Can dictation practice improve subsequent learning? ([[Basic skills training| MacWhinney]]; [[Chinese pinyin dictation | Zhang & MacWhinney]])
* How does part-task training transfer to whole-task learning? ([[Composition_Effect__Kao_Roll| Kao, Roll & Koedinger]])
* Does arithmetic over-training transfer to number discimination? ([[Arithmetical fluency project |Fiez]])
* Can an instance-based model of memory explain vocabulary learning effects? ([[A word-experience model of Chinese character learning | Reichle, Perfetti, & Liu]])
* Does dictation practice improve not only dictation but other learning as well? ([[Basic skills training| MacWhinney]])

Instructional events

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=== Instructional Events ===

Studying robust learning means hypothesizing the point at which learning occurs. Unfortunately, there is no way to directly observe these [[learning events]], or the points in time and shifts in knowledge state when students are said to learn.

As researchers, however, we do have the ability to observe '''instructional events''', which are external to the student and involve the presentation of information or elicitation of a behavioral response.

The instructional event, therefore, is used as evidence to infer student learning, or to presume the existence of an unobservable learning event.

=== References ===

[[Category:Glossary]] [[Category:PSLC_General]]

Instructional method

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A component of the instructional strategy defining a particular means for accomplishing the objective. For example a traditional instructor led instructional strategy may be accomplished using the lecture method, a Socratic lecture technique, or a defined step-by-step questioning procedure. Also called “method of instruction” or “instructional [[treatment]]“.

Click on the Independent Variables link below to see examples of different instructional methods that have been varied in experiments on learning.

[[Category:Glossary]]
[[Category:Independent Variables]]
[[Category:PSLC General]]

In vivo experiment

2011-09-08T12:43:44Z

Mbett: Reverted edits by Oliverjones (Talk); changed back to last version by Koedinger

[[Category:Glossary]]
An ''in vivo'' experiment is a principle-testing experiment run in the context of an academic course. It is a laboratory-style multi-condition experiment conducted in the natural setting of student course work including the classroom, computer lab, study hall, dorm room, home, etc. The conditions in an ''in vivo'' experiment manipulate a small but crucial, well-defined instructional variable, as opposed to a whole curriculum or educational policy. How ''in vivo'' experimentation is related to other methodologies in the learning and educational sciences is illustrated in the following figure:

[[Image:In-vivo-method.jpg]]

''In vivo'' experimentation is different from other methodologies in the learning and educational sciences including 1) design-based research, which does not have control conditions, 2) randomized field trials, which do not test a principle, but a policy or curriculum, and 3) lab experiments, which are not run in a natural course setting. Of course, all of these methodologies have strengths and should be applied at the right time for the right purpose.

The motivation for the contrast with laboratory experimentation was well articulated by Russ Whitehurst in an [http://www.psychologicalscience.org/observer/getArticle.cfm?id=1935 APS Observer article (March, 2006)]: "In contrast to learning in laboratory settings, learning in classrooms typically involves content of greater complexity and scope, delivered and tested over much longer periods of time, with much greater variability in delivery, and with far more distraction and competition for student time and effort. Before principles of learning from cognitive science can be applied to classroom instruction, we need to understand if the principles generalize beyond well controlled laboratory settings to the complex cognitive and social conditions of the classroom."

An ''in vivo'' experiment can be implemented either in a [[within classroom design]] (i.e. ''students'' are randomly assigned to conditions, regardless the class they belong to) or in a [[between classroom design]] (i.e. ''classes'' are randomly assigned to conditions).

In typical ''in vivo'' experiments within PSLC, educational technology (like an intelligent tutoring system or an on-line course) is used not only to provide part of the instruction, but also to monitor students' activities and progress through the year. The conditions in an ''in vivo'' experiment may be implemented within an advanced educational technology or outside of it. We strive to have the control condition be exactly what students would do normally in the course. We call this an [[ecological control group]]. Sometimes, however, the control activity is somewhat different from normal activity so as to be more like the treatment (i.e., just on change away). Here are a couple examples (to make this easier to follow, assume that there are just two conditions in the experiment, called the experimental and control conditions):

* '''Technology-based ''in vivo'' experiment with an ecological control''': Here the educational technology is modified to implement the experimental manipulation (by changing just one aspect of the technology). Students assigned to the experimental condition do their work on the modified system. Students assigned to the control condition use the unmodified system, which is what they would normally do (see [[ecological control group]]). [[Post-practice reflection (Katz)]] is an example of this approach.

* '''Activity-based ''in vivo'' experiment without an ecological control:''' For a limited time (e.g., a one-hour classroom period or a two-hour lab period), the control students do one activity and the experimental students do another (which, again, has a single principled difference from the control activity). In the [[Hausmann Study]], for instance, students in both conditions watched videos of problems being solved by their instructor, but in the experimental condition students were [[prompted self-explanation principle|prompted to self-explain]] steps of solutions whereas they were not in the control condition. While this manipulation did ''not'' change the advanced technology regularly used in this course (the Andes Physics tutor), the technology was in use and provided data on student performance. While the control in the [[Hausmann Study]] was much like normal practice in that it was the same content and instructor, it was not a strict [[ecological control group|ecological control]] in that students did not watch instructor videos in normal instruction.

Regardless of the experimental method used in an ''in vivo'' experiment, the educational technologies involved record log data that are used to evaluate the effects of the manipulation.

''In vivo'' experiments are not new, for instance, Aleven & Koedinger (2002). Going back further, there have been many classroom studies that have had important features of ''in vivo'' experiments. For example, below are summaries from two dissertations that were the basis of the famous Bloom (1984) 2-sigma paper. These dissertations involved 6 experiments. These experiments are borderline ''in vivo'' experiments because mastery and, particularly, tutoring are not "small well-defined instructional variables". Instead, these treatments actually varied a number of different instructional methods at one time. Nevertheless, they illustrate a number of other important features of ''in vivo'' experimentation.

Burke’s (1983) experiments measured immediate learning, far [[transfer]] and [[long-term retention]]. This dissertation involved three classroom experiments comparing conventional instruction, mastery learning and 3-on-1 human tutoring. E1 taught 4th graders probability; E2 taught 5th graders probability; E3 taught fifth graders probability at a different site. Instruction was 3-week module. Tutors were undergrad education students trained for a week to ask good questions and give good feedback (pg. 85). The mastery learning students had to achieve 80% to go on; the tutoring students had to achieve 90%. The conventional instruction students got no feedback from the mastery tests. On immediate post-testing, for lower mental process (like a [[normal post-test]], table 5 pg. 98) tutoring effect sizes averaged 1.66 (with 1.53, 1.34 and 2.11 for E1, E2 and E3, respectively) and mastery learning averaged 0.85 (with .73, .78 and 1.04 for E1, E2 and E3). For higher mental process (like far [[transfer]], Table 6 pg. 104), got for tutoring effect sizes averaged 2.11 (with 1.58, 2.65 and 2.11) and mastery learning averaged 1.19 (with 0.90, 1.47 and 1.21). For [[long-term retention]] 3 weeks later; in E3, the effect size for tutoring was 1.71 for lower mental processes vs. 1.01 for mastery learning. For higher mental processes, effect size was 1.99 for tutoring vs. 1.13 for mastery learning. The researchers also measured time on task (tutoring was higher percentage) and affect rates.

The Anania (1981) thesis involved three classroom experiments, each lasting 3 weeks, comparing conventional teaching, mastery learning and tutoring. The tutors were undergrad education students with no training in tutoring. E1 taught probability to 4th graders; E2 taught probability to 5th graders; E3 taught cartography to 8th graders. E1 and E2 used 3-on-1 tutoring; E3 used 1-on-1 tutoring. Measured immediate learning post-test, time on task, and affect measures. For gains (table 3, pg. 72), effect sizes for tutoring were 1.93 (average of 1.77, 2.06 and 1.95 for E1, E2 and E3) and for mastery learning were 1.00 (avg of 0.61, 1.29 and 1.10). The cutoff score for mastery learning as 80% (pg. 77) vs. 90% for tutoring (pg. 81).

==== References ====

* Aleven, V., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26(2).
* Anania, J. (1981). The Effects of Quality of Instruction on the Cognitive and Affective Learning of Students. Unpublished PhD, University of Chicago, Chicago, IL.
* Bloom, B.S. (1984). The 2-sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13, 4-16.
* Burke, A. J. (1983). Student's Potential for Learning Contrasted under Tutorial and Group Approaches to Instruction. Unpublished PhD, University of Chicago, Chicago, IL.
* Koedinger, K.R., Aleven, V., Roll, I., & Baker, R. (2009). ''In vivo'' experiments on whether supporting metacognition in intelligent tutoring systems yields robust learning. In D.J. Hacker, J. Dunlosky, & A.C. Graesser (Eds.), Handbook of Metacognition in Education (pp. 897-964). The Educational Psychology Series. New York: Routledge.
* Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2010). The Knowledge-Learning-Instruction (KLI) framework: Toward bridging the science-practice chasm to enhance robust student learning. CMU-HCII Tech Report 10-102. Accessible at [http://reports-archive.adm.cs.cmu.edu/hcii.html].
* Klahr, D., Perfetti, C. & Koedinger, K. R. (2009). Before Clinical Trials: How theory-guided research can inform educational science. Symposium at the second annual conference of the Society for Research on Educational Effectiveness. See [http://www.sree.org/conferences/2009/pages/program_full.shtml].
* Salden, R. J. C. M. & Aleven, V. (2009). ''In vivo'' experimentation on self-explanations across domains. Symposium at the Thirteenth Biennial Conference of the European Association for Research on Learning and Instruction.
* Salden, R. J. C. M. & Koedinger, K. R. (2009). ''In vivo'' experimentation on worked examples across domains. Symposium at the Thirteenth Biennial Conference of the European Association for Research on Learning and Instruction.
* VanLehn, K. & Koedinger, K. R. (2007). ''In vivo'' experimentation for understanding robust learning: Pros and cons. Symposium at the annual meeting of the American Educational Research Association.

In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input

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Mbett: Reverted edits by Detraransdell (Talk); changed back to last version by Koedinger

''Lisa Anthony, Jie Yang, Kenneth R. Koedinger''

=== Summary Table ===
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| '''PIs''' || Lisa Anthony, Jie Yang, & Ken Koedinger
|-
| '''Other Contributers''' || n/a
|-
| '''Study Start Date''' || April 11, 2007
|-
| '''Study End Date''' || May 25, 2007
|-
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC) and Wilkinsburg High School
|-
| '''LearnLab Course''' || Algebra
|-
| '''Number of Students''' || est. 102
|-
| '''Total Participant Hours''' || est. 300
|-
| '''DataShop''' || To be completed when study ends
|}

=== Abstract ===
This in vivo classroom experiment compared differences in learning that occur depending on the modality of input during algebra equation solving. The key to this study was that the interface used was the normal Cognitive Tutor Algebra equation solver that students normally use in their classroom.

The hypothesis of this study was that, in addition to previously seen ''usability'' advantages of handwriting over typing in terms of speed and user satisfaction, handwriting will also provide ''learning'' advantages. We hypothesize two interrelated factors would be responsible for these advantages: (1) the improved support of handwriting for 2D mathematics notations such as fractions and exponents which can be difficult to represent and manipulate via the keyboard; and (2) the decrease in extraneous and irrelevant cognitive load due to removing the overhead a cumbersome menu-based interface for mathematics can provide.

Results from our preliminary lab study indicate that students achieve similar learning gains but finish in about half the time when they use handwriting vs using typing.

=== Glossary ===
Forthcoming, but will probably include
* Sample worked-out-example:
[[Image:lanthony-example-unit18.gif]]
* Learning rate/efficiency

=== Research question ===
How is robust learning affected by the modality of the generated input of students, specifically comparing handwriting and typing?

=== Background & Significance ===
Prior work has found that handwriting can be faster and more liked by users than using a keyboard and mouse for entering mathematics on the computer [1]. Anecdotal evidence suggests that students take a long time to learn an interface, possibly because it interferes with learning the goal concept. If handwriting can be shown to provide robust learning gains over traditional interfaces for mathematics, it may be possible to improve intelligent tutoring systems for mathematics by incorporating handwriting interfaces; students will be faster, more engaged and more deeply involved in knowledge construction during the learning process.

=== Independent Variables ===
Three factors were varied:
* Modality of input: free-form handwriting space vs keyboard-and-mouse solver interface
* Type of feedback: step-targeted vs answer-targeted
* Type of instruction: pure problem-solving vs problem-solving plus worked examples

The modality is the primary factor. However, due to limitations of handwriting recognition technology and the importance of providing correct feedback to students as they learn, we must also consider varying levels of feedback. Current Cognitive Tutor Algebra provides feedback at every step, but with handwriting input, we cannot have complete confidence that we interpreted the student's input correctly without more information. As a potential mitigating factor, we introduce worked examples to the tutor interface to provide a sort of feed-forward. We therefore have 4 conditions which explore this space and allow us to determine to which factor to attribute any differences between conditions.

===== Conditions =====
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| || '''Modality''' || '''Type of Feedback''' || '''Type of Instruction'''
|-
| '''Condition 1''' || Typing || Step-Targeted || Pure Problem-Solving
|-
| '''Condition 2''' || Typing || Step-Targeted || Problem-Solving + Worked Examples
|-
| '''Condition 3''' || Typing || Answer-Targeted || Problem-Solving + Worked Examples
|-
| '''Condition 4''' || Handwriting || Answer-Targeted || Problem-Solving + Worked Examples
|}

=== Hypothesis ===
The handwriting modality has been shown to be faster than typing for mathematics [1], and this corresponding speed-up in the classroom implies that more detailed study of current topics or further study of more advanced topics is possible than students otherwise would be able to achieve. In addition, students' cognitive overhead during writing should be less than typing, in which they must spend time to think about how to generate the desired input, whereas in handwriting this would come more naturally due to long practice. This decrease in cognitive overhead may result in increased normal learning and long-term retention.

=== Dependent variables ===
* ''[[Normal post-test]], near transfer, immediate'': Students were given a 20-minute post-test after their sessions with the computer tutor had concluded.

* ''[[Long-term retention]], near transfer': 3 weeks after the students complete Unit 18 for the study, they will be given a 20-minute retention test consisting of problems isomorphic to those seen in the session.

* ''Far [[transfer]]'': Far transfer items such as 4-step problems were included on all tests.

* ''[[Accelerated future learning|Acceleration of future learning]]'': We intend to analyze the log data from the students' Cognitive Tutor usage in the equation solving unit that followed the 3-step problems, to determine if there were learning curve differences during training.

Mediating variable:
* ''Cognitive load'': We also used a scale modeled after Paas' [3] cognitive load self-report scale to ask students how much mental effort they spent during the study and whether they felt that this mental effort came more from the material or from the computer.

