The following readings are recommended before you attend the LearnLab Summer School. To view the readings you will need to have Adobe Acrobat installed.
General Readings (for all tracks)
Koedinger, Kenneth R., Albert T. Corbett, and Charles Perfetti. “The
Knowledge‐Learning‐Instruction framework: Bridging the science‐practice
chasm to enhance robust student learning.” /Cognitive science/ 36.5
(2012): 757-798. [link:
https://onlinelibrary.wiley.co
Koedinger, K., Booth, J., and Klahr, D. (2013). Instructional Complexity and the Science to Constrain It Science 22 November 2013: 342 (6161), 935-937. [DOI:10.1126/science.1238056] [pdf]
Computational Models of Learning
Maclellan, C. J. (2017). Computational models of human learning: Applications for tutor development, behavior prediction, and theory testing. Doctoral Dissertation, Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburg, PA. pdf: https://chrismaclellan.com/
Weitekamp, D., Harpstead, E., & Koedinger, K. R. (2020). An Interaction Design for Machine Teaching to Develop AI Tutors. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-11). pdf: http://erikharpstead.net/
MacLellan, C.J., Harpstead, E., Patel, R., Koedinger, K.R. (2016). The Apprentice Learner Architecture: Closing the loop between learning theory and educational data. In Proceedings of the 9th International Conference on Educational Data Mining. Raleigh, NC: International Educational Data Mining Society. pdf: https://chrismaclellan.com/
Daniel Weitekamp III, Zihuiwen Ye, Napol Rachatasumrit, Erik Harpstead and Kenneth Koedinger. (2020). Investigating Differential Error Types between Human and Simulated Learners. In Proceedings of the 21st International Conference on Artificial Intelligence in Education – AIED ‘20, Iframe, Morocco. pdf: http://www.erikharpstead.net/
Koedinger, K.R., Matsuda, N., MacLellan, C.J., McLaughlin, E.A. (2015). Methods for Evaluating Simulated Learners: Examples from SimStudent. In J. Boticario & K. Muldner (Eds.), Proceedings of the Workshops at the 17th International Conference on Artificial Intelligence in Education AIED 2015 (Vol. 5, pp. 45-54). Aachen: CEUR-WS.org. pdf: https