=== Findings ===
Findings are reported in [[Media:Anthony-thesis2008.pdf | Lisa Anthony's PhD thesis]].

=== Explanation ===
This study is part of both the [[Refinement and Fluency]] and the [[Coordinative Learning]] clusters.

===== Refinement and Fluency =====

This study addresses two of the 9 core assumptions: (1) fluency from basics: for true fluency, higher level skills must be grounded on well-practiced lower level skills; and (2) immediacy of feedback: a corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback, which is a strong point of computerized instruction, facilitates learning.

The fluency from basics element in this study is relevant to the idea that students and teachers use handwritten notations in math class extensively on paper tests and when working on the chalkboard. Learning a new interface is not the goal of a math classroom, but rather learning the concepts and operations is. Thus, extraneous cognitive load of students is increased while learning the interface and learning the math compete for resources.

The immediacy of feedback issue is represented in this study by the type of feedback used: step-targeted vs answer-targeted. Based on limitations of handwriting recognition technology, step-targeted feedback may require serious technical development effort to achieve. Answer-targeted feedback may not be as effective as step-targeted, but this study explores whether the potential drawback of this factor and the potential benefit of the examples factor (below) will balance out.

===== Coordinative Learning =====

This study belongs to the examples and explanations sub-group. This study focuses on presenting worked examples to students right alongside problem-solving, eventually fading them so that students solved problems on their own during tutor use as well.

=== Descendants ===

None.

=== Annotated Bibliography ===

Analysis and write-up in progress.

=== References ===
[1] Anthony, Lisa; Yang, Jie; Koedinger, Kenneth R. (2005) "Evaluation of Multimodal Input for Entering Mathematical Equations on the Computer." ACM Conference on Human Factors in Computing Systems (CHI 2005), Portland, OR, 4 Apr 2005, pp. 1184-1187.

[2] Anthony, Lisa; Yang, Jie; Koedinger, Kenneth R. (2007) "Benefits of Handwritten Input for Students Learning Algebra Equation Solving." To appear in Proceedings of International Conference on Artificial Intelligence in Education (AIEd 2007).

[3] Paas, F. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84, 429-434.

=== Further Information ===
=====Plans for June 2007-December 2007=====

* Analyze data to determine effect of modality as mitigated by potential benefits of worked examples or potential drawbacks of answer-targeted feedback.
* Write up results for publication in a learning science conference.
* Based on results of this study, handwriting recognition enhancements will be performed and a summative evaluation of the prototype Handwriting Algebra Tutor will be conducted in vivo in 2007-2008.

Help:Contents

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Mbett: Reverted edits by Detraransdell (Talk); changed back to last version by Alida

== PSLC Theory Wiki Help ==

=== About this page ===
This page is general help for working with the PSLC wiki. Please feel free to add anything to this you feel would be of use or to modify anything here you think is unclear.

=== Problems/Questions ===
If you have any problems/questions please contact [mailto:mbett@cs.cmu.edu Michael Bett, PSLC Managing Director]

=== Formatting ===
The for basic formating options visit [http://www.mediawiki.org/wiki/Help:Formatting www.mediawiki.org]. A good list of what is possible with examples is [http://meta.wikimedia.org/wiki/Help:Wikitext_examples available here]. For more advanced editing try searching for what you wish to do at [http://meta.wikimedia.org/wiki/ meta.wikimedia.org].

=== Adding/Starting a page ===

You can use the URL for creating a new page.
*<code><nowiki>http://www.learnlab.org/research/wiki/index.php/</nowiki>'''ARTICLE'''</code>

If you replace <code>'''ARTICLE'''</code> with the name of the page you wish to create, you will be taken to a blank page which indicates that no article of that name exists yet. Clicking the "''edit''" at the top of the page will take you to the edit page for that article, where you can create the new page by typing your text, and clicking submit.

=== Making a Link ===

To create an internal link within the wiki put the name of the page inside a double bracket.
* Example: <nowiki>[[Interactive Communication]]</nowiki> becomes [[Interactive Communication]]
If you wish to change the text of the link add a "|" inside the brackets followed by the text.
* Example: <nowiki>[[Interactive Communication | The Interactive Communication Research Cluster]]</nowiki> becomes [[Interactive Communication | The Interactive Communication Research Cluster]]
For an external link use a single bracket with the url a space followed by the text.
* Example: <nowiki>[http://www.cmu.edu/ CMU Website]</nowiki> becomes [http://www.cmu.edu/ CMU Website]

Exact specifications for creating links and more advanced linking tips can be [http://meta.wikimedia.org/wiki/Link#Interwiki_links found here].

=== Categories ===

On the bottom of ''most'' pages and what should be on the bottom of ''all'' pages is <nowiki>[[Category:Some Category]]</nowiki>. While this isn't strictly necessary it does help in the organization of the wiki. Currently there are 5 categories.
* Cluster <nowiki>[[Category:Cluster]]</nowiki>
* Project <nowiki>[[Category:Project]]</nowiki>
* Study <nowiki>[[Category:Study]]</nowiki>
* Glossary <nowiki>[[Category:Glossary]]</nowiki> [[Glossary Instructions| Additional instructions for categorizing glossary items.]]
* Protected <nowiki>[[Category:Protected]]</nowiki> which means that the page is only visible to PSLC members who are logged in.
* DataShop <nowiki>[[Category:DataShop]]</nowiki> which is on all DataShop related pages include feature requests.

These categories can then be linked directly. For example [http://www.learnlab.org/research/wiki/index.php/Category:Study http://www.learnlab.org/research/wiki/index.php/Category:Study] or just
[[:Category:Study|<nowiki>[[:Category:Study|Study]]</nowiki>]]
brings up all the studies in an alphabetical order. It should provide a quick overview of all studies, project, glossary items. Please include the category at the bottom of new pages to help with classification.

Creating a new category is as simple as typing <nowiki>[[Category:</nowiki>'''New Category'''<nowiki>]]</nowiki> (replace '''New Category''' with your actual category) anywhere on your page. This will automatically create the new category.

=== Uploading a File ===

On the left hand side menu '''toolbox''' is the link to [http://www.learnlab.org/research/wiki/index.php/Special:Upload Upload File]. Enter the form information as directed on that page. To then place a link upon your page you will use '''<nowiki>[[Image:File.jpg]]</nowiki>''' where ''File.jpg'' is the name of the file you uploaded if the file was an image, or '''<nowiki>[[Media:File.doc]]</nowiki>''' where ''File.doc'' is the name of the file you uploaded and it is not an image file.

Only certain file types are allowed for uploads. If you have a file type that is restricted and you think should be included please send an email request to PSLC Managing Director Michael Bett (mbett AT cs.cmu.edu) or wiki administrator Benjamin Billings (bkb AT cs.cmu.edu).

Geometry Greatest Hits

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Mbett: Reverted edits by Detraransdell (Talk); changed back to last version by Kirsten-Butcher

== Geometry Greatest Hits ==
=== Summary Table ===
====Study 1====
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden
|-
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)

|-
| '''Study Start Date''' || January, 2009
|-
| '''Study End Date''' || March, 2009
|-
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley
|-
| '''LearnLab Course''' || Geometry
|-
| '''Number of Students''' || 98
|-
| '''Total Participant Hours''' ||
|-
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop
|}
=== Abstract ===
The main idea in the current project is to combine instructional interventions derived from four instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following four principles:

* [[Visual-verbal integration]] principle
* [[Worked example principle]]
* [[Prompted self-explanation principle]]
* [[Accurate knowledge estimates principle]]

Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these four principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.

=== Background & Significance ===
The PSLC’s in-vivo methodology, as well as standard practice in learning science, generally focuses on testing one principle at a time. This approach is useful for understanding which principles work, how they work, and what their boundary conditions are. However, it is also useful to test combinations of principles, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.

Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful.

Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.
Can principle-oriented research make them even more effective?
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?
And will such a combination lead to a large effect size?

=== Glossary ===

=== Hypotheses ===

;H1
: A tutor that uses multiple PSLC learning principles in combination, each of which have been validated to lead to better robust learning when applied, will achieve a significantly higher effect size compared to an unmodified tutor than the principles achieve on their own.

=== Completed experiments===

* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to a modified tutor with all four improvements). Measures of learning gains (including robust learning measures) and learning efficiency (time taken to complete tutor) were utilized.

=== Planned experiments===
* Lab study (2 phases):
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles).
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.

=== Independent variables ===

The Greatest Hits version of the tutor had the following features, which are supported by prior PSLC research

* integrated problem format (symbolic information integrated in the diagram; all interaction happens in the diagram)
[[Image:VisVerb.GIF]]
* non-interactive conceptual example sets at the beginning of each curricular unit
* interactive worked examples at the beginning of each curricular, faded in an individualized manner
[[Image:WorkedExample.GIF]]
* diagrammatic self-explanations of incorrect steps
* tuned knowledge-tracing parameters to achieve more better individualized problem sequences (avoiding over-practice and under-practice)
* employed new knowledge-tracing algorithm that estimated the probability of guesses and slips in a contextual manner (to improve the accuracy of student modeling, which in turn better individualized problem sequences)

The Greatest Hits version of the tutor is compared to a control condition, which features standard tutor interactions and instruction:
[[Image:ControlCondition.GIF]]

====Dependent Variables====
*Problem-Solving Items. 
Problem Solving items have a similar format to the tutor – students must use known information to calculate the measure of an angle and should justify their problem solving step with a relevant geometry rule. These problem solving items also contain several types of new tasks: First, student must make a solvability judgment to determine if enough information is known to solve the step. Second, for “false answers, students must explain how the unsolvable problem could be makes solvable. Third, for solvable items, students must explain which diagram elements apply to the geometry rule that was used in the problem solving step. 
[[Image:ProblemSolvingItem.GIF]]
 
*Reasoning Items. 
Students also complete reasoning items, which assess how well they understand the conceptual geometry relationships by which one feature is used to solve others. For these items, students should indicate whether they can find the angles of a certain geometry rule. 
[[Image:ReasoningItem.GIF]]

=== Results ===
Not yet available.

=== Explanation ===
=== Further Information ===
==== Connections ====

==== Annotated Bibliography ====
==== References ====

Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995).
Cognitive tutors: Lessons learned. The Journal of the Learning
Sciences, 4 (2) 167-207.

Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., Kyza, E., Edelson,
D. C., & Soloway, E. (2004). A scaffolding design framework for software to support
science inquiry. The Journal of the Learning Sciences, 13(3), 337-386.

==== Future Plans ====

Geometry

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===Geometry LearnLab course description===
The Geometry LearnLab course is described [http://learnlab.org/learnlabs/geometry/ here].

===Algebra & Geometry LearnLab Course Committee===
The Math Course Committee meets monthly and is led by Albert Corbett (corbett@andrew.cmu.edu).

Teachers (and perhaps researchers) can find answers to questions on the [[FAQ for teachers]].

===Geometry Learnlab Studies===

*[[Contiguous Representations for Robust Learning (Aleven & Butcher)]]
*[[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)]]
**[[Training Geometry Concepts with Visual and Verbal Sources (Burchfield, Aleven, & Butcher)]]
*[[Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)]]
*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]
*[[Help_Lite (Aleven, Roll)|Hints during tutored problem solving – the effect of fewer hint levels with greater conceptual content (Aleven & Roll)]]
*[[Does learning from worked-out examples improve tutored problem solving? | Does learning from worked-out examples improve tutored problem solving? (Renkl, Aleven & Salden)]]
* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]]
* [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]
* [[Using learning curves to optimize problem assignment]] (Cen & Koedinger)
* [[Geometry Greatest Hits]] (Aleven, Baker, Butcher, & Salden)

These studies are also organized within research clusters that address common issues across a variety of academic content domains: [[Coordinative Learning]], [[Interactive Communication]], and [[Refinement and Fluency]].

Gaming the system

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Baker et al (2006) defines gaming the system as "Attempting to succeed in an interactive learning environment by exploiting properties of the system rather than by learning the material".

Within intelligent tutoring systems such as Cognitive Tutors, this is usually done by systematic guessing, where a student systematically tries a set of possible answers (example: 1, 2, 3, 4, 5, ... 38) or hint abuse, drilling through hints at high speed to obtain the answer (Aleven & Koedinger, 2000).

Gaming has been observed in other types of learning environments as well, including educational games (Miller, Lehman, & Koedinger, 1999; Magnussen & Misfeldt, 2004; Rodrigo et al, 2007), simulation environments (Rodrigo et al, 2007), and graded-participation newsgroups (Cheng & Vassileva, 2005).

It has been repeatedly shown that students who game the system have poorer learning than non-gaming students with comparable pre-test scores (Baker et al, 2004, 2006; Walonoski & Heffernan, 2006a). (One exception is when students drill through hints, and then self-explain them -- Shih et al, 2008; another exception is when students game time-consuming material they already know -- Baker, Corbett & Koedinger, 2004).

Gaming the System has been shown to be associated with the affective experiences of boredom and confusion (Rodrigo et al, 2007) -- in specific, a student who experiences either of these two affective states is significantly more likely to be gaming the system shortly afterwards. Frustration, though previously found to be associated with gaming (Baker et al, 2008), appears to co-occur with gaming behavior rather than preceding it.

A variety of stable or semi-stable student characteristics have been studied in relation to gaming the system (e.g. Arroyo & Woolf, 2005; Baker et al, 2008; Beal, Qu, & Lee, 2009); however, these characteristics have generally been found to have weak correlations with gaming, at best. Some characteristics found to be significantly associated with gaming include negative attitudes towards computers, the learning software, and mathematics. Performance goals and anxiety have been repeatedly found to have no correlation to gaming (Baker et al, 2008).

Recent results from PSLC project [[Baker_Choices_in_LE_Space | How Content and Interface Features Influence Student Choices Within the Learning Space]] indicate that differences between tutor lessons explain much more of the variance in how much students choose to game, than individual differences between students. This finding was obtained through an ANOVA conducted at each of these two levels, and was replicated in both the middle school Cognitive Tutor (precursor to Bridge to Algebra) (Baker, 2007), and the Algebra Cognitive Tutor (paper in preparation).

Further data mining analysis (paper in preparation) using the [[CTLVS | Cognitive Tutor Lesson Variation Space (CTLVS)]] showed that students game the system more on lessons which have features which are likely to increase student confusion (including hints which do not lead any students to better performance, reference to abstract principles in hints, whether the toolbar is unclear, and the same number being used for multiple constructs) and boredom (including time-consuming problem steps and the lack of interest-increasing text in problem statements). These results conform well to the previous evidence on which affective states are associated with gaming.

[[Scooter the Tutor]] is a software agent who responds to gaming the system with emotional expressions and supplementary exercises (Baker et al, 2006). Scooter was associated with significantly reduced gaming, and significantly improved learning for gaming students (specifically those who received supplementary exercises). Scooter was built on top of the gaming detector, software validated to automatically detect gaming in running Cognitive Tutors (Baker et al, 2008).

=== PSLC Studies Involving Gaming the System ===

*[[Baker_Choices_in_LE_Space | How Content and Interface Features Influence Student Choices Within the Learning Space (Baker, Corbett, Koedinger, & Rodrigo)]]
* [[Baker - Closing the Loop]]
* [[Baker - Building Generalizable Fine-grained Detectors]]

=== See Also ===
Ryan Baker's [http://www.joazeirodebaker.net/ryan/gaming.html webpage on gaming the system].

=== Bibliography ===
* Aleven, V., Koedinger, K.R. (2000)Limitations of Student Control: Do Students Know When They Need Help? Proceedings of the 5th International Conference on Intelligent Tutoring Systems, 292-303.
* Baker, R.S.J.d. (2007) Is Gaming the System State-or-Trait? Educational Data Mining Through the Multi-Contextual Application of a Validated Behavioral Model. Complete On-Line Proceedings of the Workshop on Data Mining for User Modeling at the 11th International Conference on User Modeling 2007, 76-80. [http://www.cs.cmu.edu/~rsbaker/B2007B.pdf pdf]
* Baker, R.S., Corbett, A.T., Koedinger, K.R. (2004) Detecting Student Misuse of Intelligent Tutoring Systems. Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 531-540. [http://www.cs.cmu.edu/~rsbaker/BCK2004MLFinal.pdf pdf]
* Baker, R.S.J.d., Corbett, A.T., Roll, I., Koedinger, K.R. (2008) Developing a Generalizable Detector of When Students Game the System User Modeling and User-Adapted Interaction, 18, 3, 287-314. [http://www.joazeirodebaker.net/ryan/USER475.pdf pdf]
* Baker, R.S.J.d., Corbett, A.T., Koedinger, K.R., Evenson, S. E., Roll, I., Wagner, A.Z., Naim, M., Raspat, J., Baker, D.J., Beck, J. (2006). Adapting to When Students Game an Intelligent Tutoring System. 8th International Conference on Intelligent Tutoring Systems, 392-401. [http://www.joazeirodebaker.net/ryan/Baker175.pdf pdf]
* Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004). Off-Task Behavior in the Cognitive Tutor Classroom: When Students “Game the System”. ACM CHI 2004: Computer-Human Interaction, 383-390. [http://www.joazeirodebaker.net/ryan/p383-baker-rev.pdf pdf]
* Baker, R.S.J.d., Walonoski, J.A., Heffernan, N.T., Roll, I., Corbett, A.T., Koedinger, K.R. (2008) Why Students Engage in "Gaming the System" Behavior in Interactive Learning Environments. Journal of Interactive Learning Research, 19 (2), 185-224. [http://www.joazeirodebaker.net/ryan/BWHRKC-JILR-draft.pdf pdf]
* Beal, C. R., Qu, L., & Lee, H. (2009). Mathematics motivation and achievement as predictors of high school students' guessing and help-seeking with instructional software. Journal of Computer Assisted Learning.
* Cheng, R., Vassileva, J. (2005) Adaptive Reward Mechanism for Sustainable Online Learning Community. Proceedings of the 12th International Conference on Artificial Intelligence in Education, 152-159.
* Magnussen, R., Misfeldt, M. (2004) Player Transformation of Educational Multiplayer Games. Proceedings of Other Players. Available at [http://www.itu.dk/op/proceedings.htm http://www.itu.dk/op/proceedings.htm]
* Miller, C.S., Lehman, J.F., Koedinger, K.R. (1999) Goals and learning in microworlds - An exploration. Cognitive Science, 23 (3), 305-336.
* Murray, R.C., vanLehn, K. (2005) Effects of Dissuading Unnecessary Help Requests While Providing Proactive Help. Proceedings of the 12th International Conference on Artificial Intelligence in Education, 887-889.
* Rodrigo, M.M.T., Baker, R.S.J.d., Lagud, M.C.V., Lim, S.A.L., Macapanpan, A.F., Pascua, S.A.M.S., Santillano, J.Q., Sevilla, L.R.S., Sugay, J.O., Tep, S., Viehland, N.J.B. (2007) Affect and Usage Choices in Simulation Problem Solving Environments. Proceedings of Artificial Intelligence in Education 2007, 145-152.
[http://www.joazeirodebaker.net/ryan/RodrigoBakeretal2006Final.pdf pdf]
* Shih, B., Koedinger, K., and Scheines, R. (2008) A Response Time Model for Bottom-Out Hints as Worked Examples. Proceedings of the 1st International Conference on Educational Data Mining, 117-126. [http://www.educationaldatamining.org/EDM2008/uploads/proc/12_Shih_35.pdf pdf]
* Walonoski, J.A., Heffernan, N.T. (2006a) Detection and Analysis of Off-Task Gaming Behavior in Intelligent Tutoring Systems. Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 382-391.
* Walonoski, J.A., Heffernan, N.T. (2006b) Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems. Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 722-724.

[[Category:Glossary]]
[[Category:Interactive Communication]]
[[Category:Help Tutor]]

French

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The French LearnLab course is described [http://learnlab.org/learnlabs/french/ here].

Numerous studies in the French LearnLab course can be found in the [[Refinement and Fluency]] research cluster.

Fostering fluency in second language learning

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{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! Project title
| Fostering fluency in second language learning: Testing two types of instruction
|-
! Principal Investigator
| Dr. N. de Jong (faculty, Vrije Universiteit Amsterdam)
|-
! Co-PIs
| Dr. L.K. Halderman (post-doc, University of Pittsburgh)
|-
|
| Dr. C.A. Perfetti (faculty, University of Pittsburgh)
|-
! Others with > 160 hours
| Claire Siskin, Jessica Hogan, John laPlante, Mary Lou Vercellotti
|-
! Study start and end dates
| Study 1: September - November 2006
|-
|
| Study 2: January - March 2007
|-
|
| Study 3: January - March 2007
|-
|
| Study 4: January - March 2008
|-
|
| Study 5: September - November 2008
|-
|
| Study 6: October - December 2009
|-
|
| Study 7: Februari - March 2010
|-
|
| Study 8: October - December 2010
|-
! Learnlab
| [[ESL]], Speaking courses (levels 3, 4, 5)
|-
! Number of participants
| 350
|-
! Total Participant Hours
| 825 hours
|-
! Datashop
| Audio files and transcripts of Studies 1 through 4 are available
|-
! Current status
| (July 2011) Paper about study 1 is published; paper about study 4 is under review; papers about studies 2 and 6 are in preparation. Many results have been presented at conferences.
Transcription and coding of studies 7 and 8 is in progress. Analysis of studies 5 and 6 is in progress.
|}

== Abstract==
[[Image:Vercellotti-DeJong GURT09 English L2 Verb Complements.gif|thumb|Verb Complement Errors (GURT09)]] [[Image:Fluency_DeJong-et-al_AB-visit_Sp09.gif|thumb|Speech Repetition and Fluency Development (Advisory Board Spring 2009)]] [[Image:Fluency_DeJong-et-al_iSLC_Sp09.gif|thumb|Elicited Imitation (iSLC Spring 2009)]] [[Image:Fluency_DeJong-et-al_IA_Sp09.gif|thumb|4/3/2 procedure (Industrial Affiliates Spring 2009)]]

Many studies have investigated the effect of exposure to language on [[fluency]]. It has been established, for instance, that [[fluency]] increases after a period of immersion or study abroad (Freed et al., 2004; Segalowitz & Freed, 2004). However, few types of instruction have been designed to increase oral [[fluency]], and even fewer have been tested.

One such type of instruction is Nation’s 4/3/2 procedure, in which learners prepare a four-minute talk and repeat it twice to different partners, first in three minutes, then in two minutes (Nation, 1989). He found that the number of hesitations decreased in the retellings, and that sentences were more complex. We may characterize such an outcome as resulting from the [[fluency pressure]] exerted by the 4/3/2 procedure. It was not investigated, however, whether the effect transferred to new speeches, which is what we showed in Study 1. Another task that may increase [[fluency]] is shadowing, in which student talk along with a recording of a short speech by a native speaker. Shadowing may also increase the feature [[strength]] of formulaic sequences, resulting in faster access to them in subsequent production tasks.

This project is transformative in the sense that it moves research on fluency away from single- or multiple-case studies, using technology to collect and analyze larger amounts of oral production data (30 to 40 students per study), and to generate multiple measures of fluency, accuracy, and complexity. For example, not only articulation rate is measured, but also pause length, length of fluent run, and phonation/time ratio.

Study 1 investigated what characteristics of [[fluency]] are affected by the 4/3/2 procedure. Measures included the number of syllables per second (speech rate); mean length of fluent runs between pauses; phonation/time ratio; number of interphrasal and intraphrasal pauses; morphosyntactic accuracy; and number of embedded clauses (syntactic complexity). The posttest tested transfer to a different topic.

In Study 2 we investigated whether [[fluency]] is further enhanced by a pretraining of formulaic sequences, like ''the point is that'', ''what I’m saying is that'', and ''and so on''). Fast and effortless access to these sequences frees up [[cognitive headroom]] which can then be used to construct sentences. This results in fewer and shorter pauses, and/or greater lexical and structural complexity.

Study 3 investigated whether shadowing leads to increased use of formulaic sequences ([[chunking]]) and native-like pauses in subsequent production tasks.

In studies 4 and 5 are investigating in further detail how the characteristics of the 4/3/2 task lead to fluency development. In Study 4a, we investigated how time pressure affects the benefits of repetition in terms of fluency, accuracy and complexity. We examined recordings both from the 4/3/2 task itself, and from long-term retention tests. In addition, we investigated the role of specific knowledge components in fluency development.

In Study 4b we tracked how the control and retrieval of specific vocabulary items and morphosyntactic structures develop as a result of the 4/3/2 training. Next, in Study 5, we examined whether priming these same items leads to greater accuracy and fluency during training and later. Data collection for Studies 4a, 4b and 5 took place in the Spring and Fall 2008 semesters, in the English as a Second Language (ESL) learnlab (level 4, higher intermediate). Data analysis is currently in progress.

(Posters presented in Spring 2009 are shown on the right. Click on the thumb images to see the larger images.)

== Glossary ==

; 4/3/2 procedure: A teaching method in which students talk about a topic for four minutes. Then they repeat their speech in three minutes, and again in two minutes.
; Shadowing: Repeating speech while it is being spoken.
; Formulaic sequence: A sequence, continuous or discontinuous, of words or other elements, which is, or appears to be, prefabricated (see Wray, 2002, p. 9), e.g., ''The point is that'', ''What I'm trying to say is that'', and ''Take something like''.
; Articulation rate: Number of syllables per second
; Phonation/time ratio: The percentage of time spent speaking as a percentage proportion to the time taken to produce the speech sample
; Morphosyntactic accuracy: In this study we will investigate subject-verb agreement, tense errors, definite/indefinite articles
; Syntactic complexity: In this study we will investigate the number of embedded finite and non-finite clauses

== Research questions ==

=== Study 1 ===
* a. What characteristics of [[fluency]] are affected by repetition of a short speech under increasing time pressure (the 4/3/2 procedure)?
* b. Does knowledge [[refinement]] take place during the 4/3/2 training, in terms of morphosyntactic accuracy and syntactic complexity?
=== Study 2 ===
* a. Does pretraining of formulaic sequences lead to an increase in their use in the subsequent 4/3/2 procedure and posttest? If so, does this increase overall [[fluency]]?
* b. Does proficiency level affect [[fluency]] development during the 4/3/2 procedure?
=== Study 3 ===
* a. What characteristics of [[fluency]] are affected by shadowing a text with formulaic sequences and a pausing pattern characteristic of spontaneous speech?
* b. Does shadowing texts with formulaic sequences lead to an increase in their use in the posttest? If so, does this increase overall [[fluency]]?

For studies 2 and 3, questionnaire data were collected about the students' contact with the second language (English) and their first language, in terms of ''types of contact'' (e.g., listening to the radio, talking to friends, talking to strangers) and ''amount of contact'' (number of days per week, number of hours per day). We will explore whether these [[individual differences]] affect pretest performance and fluency development.

=== Study 4 ===
* a. How do time pressure and repetition affect fluency, accuracy and complexity in the 4/3/2 task?
* b. Which knowledge components contribute to fluency development in the 4/3/2 task?
=== Study 5 ===
* Does priming lead to an immediate and a long-term increase in fluency, accuracy and complexity?
=== Study 6 - pilot for studies 7 and 8 ===
* What prompts will be most appropriate for studies 7 and 8 (to test the effect of time pressure on fluency, accuracy, and complexity)?
=== Study 7 ===
* What is the effect of increasing time pressure on fluency, accuracy, and complexity in immediately repeated speeches?
=== Study 8 ===
* What is the long-term effect of increasing time pressure on fluency, accuracy, and complexity?

== Background and significance ==

Many studies in the field of second language acquisition that have studied [[fluency]] have investigated the effect of study abroad, immersion and regular classroom practice on [[fluency]] (Freed, Segalowitz, and Dewey, 2004; Segalowitz & Freed, 2004). Very few studies, however, have investigated specific activities that lead to [[fluency]], which can be done in classrooms. Two such activities are tested in this project.

The first activity that is tested is the 4/3/2 procedure as proposed by Nation (1989). He investigated the development of [[fluency]] during this task, but used a limited number of measures and did not test the long-term effect: he only analyzed [[fluency]] during the task itself, not during the following weeks. This project will test the long-term effect and will include more measures, such as length and location of pauses. An attempt will be made to link these measures to cognitive mechanisms.

A general effect of the 4/3/2 task on fluency development was found in Study 1. The effect was investigated in more detail in Study 4a, focusing on the two main characteristics of the task: repetition and increasing time pressure.

The contribution of knowledge components is tested in Studies 2, 4b and 5. In Study 2, students received a pretraining of a set of formulaic sequences before the first 4/3/2 fluency training session. In Study 5 students received a pretraining at the start of each 4/3/2 fluency training session. The knowledge components in this pretraining was selected based on the results of Study 4b, which investigated the role of vocabulary breadth, vocabulary depth and grammatical knowledge in oral fluency.

Study 3 investigated whether shadowing affected fluency development. In addition, it was tested whether the presence of formulaic sequences in the model speeches increased use of those sequences in later speaking tasks, and whether such an increase affected [[fluency]] measures.

== Dependent variables ==
=== Fluency, accuracy, and complexity ===
* Temporal measures of [[fluency]]:
** Articulation rate: number of syllables per second
** Pauses:
***mean length of fluent runs between pauses
***mean length of pauses
***phonation/time ratio
***number of interphrasal and intraphrasal pauses
** Formulaic sequences: number of appropriate formulaic sequences repeated from training
* Accuracy: morphosyntactic accuracy (target-like use of several structures, including subject-verb agreement, tense errors, and definite/indefinite articles; see Mizera, 2006: 71)
* Complexity: number of embedded finite and non-finite clauses (cf. Nation, 1989); lexical variety as measured by the Mean Segmental Type-Token Ratio (Towell, Hawkins & Bazergui, 1996)

=== Transfer ===
* ''Near transfer, immediate and delayed, [[normal post-test]]'': After completing the last training session, students performed a similar task (spontaneous speech about a given topic), to test whether any gains in [[fluency]] during the training task were maintained in a new instance of the same task. This test was given one week and four weeks after the last training session, each time with a different topic. These recordings were made as part of the Recorded Speaking Activities (RSAs) from the project "[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]".
* ''Far transfer, delayed'': The delayed posttests in Study 4 will include measures of vocabulary breadth, vocabulary depth and grammatical knowledge.

== Independent variables ==
(For screenshots exemplifying some of the independent variables, see Screenshots below.)
* Studies 1-3: Pretest vs. immediate posttest vs. [[long-term retention]] posttest
* Study 1: Repetition vs. No Repetition
:: In the Repetition condition students talk about one topic three times. In the No Repetition condition, students talk about three different topics.
* Study 2:
** a. Pretraining vs. no pretraining of formulaic sequences
:: In the Formulaic Sequences condition, students receive a short training of a number of formulaic sequences before they start the [[fluency]] training (4/3/2 task). In the No Formulaic Sequences condition, students do not receive this pretraining, and only do the 4/3/2 task.
:* b. Low intermediate vs. high intermediate proficiency level
:: Low intermediate students are enrolled in ELI Speaking courses at level 3, high intermediate at level 4.
* Study 3: Shadowing text with formulaic sequences vs. without formulaic sequences
:: In the Formulaic Sequences condition, students shadow texts that contain formulaic sequences. In the No Formulaic Sequences condition, students shadow the same texts, from which the formulaic sequences that are being studied have been removed.
* Study 4:
** a. Repetition vs. No Repetition
:: The 4/3/2 task will include either 1 topic (repeated) or 3 topics (not repeated)
** b. Increasing Time Pressure vs. No Increasing Time Pressure
:: The 4/3/2 task will include recordings of either 4, 3 and 2 minutes, or 3, 3, and 3 minutes
* Study 5: Pretraining vs. no pretraining of vocabulary and grammar knowledge components
:: In the Pretraining condition, students perform short tasks before the 4/3/2 training sessions to prime their vocabulary and grammar knowledge. Students in the No Pretraining condition do not receive this pretraining.

== Hypotheses ==
=== Study 1 ===
* It is hypothesized that repetition of a short speech (independent variable) under increasing time pressure ([[fluency pressure]]) increases articulation rate and sentence complexity (dependent variables), and decreases the number and length of pauses (dependent variables). The reason is that repetition will--temporarily--increase the [[availability]] of vocabulary and sentence structures (leading to increase speech rate, short and fewer pauses), leaving more [[cognitive headroom]] for other processes (higher accuracy and syntactic complexity).

=== Study 2 ===
* It is hypothesized that the presence of a pretraining of formulaic sequences (independent variable) leads to an increase in their use in subsequent spontaneous speech (dependent variable). Effortless use of these sequences will free up [[cognitive headroom]] for sentence structure planning, which may lead to overall more fluent performance, in terms of speed and pausing patterns (dependent variables). Thus, the training of formulaic sequences may accelerate [[accelerated future learning|future learning]].
* Students at different proficiency levels may benefit in different ways from the 4/3/2 training. At lower proficiency levels, repetition may facilitate the use of particular words and grammar, leading to more instances of correct usage of vocabulary, morphosyntax and syntax. At higher proficiency levels, on the other hand, repetition may lead to a greater number of reformulations resulting in higher complexity.

=== Study 3 ===
* It is hypothesized that shadowing a speech that contains formulaic sequences (independent variable) leads to an increase in their use in subsequent spontaneous speech (dependent variable). Since effortless use of these sequences will free up [[cognitive headroom|headroom]] for sentence structure planning, performance may become more fluent overall, in terms of speed and pausing patterns (dependent variables). Thus, shadowing may accelerate [[accelerated future learning|future learning]]. In addition, shadowing a text with target-language pausing patterns is expected to lead to a more native-like pausing pattern in subsequent spontaneous speech, mainly in terms of position (dependent variables: interphrasal and intraphrasal pauses).

=== Study 4 ===
* Study 4 a: We hypothesize that time pressure in combination with speech repetition encourages a strategy of retrieval (shorter pauses, high lexical overlap between two subsequent recordings), while repetition without time pressure encourages computation, leading to higher accuracy and complexity with lower fluency. We expect that the strategy of computation may result in higher fluency in the longer term, because it leads to more [[refinement]] and [[strength|strengthening]] of knowledge components, thus reducing hesitations and [[automaticity|accelerating retrieval]] in future speeches (cf. the [[assistance|assistance dilemma]]).
* Study 4b: We hypothesize that students with a broader vocabulary—-since they have more words to choose from—-will be able to find an appropriate word more often and more quickly, and therefore speak more fluently (shorter and fewer pauses, fewer hesitations). This is a measure of individual differences, and of general vocabulary knowledge. Greater vocabulary depth will also increase fluency, because students have more control over vocabulary items. In addition, it is predicted that retrieval speed for words used in the fluency training will show a increase in retrieval speed compared to items that were used in the fluency training. Finally, we expect to find a positive correlation between fluency in speech production and the accuracy scores on a test of morphosyntactic knowledge.

=== Study 5 ===
* We hypothesize that the pretraining will lead to more fluent speech production, as well as more accurate and more complex output. We expect that this effect will be retained on the posttest and delayed posttest. This would be an example of [[accelerated future learning]] through [[feature focusing]].

=== Study 6 ===
* This is an exploratory pilot study to test instructions and prompts for a new study, which will focus on the effect of time pressure on fluency and fluency developement (cf. Study 4a). The new speaking prompts will be picture stories, in order to create "pushed output" and to increase comparability of performance across students.

=== Study 7 ===
This is a pilot study for Study 8, to examine the effect of time pressure on repeated and non-repeated story retellings. If a difference between the two groups is found, Study 8 will investigate the longer-term effects of time pressure.
* We hypothesize that time pressure in combination with speech repetition encourages a strategy of retrieval (shorter pauses, high lexical overlap between two subsequent recordings), while repetition without time pressure encourages computation, leading to higher accuracy and complexity with lower fluency. We expect that the strategy of computation may result in lower fluency in the short term (but higher fluency in the longer-term; see Study 8).

=== Study 8 ===
This is a partial replication of Study 4, with tighter control over variables such as the content of the speeches, and with larger group sizes.
* We hypothesize that time pressure in combination with speech repetition encourages a strategy of retrieval (shorter pauses, high lexical overlap between two subsequent recordings), while repetition without time pressure encourages computation, leading to higher accuracy and complexity with lower fluency. We expect that the strategy of computation may result in higher fluency in the longer term, because it leads to more [[refinement]] and [[strength|strengthening]] of knowledge components, thus reducing hesitations and [[automaticity|accelerating retrieval]] in future speeches (cf. the [[assistance|assistance dilemma]]).

=== Robust learning ===
* ''Near transfer, immediate'': In all studies, a posttest is administered about a week after the last training session. This will be a similar task—a 2-minute monologue—with new content—a new topic.
* ''Near transfer, retention'': In Studies 1 and 2, another posttest is administered two to three weeks after the immediate posttest (three to four weeks after the last training session). Again, this will be a similar task—a 2-minute monologue—with new content—a new topic.
* ''[[accelerated future learning|Acceleration of future learning]]'': In Study 5, the students in the experimental condition first receive a pretraining of a number of formulaic sequences. It will be tested whether their [[fluency]], accuracy and syntactic complexity increases more during subsequent training, than of students who do not receive this pre-training.

== Findings ==

=== Study 1: Repetition, proceduralization and L2 fluency ===
[[Image:FluencyStudy1_SPR-Session1.JPG|thumb]] [[Image:FluencyStudy1_SPR-Session2.JPG|thumb]] [[Image:FluencyStudy1_SPR-Session3.JPG|thumb]]

''Fluency during the 4/3/2 task''

During each training session, the mean length of fluent runs increased mostly for the two conditions in which speeches were repeated (Repetition and Control/Repetition). In addition, for all three groups, pauses, on average, became shorter, and phonation/time ratio increased (i.e., students were able to fill more time with speech).

Figures 1, 2 and 3 show the mean length of fluent runs for each recording in each of the sessions. It is clear that in sessions 1 and 2 the two conditions in which speeches were repeated pattern more closely together than the condition in which speeches were not repeated. The improvement in performance of the two repetition conditions seems more stable, whereas the performance of the No Repetition condition seems to be influenced by the topic of a particular speech.

''Transfer to new topics''

On the immediate posttest, students in the Repetition condition are able to produce the same length of fluent runs with shorter pauses. Also, they fill relatively more time with speech (increased phonation/time ratio). It seems, therefore, that they speak more fluently than students in the No Repetition condition. However, on the delayed posttest, the No Repetition condition seems to have caught up with the Repetition condition, also having shorter pause lengths, with stable lengths of fluent runs.

Both groups reach a higher articulation rate, measured in syllables per minutes, on the delayed posttest. This may have been due to their continued Speaking classes in the English Language Institute, and may not have been related to this study.

It should be noted that the posttests were administered one and four weeks after the last session of the [[fluency]] training, and involved a new topic, which the students had not talked about during the 4/3/2 training.

{|+ Preliminary results Study 1
!   !! align="center" colspan="3" | No Repetition (n=9) !!   !! colspan="3" | Repetition (n=10) !!   !! colspan="3" | Control&Repetition (n=5)
|-
! align="center" |   !! Pretest !! Immediate !! Delayed !!   !! Pretest !! Immediate !! Delayed !!   !! Pre-pretest !! Pretest !! Immediate
|-
! align="center" |   !!   !! Posttest !! Posttest !!   !!   !! Posttest !! Posttest2 !!   !!   !!   !! Posttest
|-
! align="left" | Length of fluent runs (in syllables) *
| align="center" | 4.42 || align="center" | 4.11 || align="center" | 4.27 ||   || align="center" | 4.42 || align="center" | 4.82 || align="center" | 4.69 ||   || align="center" | 4.58 || align="center" | 4.80 || align="center" | '''5.44'''
|-
! align="left" | Pause length (in sec.) *
| align="center" | 0.92 || align="center" | 1.08 || align="center" | 0.96 ||   || align="center" | 1.18 || align="center" | '''0.96''' || align="center" | 0.99 ||   || align="center" | 0.99 || align="center" | 0.97 || align="center" | 0.84
|-
! align="left" | Phonation/time ratio *
| align="center" | 0.59 || align="center" | 0.55 || align="center" | 0.57 ||   || align="center" | 0.54 || align="center" | '''0.60''' || align="center" | 0.59 ||   || align="center" | 0.57 || align="center" | 0.59 || align="center" | 0.62
|-
! align="left" | Syllables per minute
| align="center" | 194 || align="center" | 190 || align="center" | '''204''' ||   || align="center" | 196 || align="center" | 196 || align="center" | '''204''' ||   || align="center" | 209 || align="center" | 214 || align="center" | 232
|}

<nowiki>*</nowiki> Significant interaction Condition x Time

''Conclusion''

The results of Study 1 suggest that knowledge becomes more easily accessible in subsequent speeches, since phonation/time ratio increases while pauses on average become shorter and the length of fluent runs is at least stable. In the two conditions in which speeches are repeated, the length of fluent runs increases, indicating an advantage over the no-repetition condition, in which this length is stable. It seems, therefore, that repeating a speech about a particular topic enables students to produce longer fluent runs. The overall advantage of these two conditions over the no-repetition condition parallels patterns in the pre- and posttest data.

Overall, it seems that the performance of the no-repetition condition was more variable across speeches. This may be due to an effect of topic, which may be more or less familiar, complex, or linguistically difficult (e.g., eliciting present vs. past tense).

=== Study 2: Formulaic sequences ===

[[Image:FormSeq-per-speech for-wiki.jpg|thumb]]

A pretraining of ten formulaic sequences led to an increase in their use during the 4/3/2 procedure. However, students often used the sequences incorrectly, and some students used them more than others. There was very little transfer to other speaking tasks.
The use of formulaic sequences had a mixed effect on fluency, in that it led to longer fluent runs (higher fluency) but also longer pauses (lower fluency). The trained formulaic sequences were probably not stored as chunks, and retrieval was not automatized
Interestingly, after the pretraining students used more formulaic sequences that were not trained, as compared to the pretest and the students who had not received the pretraining. This is likely to be due to increased awareness of the existence and usefulness of formulaic sequences. These sequences with used with high accuracy.
Overall, it seems that the use of formulaic sequences was not effortless, and had a mixed effect on fluency. The form errors suggest that the students had learned formulaic sequences at the word level, and did not store and retrieve them as chunks (cf. Towell et al., 1996; Wray, 2002).

=== Study 3: Shadowing and formulaic sequences ===
Data transcribed and coded. Analyses in progress.

=== Study 4: The role of vocabulary and grammar in L2 fluency (PRELIMINARY FINDINGS) ===

Several tests were used to assess students' linguistic knowledge (vocabulary, grammar): Immediate Picture Naming, Delayed Picture Naming, Vocabulary Knowledge Scale, and Elicited Imitation. Performance on these tests was mostly as predicted, e.g., in terms of pre/posttest effects and frequency effects, as shown by the following findings.

''Breadth of productive vocabulary: Picture Naming accuracy''

Accuracy improved from pretest (74.2%) to posttest (85.7%), and was higher on the 1-1000 frequency band (85.7%) compared to the 2001-3000 frequency band (74.2%). There was a greater difference between the frequency bands in the Immediate naming condition compared to the Delayed naming condition. Therefore, naming lower-frequency words seems to be more difficult under time pressure (Immediate naming) than under no time pressure (Delayed naming).

''Retrieval speed of vocabulary: Picture Naming response time''

The Delayed naming condition was significant faster (1.046 seconds) compared to the Immediate naming condition (1.794 seconds). Only in the Delayed condition did reaction times improve from pretest (1.303 seconds) to posttest (0.670 seconds). These findings suggest that changes occurred over the course of the semester for the Delayed condition only. This may be due to improvements in articulation rate rather than lexical retrieval since lexical retrieval is executed prior to the cue to name. If improvements in lexical retrieval had occurred, there should have been a reduction in reaction time at the posttest for the immediate condition since immediate naming includes lexical retrieval and articulation rate.

''Vocabulary depth and productive use: Vocabulary Knowledge Scale''

Depth of knowledge for nouns was less affected by frequency than verbs. However, for high-frequency verbs, depth of knowledge was similar to that of nouns. These findings suggest that extra experience or enhanced instruction may be necessary for ESL students to acquire rich representations of low-frequency verbs. The scores for most categories (all nouns and high and mid frequency verbs) were on average around 4 ("I know this word. It means: ..."), which indicates that the students had good receptive depth of knowledge of these words, but were just short of productive knowledge. It seems therefore that these words were ready to be brought into productive use, but most were note in productive use yet, even the 2-3K words.

''Productive grammatical ability: Elicited Imitation''

"Grammatical ability" was tested with a range of forms and structures, morphosyntactic as well as syntactic, and less to more complex (earlier to later acquired). Elicited Imitation accuracy was marginally significantly higher on the posttest (50%) compared to the pretest (45%).

There were different patterns of performance across structures for the correct stimuli compared to the pattern observed for the incorrect stimuli. For correct stimuli, Relative Clauses and Plurals had among the best performance. However, when the stimuli were incorrect, subjects infrequently corrected them leading to poorer performance than expected given the high accuracy on the correct stimuli trials. Other structures like Regular Past Tense and Verb Complements were unaffected by the accuracy of the stimulus, showing no difference between the two conditions. The remaining structures (Embedded Questions, Indefinite Articles, Modals and Third Person –s) showed significantly better performance for the correct compared to the incorrect condition; however, performance on these structures relative to other structures was similar across accuracy conditions. On average students clearly had some knowledge of the grammatical items tested, because they were able to correctly repeat most of the correct stimuli. However, scores were fairly low, well below the 90% that is often used as a criterion for acquisition. Also, performance was strongly affected by ungrammatical stimuli, which shows that the students’ knowledge or processing of the grammatical items was variable. This poor performance was found both for grammatical forms and structures that are typically late acquired (e.g., third person –s, embedded questions) and relatively early acquired (e.g., noun plurals, regular past)

For relative clauses and plurals (and perhaps some other forms), grammatically correct and incorrect forms appear to be seen as two acceptable alternatives, as indicated by the large difference between grammatically correct and incorrect stimuli. For these items, the low scores for incorrect stimuli were almost complementary to high scores for correct stimuli: 70-30%, and 70-15%).

''Linguistic knowledge and oral fluency''

The relationship between linguistic knowledge (vocabulary, grammar) and temporal measures of oral fluency was examined:

'''Pretest'''

Students with greater grammatical ability and deeper vocabulary knowledge spoke with longer fluent runs and higher articulation rates. In addition, students with a greater breadth of vocabulary used longer pauses and filled less time with speech. This finding is surprising. It is possible that students with larger vocabularies tried to use more words and more infrequent words, which led them to speak with lower fluency. This deserves further investigation. Finally, students whose lexical retrieval was faster were able to produce more syllables per minute of speech.

'''Posttest'''

Students with greater grammatical ability spoke with higher articulation rates. Students with a greater breadth of vocabulary knowledge used shorter pauses and filled more time with speech. This finding was expected, but opposite to the pretest. Here, it seems that students with larger vocabularies were able to find words more easily, leading to higher fluency. Finally, students whose lexical retrieval was faster were able to produce more syllables per minute of speech.

Overall, grammatical ability seems to lead to higher articulation rate. Vocabulary depth is related to longer fluent runs and higher articulation rates, whereas findings for vocabulary breadth are variable, leading to longer or shorter pauses and more or less time filled with speech. Faster lexical retrieval is associated with a higher number of syllabes per minute of speech.

''Gains in linguistic knowledge and gains in oral fluency''

To be analyzed

=== Study 5: Priming vocabulary and grammar for fluent production ===
Data transcribed. Coding in progress.

=== Study 6: Piloting picture story prompts (PRELIMINARY FINDINGS) ===

Note: Detailed findings were presented at AAAL 2011 and will be reported in De Jong and Vercellotti (forthcoming).

''Native speakers''

Native-speaker data were examined for seven stories:
Frog, Picnic, Race, Rude Driver, Shopper, Turtle, Tiger

(Frog and Turtle consisted of six pictures selected from wordless storybooks by Mayer, 1967, 1971)

(All other pictures were six-picture stories from Heaton, 1965)

'''Evaluations by speakers'''

Clarity of the pictures and story lines, and recognizability of the characters was rated well for all stories, but lower for Frog and Turtle. Students reported that most stories contained sufficient information to talk about, but Shopper consistently scored low on this point. Shopper scored low on enjoyment/interest, while Tiger scored high (despite the Tiger being killed). All other stories got medium scores on enjoyment/interest, with some variability. Only Tiger had consistently low ratings for the statement “The pictures showed things that might happen in my country”.

''Non-native speakers''

Non-native speaker data for five picture stories were examined:
Frog, Race, Rude Driver, Turtle, Tiger (Picnic and Shopper were dropped)

'''Amount of speech elicited'''

The amount of speech elicited was roughly similar for all five stories. Most students were able to produce close to four minutes of speech. Only with Turtle, a number of students had trouble filling 180 seconds.

In the main study, we will reduce the time available for speaking, from 4/3/2 minutes to 3/2.25/1.5 (180/135/90 sec.), so that there will be some time pressure for everyone.

'''Evaluations by students'''

Rude Driver, Race, and Tiger got the best ratings for clarity, interest, and plausibility. The ending of the Tiger story was evaluated negatively by some students. Frog and Turtle received lower ratings across the board. Frog and Turtle received lower ratings across the board; the image quality of these two stories was slightly lower, and the coherence of the panels may have been less because they were selected from a longer story.

All picture stories had a tight narrative structure. Most picture stories had a complex storyline (foreground and background information), except Tiger. The storyline for Frog can be considered slightly less complex. (Cf. the task features investigated by Foster & Tavakoli, 2009; Tavakoli & Foster, 2008.)

== Explanation ==
This project initially took part in the [[Refinement and Fluency]] cluster. The studies in this cluster concerned the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The general hypothesis was that the structure of instructional activities affects learning.

This project addresses the core issues of task analysis, [[fluency]] from basics, [[in vivo experiment|in vivo]] evaluation, and scheduling of practice. The 4/3/2 task has been analysed into its components. In Study 1, the effect of the component of repetition was investigated. Practice with the basic skills of using vocabulary and grammar was expected to increase [[fluency]]. This would be the case in the Repetition condition, where students had the opportunity to re-use the words, formulaic sequences and grammar in subsequent recordings. In Study 2, students were encouraged to use formulaic sequences that had been taught prior to the fluency training sessions. In Study 3 it was investigated whether shadowing promoted the use of formulaic sequences in spontaneous speech. All three studies took place in an [[in vivo experiment|in vivo]] setting.

The project is currently participating in the [[Cognitive Factors]] thrust.

== Further information ==

For a summer intern project in June and July 2007, Kara Schultz did a multiple case study of six students from Study 1. The project was a first step towards more in-depth analyses of the data of all three studies in the ESL fluency project, addressing the following research questions:
* Does the absence of the need to generate new semantic content in the two retellings during the 4/3/2 task free up headroom, resulting in changes in fluency, morphosyntactic accuracy, and complexity?
* If so, what types of changes occur, and what are the causes for these changes?
* Is there long-term retention of the changes (one week)?

In September 2007 the PSLC executive committee approved our new project plan, in which we proposed follow-up studies that investigate the effect of time pressure and the role of specific knowledge components (vocabulary, grammar) in oral fluency. These studies will be run in the Spring and Fall semesters of 2008.

== Descendants ==
[[Fluency Summer Intern Project | Fluency Summer Intern Project 2007]] -- Kara Schultz

[[Fluency Summer Intern Project 2008]] -- Megan Ross

[[Fluency Summer Intern Project 2009]] -- Maya Randolph

[[Fluency Summer Intern Project 2010]] -- Mariah Warren

[[Fluency Summer Intern Project 2011]] -- Anthony Brohan

[[Explicit and implicit knowledge of infinitival and gerundival verb complements in L2 speech]]

== Annotated bibliography ==

Nation, I.S.P. (1989). Improving speaking fluency. ''System'', ''17'', 377-384.
* Studied the 4/3/2 task. Did not distinguish between effect of speech repetition and time pressure.

Foster, P., & Tavakoli, P. (2009). Native speakers and task performance: Comparing effects on complexity, fluency, and lexical diversity. Language Learning, 59(4), 866-896.
* Studied the effect of narrative structure and storyline complexity on fluency, accuracy, complexity, and lexical diversity in native speakers of English. See also Tavakoli and Foster (2008) for non-native-speaker data.

Freed, B. F., Dewey, D. P., Segalowitz, N., & Halter, R. (2004). The Language Contact Profile. ''Studes in Second Language Acquisition'', ''26'', 349-356.
* Developed a questionnaire about out-of-class language contact. Used in Study 2 and 3.

Freed, B. F., Segalowitz, N., & Dewey, D. P. (2004). Context of learning and second language fluency in French: Comparing regular classroom, study abroad, and intensive domestic immersion programs. ''Studies in Second Language Acquisition'', ''26'', 275-301.
* Used the Language Contact Profile questionnaire. Studied fluency development as a result of study abroad and immersion.

Mizera, G. J. (2006). ''Working memory and L2 oral fluency''. Unpublished doctoral dissertation. University of Pittsburgh, Pittsburgh.
* Includes an investigation of native speaker ratings of oral fluency.

Segalowitz, N., & Freed, B. F. (2004). Context, contact, and cognition in oral fluency acquisition. ''Studies in Second Language Acquisition'', ''26'', 173-199.
* Studied fluency development as a result of study abroad.

Towell, R., Hawkins, R., & Bazergui, N. (1996). The development of fluency in advanced learners of French. ''Applied Linguistics'', ''17'', 84-119.
* Argued that proceduralization of linguistic knowledge shows up in second language speech as longer fluent runs with stable or improving pause length and phonation/time ratio.

Tavakoli, P., & Foster, P. (2008). Task Design and Second Language Performance: The Effect of Narrative Type on Learner Output. Language Learning, 58(2), 439-473.
* Studied the effect of narrative structure and storyline complexity on fluency, accuracy, complexity, and lexical diversity in non-native speakers of English. See also Foster and Tavakoli (2009) for native-speaker data.

== PSLC-related publications and presentations ==
<nowiki>*</nowiki> Images available at top of page

De Jong, N. & Vercellotti, M.L. (2011). Norming picture story prompts for second language production research: Fluency, linguistic items, and speakers’ perception. ''Paper presented at the American Association for Applied Linguistics conference.'' Chicago, IL, March 2011.

Warren, M. (2011). The role of repeated grammatical structures in second language fluency. ''Paper presented at McGill's Canadian Conference for Linguistics Undergraduates.'' Montreal, QC, March 2011.

De Jong, N. & Halderman, L.K. (submitted). The role of vocabulary and grammar knowledge in second language oral fluency.

De Jong, N. & Perfetti, C.A. (2011). Fluency training in the ESL classroom: An experimental study of fluency development and proceduralization. ''Language Learning'', ''61'', 533-568.

Vercellotti, M.L. & De Jong, N. (2010). How does fluency training in the ESL classroom affect language complexity? ''Poster presented at the Third Annual Inter-Science of Learning Center Conference.'', Boston, MA, March 2010.

De Jong, N. & Halderman, L.K. (2010). Vocabulary and grammatical knowledge contribute differentially to second language oral fluency. ''Paper presented at the Third Annual Inter-Science of Learning Center Conference.'' Boston, MA, March 2010.

De Jong, N. & Halderman, L. (2009). The role of vocabulary and grammar knowledge in second-language oral fluency: A correlational study. ''Paper presented at the Second Language Research Forum, East Lansing, MI'', October 2009.

De Jong, N. (2009). Pre-training formulaic sequences and its effect on oral fluency. ''Talk given at the SLA lab meeting, CUNY Graduate Center'', April 24, 2009.

De Jong, N., Halderman, L.K., & Ross, M. (2009). The effect of formulaic sequences training on fluency development in an ESL classroom. ''Paper presented at the American Association for Applied Linguistics conference 2009'', Denver, CO, March 2009.

<nowiki>*</nowiki> Vercellotti, M.L. & De Jong, N. (2009). “I prefer go”: English L2 Verb Complement Errors. ''Poster presented at the Georgetown University Round Table'', Washington, D.C., March 2009.

<nowiki>*</nowiki> Vercellotti, M.L. & De Jong, N. (2009). “I always dessert cake to diet”: Elicited Imitation as an L2 task. ''Poster presented at the Second Annual Inter-Science of Learning Center Conference'', Seattle, WA, February 2009.

De Jong, N. (2008). The study of oral fluency development in ESL. ''Presentation given at the Colloquium on Teaching and Learning World Languages, Queens College of CUNY'', March 2008.

De Jong, N. (2008). Oral fluency development in a second language. ''Presentation given at the Cognitive Approaches to Second Language Acquisition research group at the University of Amsterdam'', January 2008.

De Jong, N., (2007). Approaches to the study of second language acquisition. ''Guest lecture at the CUNY Graduate Center'', December 2007.

De Jong, N., (2007). Oral fluency development in ESL classrooms. ''Guest lecture at the CUNY Graduate Center'', November 2007.

De Jong, N., McCormick, D., O'Neill, C., and Bradin Siskin, C., (2007). Self-correction and fluency in ESL speaking development. ''Paper presented at the American Association for Applied Linguistics 2007 Conference'', Costa Mesa, California, April 2007.

De Jong, N., (2006) Developing oral fluency with the 4/3/2 task. ''Presentation given at the Multimedia Showcase'', University of Pittsburgh, September 2006.

==Screen shots==
[[Image:Fluency_screenshot-notes.jpg|600px]]

Screenshot of the screen where students take notes before they start speaking

[[Image:Fluency_screenshot-speech.jpg|600px]]

Screenshot of the screen where students record their speeches

[[Image:Fluency_screenshot-questions.jpg|600px]]

Screenshot of some of the questions after each speech has been recorded

[[Image:Fluency screenshot-picture-story-prompts.jpg|800px]]

Screenshot of sample picture story prompt

==DataShop==
Pretest and posttest data of Study 4 is available in [https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=186 Datashop].
Transcripts and audio files will be available through the TalkBank database.

Focusing

2011-09-02T20:31:56Z

Mbett: Reverted edits by Shelaybarra (Talk); changed back to last version by Koedinger

Focusing is a kind of instructional [[treatment]] that involves guiding or attracting students attention to target [[knowledge components]] or [[features]] of those knowledge components (see [[feature focusing]]).

Focusing my enhance robust learning through different learning processes:

* Focusing student attention on key [[knowledge components]] may also result in students spending more time during a [[learning events|learning event]] on a particular [[knowledge component]] and thus increase its [[strength]].

* Focusing student attention on relevant [[features]] of knowledge components (and perhaps away from irrelevant features) may help students distinguish the relevant from irrelevant features for retrieving and using knowledge components correctly and with understanding. (See [[feature focusing]]).

[[Category:Glossary]]
[[Category:Independent Variables]]
[[Category:Refinement and Fluency]]

Features of Adaptive Assistance that Improve Peer Tutoring in Algebra (Walker, Rummel, Koedinger)

2011-09-02T20:30:31Z

Mbett: Reverted edits by Elenilowery (Talk); changed back to last version by Erin-Walker

== Collaborative Extensions to the Cognitive Tutor Algebra: Adaptive Assistance for Peer Tutoring ==
''Erin Walker, Nikol Rummel, and Ken Koedinger''

== Summary Tables ==
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| '''PI''' || Erin Walker
|-
| '''Co-PIs''' || Nikol Rummel, Ken Koedinger
|}
 

== Abstract ==
Adaptive collaborative learning support, where an intelligent system assesses student collaboration as it occurs and provides assistance when necessary, is a promising area of research. While fixed forms of support such as scripting student interaction have had a positive effect on collaboration quality, they can overconstrain the interaction for some students and provide too little help for others. Using intelligent tutoring technology to support collaboration might be more effective, but little is known about how to build these adaptive systems for collaboration and what effects they might have. We explore this area of research by augmenting an existing intelligent tutoring system with a peer tutoring activity and providing automated adaptive support to the activity.

This project has focused on how to improve the construction of adaptive collaboration systems with respect to their suitability for classroom deployment and the breadth of the models they employ. Most currently implemented systems are prototypes which are limited both in the scope of interaction that they support and in their use by students. In our first PSLC project, “[[Walker A Peer Tutoring Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition]]", we explored the advantages of refactoring an existing intelligent tutoring system in order to transform it into a platform for collaborative research, such that interface and tutoring components can be added and removed in order to create different research conditions. We next demonstrated how individual intelligent tutoring models could be used as input to collaboration models in order to better assess peer tutoring behaviors, in the PSLC project "[[Adaptive Assistance for Peer Tutoring (Walker, Rummer, Koedinger)|Collaborative Extensions to the Cognitive Tutor Algebra: Adaptive Assistance for Peer Tutoring]]". In this project, we extend this work by examining how individual models of student domain skills can be used as input to interaction models.

A related area of research is the potential of adaptive support for improving student interaction. The majority of the adaptive collaborative learning systems that have been developed have not been evaluated, and thus it is still unclear what influence adaptive support has compared to other forms of support. Our first step in this area was to develop adaptive domain support for the peer tutor, and compare it to a condition where the peer tutor is simply given problem solutions. While both types of support had advantages and disadvantages, it was clear peer tutors needed assistance that targeted collaboration skills in addition to domain knowledge. The next iteration of the system added adaptive interaction support to the adaptive domain support, and we compared the combined assistance to a fixed condition in a classroom study. As part of this project, we analyzed the study data in order to identify the broad impact both types of support have on the quality of student interaction, finding that adaptive support improves the quality of student help over fixed support. We now propose to investigate in more detail the potential role adaptive feedback could play in assisting student interaction by: 1) using HCI design methodologies to examine how students perceive and react to different features of support, and 2) empirically evaluating whether adaptive support has a cognitive or motivational influence on students. As an outcome of this research, we expect to add to understanding of the mechanisms by which adaptive support has an impact on student interaction, and how the support should be provided.

== Background & Significance ==

'''Peer Tutoring: Learning by Teaching'''

Incorporating peer tutoring into the CTA might be a way to encourage deep learning. Roscoe and Chi conclude that peer tutors benefit due to knowledge-building, where they reflect on their current knowledge and use it as a basis for constructing new knowledge (Roscoe & Chi, 2007). Because these positive effects are independent of tutor domain ability, researchers implement reciprocal peer tutoring programs, where students of similar abilities take turns tutoring each other. This type of peer tutoring has been shown to increase academic achievement and positive attitudes in long-term classroom interventions (Fantuzzo, Riggio, Connely, & Dimeff, 1989). Biswas et al. (2005) described three properties of peer tutoring related to tutor learning: tutors are accountable for their tutee’s knowledge, they reflect on tutee actions, and they engage in asking questions and giving explanations. Tutee learning is maximized at times when the tutee reaches an impasse, is prompted to find and explain the correct step, and is given an explanation if they fail to do so (VanLehn et al., 2003).

Peer tutors rarely exhibit knowledge-building behaviors spontaneously (Roscoe & Chi, 2007), and thus successful interventions provide them with assistance in order to achieve better learning outcomes for them and their tutees. This assistance can target tutoring behaviors through training, providing positive examples, or structuring the tutoring activities. For example, training students to give conceptual explanations had a significantly positive effect on learning (Fuchs et al., 1997). It is just as critical for assistance to target domain expertise of the peer tutors, in order to ensure that they have sufficient knowledge about a problem to help their partner solve it. Otherwise, there may be cognitive consequences (tutees cannot correctly solve problems) and affective consequences (students feel that they are poor tutors and become discouraged; Medway & Baron, 1997). Domain assistance can take the form of preparation on the problems and scaffolding during tutoring (e.g., Fantuzzo, Riggio, Connely, & Dimeff, 1989). Although assistance for peer tutoring has generally been fixed, providing adaptive support may be a promising approach.

'''Adaptive Collaborative Learning Systems'''

In order to benefit from collaboration students must interact in productive ways, and collaborative activities can be structured (scripted) to encourage these behaviors (e.g., Rummel & Spada, 2007). However, fixed scripts implemented in a one-size-fits-all fashion may be too restrictive for some students and place a high cognitive demand on others (Rummel & Spada, 2007; Dillenbourg, 2002). An adaptive system would be able to monitor student behaviors and provide support only when needed. Preliminary results suggest that adaptive support is indeed beneficial: Adaptive prompting realized in a Wizard of Oz fashion has been shown to have a positive effect on interaction and learning compared to an unscripted condition (Gweon, Rose, Carey, & Zaiss, 2006). An effective way to deliver this support would be to use an adaptive collaborative learning system, where feedback on collaboration is delivered by an intelligent agent.

Work on adaptive collaborative learning systems is still at an early stage. One approach is to use machine learning to detect problematic elements of student interaction in real-time and trigger helpful prompts. Although implementations have lead to significant learning gains, the adaptive feedback appears to be disruptive to dyadic interaction (Kumar et al., 2007). Another promising approach has explored using an intelligent agent as one of the collaborators; students teach the agent about ecosystems with the help of a mentoring agent (Biswas et al. 2005). However, the agents do not interact with the students in natural language, one of the primary benefits of collaboration. With respect to peer tutoring, intelligent tutoring technology could be applied either to supporting tutor behaviors or supporting the domain knowledge of peer tutors.

== Glossary ==
See [[:Category:Peer Tutoring|Peer Tutoring Glossary]]

== Research Questions ==

Can individual problem-solving models improve the effectiveness of adaptive collaborative learning support by providing more problem-solving context for models of collaboration? Do they make it easier to construct adaptive collaborative learning support systems?

What are the differential effects of adaptive and fixed support on student collaborative process during a peer tutoring activity, the acquisition of help-giving skills, and the resulting [[robust learning]] outcomes? Does adaptive support improve student ability to collaborate, student motivation to collaborate, or both?

== Independent Variables ==
1. ''Actual adaptivity of interaction support.'' We vary whether students are given support with highly relevant content at the moments they need it, or support with random content at moments when it is not needed.
2. ''Perceived adaptivity of interaction support.'' We vary whether students believe the support they are receiving is adaptively or randomly chosen.
3. ''Student role.'' We vary whether students take on the tutor or tutee role.

 Peer tutoring in the Cognitive Tutor Algebra. Adaptive interaction support received by the peer tutor. 
[[Image:Walker_adaptive__interaction_support.jpg]]

== Hypotheses ==
1. Peer tutors that show effective tutoring behaviors will show more domain learning than students that show ineffective tutoring behaviors.

2. Peer tutees that receive good tutoring will show more domain learning than peer tutees that receive bad tutoring.

3. Peer tutors that believe the assistance that they are receiving is adaptive will improve the quality of their tutoring, because they will feel more accountable for their behaviors.

4. Peer tutors that receive adaptive assistance will improve the quality of their tutoring, because they will be able to more easily apply the assistance to their behaviors.

== Dependent variables ==
* ''[[Normal post-test]]'': Students are given a brief post-test immediately after each study day on isomorphic problems
* ''Far [[transfer]]'': This paper and pencil test assessed students' understanding of the main mathematical concepts from the learning phase. The transfer items students had to solve tapped the same knowledge components as the problems in instruction, however, the problems where non-isomorphic to those in the instruction, thus demanded students to flexibly apply their knowledge to problems with a new format.
* ''Collaboration posttest'': Students collaborate without support in order to determine if they've improved their tutoring skills.

To compare [[collaboration skill]]s of students, we will be conducting an analysis of student dialogs during the learning phase.

To assess immediate effects of the instructional variations, we will analyze student progress on training problems as they work through the instruction.

== Findings ==
We are in the process of conducting a lab study with roughly 120 students. Out of this study, we expect to analyze in detail the effects of adaptive interaction support on student interaction, student acquisition of collaborative skills, and domain learning.

== Annotated bibliography ==
* Walker, E., Rummel, N., & Koedinger, K. R. Integrating collaboration and cognitive tutoring data in evaluation of a reciprocal peer tutoring environment. Research and Practice in Technology Enhanced Learning.
* Walker, E., Rummel, N., & Koedinger, K. R. CTRL: A Research Architecture for Providing Adaptive Collaborative Learning Support. User Modeling and User-Adapted Interaction.
* Walker, E., Rummel, N., and Koedinger, K. R. To Tutor the Tutor: Adaptive Domain Support for Peer Tutoring. To appear at the 9th International Conference on Intelligent Tutoring Systems. 2008.
* Walker, E., McLaren, B. M., Rummel, N., and Koedinger, K. R. Who Says Three's a Crowd? Using a Cognitive Tutor to Support Peer Tutoring. 13th International Conference on Artificial Intelligence and Education. 2007.
* Walker, E., Rummel, N., McLaren, B. M. & Koedinger, K. R. The Student Becomes the Master: Integrating Peer Tutoring with Cognitive Tutoring. Short paper at the Conference on Computer Supported Collaborative Learning (CSCL-07). Rutgers University, July 16-21, 2007.
* Walker, E., Koedinger, K., McLaren, B. M., & Rummel, N. (2006). Cognitive tutors as research platforms: Extending an established tutoring system for collaborative and metacognitive experimentation. ''Lecture Notes in Computer Science, Volume 4053/2006. Proceedings of the 8th International Conference on Intelligent Tutoring Systems'' (pp. 207-216). Berlin: Springer
* Walker, E. (2005). Mutual peer tutoring: A collaborative addition to the Algebra-1 Cognitive Tutor. Paper presented at the 12th International Conference on Artificial Intelligence and Education (AIED-05, Young Researchers Track), July, 2005, Amsterdam, the Netherlands.

== References ==
* Roscoe, R. D. & Chi, M. Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutors’ explanations and questions. Review of Educational Research 77(4), 534-574 (2007)
* Fantuzzo, J. W., Riggio, R. E., Connelly, S., & Dimeff, L. A. Effects of reciprocal peer tutoring on academic achievement and psychological adjustment: A component analysis. Journal of Educational Psychology 81(2), 173-177 (1989)
* Biswas, G., Schwartz, D. L., Leelawong, K., Vye, N., & TAG-V. Learning by teaching: A new agent paradigm for educational software. Applied Artificial Intelligence 19, 363–392 (2005)
* VanLehn, K., Siler, S., Murray, C., Yamauchi, T., & Baggett, W. Why do only some events cause learning during human tutoring? Cognition and Instruction 21(3), 209-249 (2003)
* Fuchs, L., Fuchs, D., Hamlett, C., Phillips, N., Karns, K., & Dutka, S. Enhancing students’ helping behaviour during peer-mediated instruction with conceptual mathematical explanations. The Elementary School Journal 97(3), 223-249 (1997)
* Medway, F. & Baron, R. Locus of control and tutors’ instructional style. Contemporary Educational Psychology, 2, 298-310 (1997).
* Rummel, N. & Spada, H. Can people learn computer-mediated collaboration by following a script? In F. Fischer, I. Kollar, H. Mandl &, J. Haake, Scripting computer-supported communication of knowledge. Cognitive, computational, and educational perspectives (pp. 47-63). New York: Springer. (2007)
* Dillenbourg, P. Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In P. A. Kirschner (Ed.), Three worlds of CSCL. Can we support CSCL (pp. 61-91). Heerlen: Open Universiteit Nederland. (2002)
* Gweon, G., Rosé, C., Carey, R. & Zaiss, Z. Providing Support for Adaptive Scripting in an On-Line Collaborative Learning Environment. Proc. of CHI 2006, pp. 251-260. (2006)
* Kumar, R., Rosé, C. P., Wang, Y. C., Joshi, M., Robinson, A. Tutorial dialogue as adaptive collaborative learning support. Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED 2007), Amsterdam: IOSPress. (2007)

== Connections ==
This study is an extension of the PSLC project "[[Walker A Peer Tutoring Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition]]" and "[[Adaptive Assistance for Peer Tutoring (Walker, Rummer, Koedinger)|Collaborative Extensions to the Cognitive Tutor Algebra: Adaptive Assistance for Peer Tutoring]]."

Like this study, [[Rummel Scripted Collaborative Problem Solving]] adds scripted collaborative problem solving to the Cognitive Tutor Algebra. The studies differ in the way collaboration is integrated in the Tutor. First, in the Rummel et al. study, both students first prepare one subtasks of a problem to mutually solve the complex story problem later on. Thus, although the students are experts for different parts of the problem, they have a comparable knowledge level during collaboration. In contrast, in this study, one student prepares to teach his partner. Then, they change roles. Thus, during collaboration, their knowledge level differs. Second, in the Rummel et al. study, collaboration was face to face, whereas this study used a chat tool for interaction.

Similar to the adaptive script component of the Collaborative Problem-Solving Script, the [[The Help Tutor Roll Aleven McLaren|Help Tutor project]] aims at improving students' [[help-seeking behavior]] and at reducing students' tendency to [[game the system]]. Furthermore, both studies contain instructions to teach [[metacognition]]. The metacognitive component in our study instructs students to monitor their interaction in order to improve it in subsequent collaborations; the Help Tutor project asks students to evaluate their need for help in order to improve their help-seeking behavior when learning on the Tutor.

Both in this study and in the [[Reflective Dialogues (Katz)|Reflective Dialogue study]] from Katz, students are asked to engage in reflection following each problem-solving. In this study, the reflection concentrates on the collaborative skills, while in Katz' study, the reflection concentrates on students' domain knowledge of the main principles applied in the problem.

Furthermore, both our study and the [[Help Lite (Aleven, Roll)]] aim at improving conceptual knowledge.

This project relates the more general thrust goals as follows. It is examining how features of assistance affect the three aspects of accountable talk: accountability to knowledge, accountability to rigorous thinking, and accountability to the learning community. Steps are being made toward the ambitious goal to operationalize and assess these aspects of accountable in real time as students interact and receive assistance in this computer-mediated environment. There is also a potential to code the three way dialog (student tutee, student tutor, and computer tutor) for transactivity. In particular, the student tutee's dialog moves have not yet been coded, but appear to have interesting elements, like asking for specific help or self-explaining, that may well connect to transactivity codes. Finally, there is a potential to analyze the computer tutor's reflective prompts for similarity with accountable talk moves and associated effectiveness. Some of the prompts were indeed inspired by accountable talk moves.

[[Category:Study]]

Fluency Summer Intern Project 2011

2011-09-02T20:30:07Z

Mbett: Reverted edits by Elenilowery (Talk); changed back to last version by Brohan

== Background ==

Lexical richness plays a role in terms of overall language proficiency. Lexical richness is a broad term encompassing the depth and breadth of vocabulary a speaker is able to deploy.

Several papers have proposed measures of lexical richness, which all measure different aspects of lexical richness. Read (2000) unpacks the idea of lexical richness into lexical diversity and lexical sophistication. Lexical diversity measures ''range'' of words used by a speaker, whereas lexical sophistication measures the amount of ''advanced'' words a speaker uses. Measures of richness usually focus on either diversity or sophistication.

Measures of lexical sophistication depend on using a word list to define ‘advanced words’. Different studies have used different word lists. For instance, the Academic Word List (Coxhead, 2000) collected classroom data (van Hout & Vermeer 1992), the spoken segment of BNC corpus (Skehan 2009). Diverging approaches pose problems to the design of studies.
Skehan (2009) has done a similar study comparing measures of lexical diversity and lexical sophistication. Skehan finds that measures of diversity and sophistication are very poorly correlated, and posits that these two aspects of lexical richness are independent. Given a range of task types, Skehan argues that lexical sophistication is related to the conceptualizer and diversity is related to the formulator in Levelt’s (1989) model of L2 production.

== Research Question ==

Given several measures of lexical richness, which perform the best at discriminating group differences between EFL learners at low-intermediate, high-intermediate and low-expert levels? Given different word lists, which perform the best for measures of lexical sophistication?

How are lexical diversity and lexical sophistication related? Do they exist as independent constructs, as in Skehan (2009) or are they related? Is their relation related to proficiency levels?

== Measures ==
Several measures exist for evaluating lexical richness, some which are more focused on diversity and others which are more focused on sophistication. Below is a brief outline of the measures used in the project. Most of these measures are not well suited to short transcripts (> 100 words).

=== Lexical Diversity ===
Lexical diversity is most basically measured by the type-token ratio, which looks at the number of types (lexemes) to the number of tokens (words). This measure is however problematic, as the number of unique types in a text decreases as the text gets longer. Each of the following measures offers a compensation for text length.

'''Measures'''

* G (Guiraud, 1954)
:Simple re-calculation of TTR by using Types/√ Tokens.
* voc-D (Malvern & Richards, 1997)
:Measure of vocabulary deployment. Computed by using a curve fitting operation on the TTR of random samples of tokens of different sizes. Shown to be a robust measure of lexical diversity. voc-D is used as the standard measure of lexical diversity in EFL studies.
* MTLD (McCarthy, 2006)
:The average number of tokens to reach a TTR criteria (0.72). MTLD is computed sequentially forwards and backwards. MTLD has been used in validation studies and written corpora and seems to be a promising measure of lexical diversity.

===Lexical Sophistication===
Lexical sophistication is the ability of a speaker to be able to deploy advanced words. Studies differ in how they define 'advanced words'. This study uses three different word lists to operationalize 'advanced' words.
# BNC Spoken Segment (10 million tokens), Cutoff 150/million
# BNC Spoken Segment (10 million tokens), Cutoff 150/million + Task Specific words (words used by more than half the participants in a task.
# ELI Corpus (50,000 tokens). Data collected across all transcripts used in the study. Cutoff 140/million.
'''Measures'''
* Advanced G (Daller et. al, 2003)
: Modified version of the Guiraud measure: Advanced Types/√ Tokens. Measures the ''range'' of advanced types used.
* P_Lex (Meara & Bell, 2011)
: Sophisticated measure which counts the number of advanced words occuring every 10 words, and fits a poisson distribution to the count frequencies. Measures the ''saturation'' of advanced words in text.

==Participants & Data==
Data was collected through the English Language Institute of the University of Pittsburgh. Participants (n=85) recorded 2-minute speeches (n=345) on different topics as part of the teaching curriculum of a spoken English courses. Speeches were recorded at three different times during the semester. Participants were in two cohorts, and speeches were collected across three semesters and three levels (3,4,5) of English Speaking classes.

Participants come from mixed L1 backgrounds, and most participants have significant experience studying English in a classroom prior to the ELI course (71% studied English > 3 years).

==Analysis & Results==
All speeches from each level was grouped together, one-way ANOVA was used to estimate effect sizes.

[[Image:Brohan2011-graph1.jpg]]

{| class="wikitable sortable" border="1"
|+ Effect Sizes
|-
! scope="col" | Measure
! scope="col" | Effect Size (eta squared)
|-
| MTLD || 0.122
|-
| G || 0.103
|-
| D || 0.076
|-
| L3_AG || 0.146
|-
| L1_AG || 0.089
|-
| L2_AG || 0.065
|-
| L2_PLex || 0.060
|-
| L1_PLex || 0.059
|-
| L3_PLex || 0.020
|}

* Measures of lexical diversity are robustly able to discriminate groups. MTLD has the highest effect size of lexical diversity measures.
* Lexical sophistication measures (using list 1 & 2) are poorly able to discriminate group differences. Participants near-equal levels of sophistication scores can be explained by prior English education (focusing on vocabulary).
* Advanced Guiraud using word list 3 performs the best at discriminating group differences. However, word list 3 is biased in it's construction (topics were not balanced by number of participants).
* Topics varied in complexity and accordingly there was a significant interaction of measures of lexical diversity and Advanced Guiraud within-levels between topics in levels 3,4.
* P_Lex is an inappropriate measure to use for short texts, was unable to discriminate group differences.

=== Diversity & Sophistication ===

* Skehan's findings replicated: moderate correlations between lexical diversity measures and lexical sophistication measures. (0.41 correlation between C3_AG and MTLD).

To check construct validity, factor analysis was run using Principal Components Analysis. Using z-normailzed measures of G,D,MTLD and Advanced Guiraud measurements using all three word lists factor analysis was performed. Two components were identified (λ > 1.0). Promax rotation with kaiser normalization was performed on the principal components.

[[Image:Brohan2011-graph3.jpg]]

We can conclude based on the moderate correlation and the composition of the rotated components that lexical diversity and lexical sophistication can be considered separate, but related constructs.

==References==
Daller, H. et al. (2003) Lexical Richness in the Spontaneous Speech of Bilinguals. Applied Linguistics (24) 2, 197-222

Guiraud, P. (1954) ''Les caractères statistiques du vocabulaire'', Paris: Presses universitaires françaises.

Levelt, W.J.M. (1989) ''Speaking: from intention to articulation'', Cambridge, Massachusetts: The MIT Press.

Malvern D. D. & Richards, B. J. (1997) A new measure of lexical diversity. In A. Ryan & A. Wray (Eds.), ''Evolving models of languag''e 58-71 Clevedon: Multilingual Matters

McCarthy, P.M. (2006) An assessment of the range and usefulness of lexical diversity measures and the potential of the measure of textual, lexical diversity (MTLD). ''Dissertation Abstracts International'' 66 (12)

Meara, P. & Bell, H. P_Lex: A simple and effective way of describing the lexical characteristics of short L2 texts. ''Prospect'' 16 (3), 5-19

Skehan, P. (2009) Modelling Second Language Performance: Integrating Complexity, Accuracy, Fluency, and Lexis. ''Applied Linguistics'' 30 (4)

Features

2011-09-02T20:29:51Z

Mbett: Reverted edits by Elenilowery (Talk); changed back to last version by Koedinger

[[Category:Glossary]]
[[Category:PSLC General]]
Features are the individual properties of a [[knowledge component]] (KC) that determine the retrieval conditions of that KC, that is, when a student uses or thinks of a particular action or idea (e.g., divide both sides of an equation, pick 'a' vs. 'the' as an article).

If the KC is part of the knowledge that we want students to learn, and it makes sense to distinguish contexts where the KC should and should not be applied, then we can also distinguish ''relevant'' features of the KC from ''irrelevant'' features. Relevant features tend to be present in contexts where the KC should be applied and absent in contexts where the KC should not be applied. Irrelevant features tend to be absent in contexts where the KC should be applied and/or present in contexts where the KC should not be applied. A knowledge component that has just relevant features and no irrelevant features has high [[feature validity]].

Sometimes features are relatively directly perceivable (seen or heard). In the language literature, such features are called cues. Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser (1981) distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.

A number of projects provide some good examples of KC feature analysis including [[Booth | Julie Booth's in Algebra]] and [[FrenchCulture|Amy Ogan's in French]]. In both, much of the instructional design is focused on helping students to [[refinement|refine]] their knowledge, that is, learn the relevant deep features (e.g., a term includes a number and its sign, positive or negative) and distinguish them from irrelevant shallow features (e.g., a number without it's sign).

=== References ===
* Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.

Flow

2011-09-02T20:29:32Z

Mbett: Reverted edits by Elenilowery (Talk); changed back to last version by Tlatoza

Flow is the affective state of optimal experience that creates pleasure by balancing the challenge of the task at hand to the skills of the person. Originally discovered and popularized [1] by Mihaly Csikszentmihalyi ("chick-sent-me-high-ee"), flow has been studied across diverse tasks ranging from athletics to learning by students to games. More recently, flow states of students using learning systems have been measured to understand its relation to learning outcomes.

==Foundations of flow==
Flow, as originally formulated by Csikszentmihalyi and described in his book [1], has 8 components that are highly associated with the state of flow:

'''1. A challenging activity that requires skill'''

Flow is associated with challenging activities that requires skill to accomplish. In particular, flow occurs when the "opportunities for action perceived by the individual are equal to his or her capabilities." [1](p52). An activity too challenging leads to anxiety; not challenging enough leads to boredom.

'''2. The merging of action and awareness'''

Flow activities are associated with the complete occupation of attentional resources by the task at hand.

'''3. Clear goals and feedback'''

Individuals need clear, specific goals and feedback moment to moment on if they are being accomplished. For example, a painter might have criteria to decide, for each brush stroke, if it is good or bad.

'''4. Concentration on the task at hand'''

Individuals completely ignore any task irrelevant stimuli or recollections from memory.

'''5. The Paradox of control'''

Flow activities are associated with the perception of control rather than the actuality of control. Individuals perceive that their actions have consequences: if they act just right, they can achieve perfection. But external events may always cause this control to be imperfect.

'''6. The loss of self consciousness'''

Individuals no longer attend to their ego or engage in self scrutiny or judgments of self.

'''7. The transformation of time'''

Due to the intense concentration on the task, perceived time is no longer related to actual time.

'''8. The Autotelic experience'''

Flow activities are intrinsically rewarding - doing the activity itself is the reward, not reinforcement after the task (e.g., money). Individuals engage in flow activities for the sake of engaging in flow activities.

The data for results come from two types of studies. Csikszentmihalyi interviewed a huge variety of individuals and asked them to report on their most enjoyable experiences. From these retrospective accounts, he pulled out components and aspects that frequently occurred. Csikszentmihalyi also employed the experience sampling methodology in which participants were equipped with a beeper which semi-randomly went off. This triggered the participant to complete a short survey reporting on aspects of their current activity and current affect. These studies had the benefit of not being as clouded by memory biases or generalizations.

==Flow in education==
Flow has been used as a construct for understanding relationships between interventions and outcomes for students in the classroom. In particular, in a study examining the conditions in which after school activities improve students' rates of absence, suspension, and lateness and their English grades, experiencing flow during these activities made students significantly more likely to show positive outcomes [6]. Thus, flow is a useful measure of the utility of after school activities in benefiting students.

==Flow in learning systems==
More recently, studies investigating the affective state of learners using computerized learning systems and intelligent tutors have measured flow alongside other affects such as boredom, confusion, and frustration. Flow is particularly important because it is a desirable state for learners to be in. Flow has been shown to be associated with learning gains. In a study of students using Autotutor, flow was found to have a correlation with learning gains (knowledge pre and post task) of .29 [2].

However, attaining the benefits of flow requires careful design of the system to ensure students achieve flow. In a study of a hypertext learning system, students using an improved version of the system were not more likely to experience flow [8]. The authors speculate this failure could be due to the high incidence of apathy, which might have been caused by the rigid standardization of the tasks, reduced challenge, and reduced perceived skills. But flow was worth achieving in that, across all dimensions, the enjoyment and quality of the experience was highest in flow states.

==Temporal dynamics of flow in learning systems==
Flow does not necessarily persist indefinitely - it requires the right conditions to remain present. When learners were asked to report their current level of challenge and skills after each 7 subtasks using a learning system, whether or not students were in the flow zone - optimal balance between skill and challenge - fluctuated wildly with students jumping between flow, boredom, and anxiety [7]. Interestingly, these measures of flow over the course of the activities were unrelated to a final post-task assessment of enjoyment and control.

Theories of learning have postulated that the transitions between affective states are important for realizing learning gains. To empirically examine such theories, studies have examined transitions between affective states in the course of using a learning system [4][5]. Flow has been found to be a sink state - learners in flow are more likely than chance to stay in flow rather than transition to other affects. And they are unlikely to transition to boredom, confusion, or delight. Consistent with the idea that flow is associated with concentration on the task at hand, they are also less likely to game the system.

==Measuring flow==
Depending on the setting and purpose, a variety of approaches have been used to measure flow. In interviews, flow has been measured by its correspondence to the components identified by Csikszentmihalyi [1]. In experience sampling method studies, these measures have typically been less direct and focused on enjoyment of the activity rather than individual components.

There are several approaches to measuring flow in lab or field studies of learners using learning systems. One approach is to ask participants to rate their flow experiences. Studies have used methods such as rating the challenge and skills available at different points in the task [7]. Another is to have experimenters observe participants' actions and facial expressions to rate affective state, including flow [4][5]. This is usually done by sampling over a period of time to give the observer enough data to make a rating or allow them to switch between multiple participants.

To enable learning systems to use affective ratings of participants to influence their behavior, completely automated measurements of flow are desirable. To do so, both vision sensors and posture sensors recording the pressure on the seat at different points have been used. One study found posture sensors to be the most informative for detecting flow [3].

==References==
1. Csikszentmihalyi, M. (1990). Flow - the psychology of optimal experience. New York, Harper.

2. Craig, S.D. (2004). Affect and learning: an exploratory look into the role of affect in learning with AutoTutor.

3. D'Mello, S., Picard, R., and Graesser, A. (2007). Towards an affect-sensitive AutoTutor. IEEE Intelligent Systems.

4. Baker, R.S.J.d., Rodrigo, M.M.T., and Xolocotzin, U.E. (2007). The dynamics of affective transitions in simulation problem-solving environments. Affective Computing and Intelligent Interaction. 666-677.

5. D'Mello, S., Taylor, R.S., and Graesser, A. (2006). Monitoring affective trajectories during complex learning.

6. Shernoff, D. J., Vandell, D. L, & Bolt, D. M. (2008). Experiences and emotions as mediators in the relationship between after-school program participation and developmental outcomes. Long-term impact and outcomes of out- of-school time programs, Symposium conducted at the annual meeting of the American Educational Research Association, New York, NY.

7. Pearce, J. M., Ainley, M., & Howard, S. (2005). The ebb and ﬂow of online learning. Computers in Human Behavior, 21, 745–771.

8. Konradt, U., Filip, R., and Hoffmann, S. (2003). Flow experience and positive affect during hypermedia learning. British Journal of Educational Technology, 34(3), 309-327.

Feature validity

2011-08-31T15:43:21Z

Mbett: Reverted edits by Pierrehernandez (Talk); changed back to last version by Koedinger

[[Category:Glossary]]
[[Category:Coordinative Learning]]

The feature validity of a [[knowledge component]] measures how well the [[features]] associated with the mental representation of the knowledge component match the features present during all situations where the component should be recalled.

A student has acquired a knowledge component (KC) with high feature validity when the retrieval features of that knowledge component are all relevant and none are irrelevant. Through the learning process of [[refinement]] a learner may modify an existing KC to produce a new one with higher feature validity.

Feature validity is a generalization of the standard concept of cue validity. Cues are usually understood to be perceptual or at least rapidly computed (McDonald & MacWhinney, 1989). The term “features” includes cues as well as higher level properties, such as those used by experts but not novices (Chi, Feltovitch, & Glaser, 1981).

See the [[Booth]] page for examples of knowledge components with different levels of feature validity.

=== References ===
* Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.
* McDonald, J. L., & MacWhinney, B. (1989). Maximum likelihood models for sentence processing research. In B. MacWhinney & E. Bates (Eds.), The crosslinguistic study of sentence processing (pp. 397-421). New York: Cambridge University Press.
* Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351).

Explicit instruction

2011-08-31T15:42:54Z

Mbett: Reverted edits by Pierrehernandez (Talk); changed back to last version by Koedinger

Explicit instruction occurs in an instructional task that provides the learner with specific information or directions about what is to be learned from the task. Explicit instruction often comes in the form of rules or verbal statements that provide guidance to the student about what is to be learned. Although explicit instruction contrasts with [[implicit instruction]], instructional tasks are often graded with elements of each. Thus instruction can be relatively explicit or relatively implicit. Explicit instruction works through [[feature focusing]], drawing the learner's attention to the valid or critical features ([[feature validity]]) of the content to be learned. Implicit learning can be designed to promote feature focusing, as well, although often it does not.

[[Instructional explanation]]s are one specific type of explicit instruction.

[[Category:Glossary]]
[[Category:Independent Variables]]
[[Category:Help Tutor]]
[[Category:PSLC General]]

Explicit and implicit knowledge of infinitival and gerundival verb complements in L2 speech

2011-08-31T15:42:10Z

Mbett: Reverted edits by Pierrehernandez (Talk); changed back to last version by Ndjong

[This page is under construction]

== Abstract ==

The present study set out to investigate whether students were able to use their explicit knowledge of verb complements during spontaneous speech. Spontaneous speech data was elicited from 32 high-intermediate English as a Second Language (ESL) students. Results show that students made errors that were not typical of explicit knowledge, in that they did not merely switch verb complement types. Instead, they used many unmarked complements or doubly-marked complements. This suggests they relied on implicit knowledge, possible acquired from exposure to the target language. Variable input due to matrix verbs that can take either type of complement may have affected the acquisition of the correct complement forms.

Teachers cannot assume that the variable matrix verbs are the easiest ones because they allow both forms. Instead, these verbs may need additional instruction. In addition, instruction needs to include sufficient practice in order to stimulate the acquisition of implicit knowledge.

== Background and significance ==

It is a common phenomenon that second language (L2) learners know many grammatical rules but do not apply them correctly and consistently when speaking. In class, students often acquire explicit knowledge of grammatical structures, but this is slow to use and requires attentional resources (De Jong, 2005; DeKeyser, in press; Ellis, 2005, 2006; Hulstijn, 2002). Therefore, to be able to speak with high fluency and grammatical accuracy, it is necessary for students to acquire implicit knowledge, because it is faster to use and does not require as much attentional resources.

The present study set out to investigate whether students were able to use their explicit knowledge of verb complements during spontaneous speech. If explicit knowledge is used, L2 learners may choose the two verb complement forms equally; if implicit knowledge is used, L2 learners might show preference for one of the two forms. If explicit knowledge is being used, it can be expected that errors would involve switching the two types of complements. However, if other errors would occur, implicit knowledge is likely being used and possibly L1 transfer may be involved.

== Research question ==

* Do high intermediate ESL students rely on their explicit knowledge of a variable grammatical structure (verb complements) during spontaneous speech, or do they rely more on implicit knowledge instead?

== Method ==
Participants were 32 students enrolled in high intermediate Speaking classes. They had been taught the grammatical structure of English verb complements, in that some verbs take a to-infinitive, some take a gerund, and others take either a to-infinitive or gerund as a complement. The students took part in three sessions of the 4/3/2 task, in each of which they spoke three times, for four, three, and two minutes, respectively. Fifteen students repeated the same topic during a session, while 8 other students spoke about three different topics. In total, 27 minutes of speech were elicited from each student.

The speeches were transcribed in PRAAT, and annotated in CHAT/CLAN (the software of the Childes project). Codes were added for parts of speech, errors, and retracings (repetitions, corrections, and reformulations). All verb complements (correct and errors) were retrieved to be further analyzed.

== Independent variables ==
* Type of matrix verb requirement
**to-infinitive
**gerund
**either

== Dependent variables ==
* Type of complement produced
**to-infinitive
**gerund
**ambiguous
*error
**appropriate
**not appropriate

== Hypotheses ==

* If students rely more on explicit knowledge of the target structure, errors will involve mostly switching of the verb complement types.
* If students rely more on implicit knowledge of the target structure, other types of errors will be made, e.g., use of the default form.

== Findings ==

''Verb complement use and accuracy''
All participants attempted the target structure; the range was 7-44 verb complement attempts per student. The mean accuracy per student was 82.2% (12%) with a range of 60 – 100%.

Matrix verbs that require a gerund were produced least often (16) but most accurately. More instances were produced of matrix verbs that require an infinitive (152) or allow either a gerund or a to-infinitive (352). The error rate for both groups of these matrix verbs was around 15%. The error pattern for these types of matrix verbs was very similar.

Errors were rarely a result of a mismatch between matrix verb and verb complement, as when a gerund would be used instead of a to-infinitive or vice versa (e.g., I enjoy watch TV, I want studying). Instead, the most common error was producing only the root verb with neither the infinitival to marker nor the gerundival -ing, while the next common error was using both markers.

''L1 transfer''
To explore the origins of the preference for using gerunds and to-infinitive markers, we investigate L1 influence. The Korean L1 and Chinese L1 speakers had much lower means of verb complement structures per student (about 16) than the other L1 groups (over 30). The Korean L1 students and the single Russian speaker had slightly lower accuracy (about 73%) than the other L1 groups.

It might be predicted that students from L1s with less morphology produced the ambiguous verb complements with neither morpheme. This expectation was not supported because L1 did not seem to impact the type of errors.

== Explanation ==

If explicit knowledge were used, it would be likely that students knew the correct verb complement forms, but made errors selecting the appropriate complement for a particular matrix verb. Thus, gerunds would be used instead of to-infinitives, and vice versa. In addition, there would be an even distribution of the two options. Instead, the most common error was a lack of either morphological marking, only producing the root verb for the verb complement.

A possible explanation is that the online demands of the production task and trying to apply explicit knowledge about the matrix verb requirements were too great, resulting in no morphological marking. Therefore, the students may have relied on their implicit knowledge during this production task.

One type of implicit knowledge may have stemmed from experience with the target language. Of the matrix verbs that can take either verb complement form, 13% of the verb complements were ambiguously produced. This finding might reflect the difficulty of learning a structure that has varied input; there is no clear collocation with the matrix verbs, especially in the variable category (like, prefer, love) despite the high frequency of these verbs.

Teachers cannot assume that the variable matrix verbs are the easiest ones because they allow both forms. Instead, these verbs may need additional instruction. In addition, instruction needs to include sufficient practice in order to stimulate the acquisition of implicit knowledge.

== Further information ==

The results of this study were presented at the GURT conference in March 2009, and are under review for the proceedings. The study was performed by Mary Lou Vercellotti from the University of Pittsburgh and Dr. Nel de Jong from Queens College of CUNY.

This study was part of the project [[Fostering_fluency_in_second_language_learning|Fostering fluency in second language learning]] by Nel de Jong, Laura Halderman, and Charles Perfetti.

Example-rule coordination principle

2011-08-31T15:41:48Z

Mbett: Reverted edits by Pierrehernandez (Talk); changed back to last version by Koedinger

==Brief statement of principle==
Instruction that combines or helps students' combine learning from examples and learning of or from rules tends to be more effective than instruction that includes the same examples and rules but does not help students combine them.

==Description of principle==
Example-rule coordination refers to a class of [[instructional method]]s that involve combining instructional [[example]]s with other forms of instruction including [[self-explanation]], problem-solving practice, [[analogical comparison]]. Coordination support may occur through explicit prompting for self-explanations, interleaving [[worked examples]] and problems, fading [[assistance]] from worked examples to problems.
===Operational definition===
===Examples===
Studies exploring various forms of example-rule coordination include: Butcher's [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)|integrated hints]] in Geometry, Booth's [[Booth |corrective self-explanation]] in Algebra, McLaren's [[Stoichiometry_Study | worked example interleaving]] in Chemistry, Eskenazi's [[REAP_main |vocabulary example personalization]] in English, Ringenberg's [[Ringenberg_Examples-as-Help |example-based help]] in Physics, Anthony's [[Effect of adding simple worked examples to problem-solving in algebra learning |worked example interleaving]] in Algebra, Noke's [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation |analogical comparison of examples]] in Physics, Renkl's [[Does learning from worked-out examples improve tutored problem solving? |example fading]] in Geometry.

==Experimental support==
See the pages listed in the examples section for studies providing experimental support. See also the references below.

The recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).

===Laboratory experiment support===

===In vivo experiment support===

==Theoretical rationale==
Combining examples and rules can enhance [[refinement]] toward better [[feature validity]] of [[knowledge components]]. For example, by prompting students to engage in [[self-explanation]] of an instructional example, students are more likely to try to express the more general rules inherent in the example and thus focus on the deep, relevant features rather than shallow, perceptual features that are irrelevant to correct application of the target [[knowledge component]].

Another way combining instructional examples and rules may enhance [[refinement]] is through [[self-supervised learning]] processes similar to [[co-training]] whereby a learner may draw on complementary strengths and weaknesses of learning by induction from instructional examples versus learning by comprehension of instructional text or rules. More specifically, a learner may identify and eliminate errors in induction from an instructional example by noticing an inconsistency with his or her comprehension of a given verbal rule. Or, conversely, a learner may identify and eliminate errors in comprehension of a rule by noticing an inconsistency with his or her induction (or analogical reasoning) from an example.

==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
== Variations (descendants) ==

* [[Worked example principle]]
* [[Prompted self-explanation hypothesis]]
** [[Corrective self-explanation]]
* [[Analogical comparison principle]]

==Generalizations (ascendants)==
[[Coordinative Learning]]

==References==
* Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In Proceedings of Eleventh Annual Conference on Computational Learning Theory (COLT), (pp. 92–100). New York: ACM Press. Available: citeseer.nj.nec.com/blum98combining.html
* Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT Press.
* Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93(2), 346–262.
* Rittle-Johnson, B., & Koedinger, K. R. (2002). Comparing instructional strategies for integrating conceptual and procedural knowledge. Paper presented at the Psychology of Mathematics Education, National, Athens, GA.

References that need to be added:
#worked example references (Sweller, Renkl, etc.)
#specific papers on studies that combine example and rule instruction by Nisbett, Holyoak etc.
#self-explanation references (Chi etc.)
#analogical comparison refs (Gentner, Nokes, etc.)

[[Category:Glossary]]
[[Category:Instructional Principle]]

English

2011-08-31T15:41:23Z

Mbett: Reverted edits by Pierrehernandez (Talk); changed back to last version by Koedinger

The English LearnLab course is described [http://learnlab.org/learnlabs/english/ here].

Numerous studies in the English LearnLab course can be found in the [[Coordinative Learning]], [[Interactive Communication]], and [[Refinement and Fluency]] research clusters.

Elaborated Explanations

2011-08-31T15:41:05Z

Mbett: Reverted edits by Kamearobinson (Talk); changed back to last version by Kirsten-Butcher

Elaborated explanations are [[Self-explanation]]s that make use of multiple sources of information or multiple modes of expression.

For example, an elaborated explanation in geometry requires students to explain their problem solving steps by referring both to verbal information (e.g., naming geometry principles that justify an answer) and visual information (e.g., naming the diagram features that are relevant to chosen geometry principles).
 
Relevant studies include:
[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)|Elaborated Explanations in Geometry (Aleven & Butcher)]]

[[Category:Glossary]]

[[Category:Interactive Communication]]

[[Category:Independent Variables]]

[[Category:Dependent Variables]]

[[Category:Visual-Verbal Learning (Aleven & Butcher Project)]]

Effect of adding simple worked examples to problem-solving in algebra learning

2011-08-31T15:40:17Z

Mbett: Reverted edits by Kamearobinson (Talk); changed back to last version by Gurpreet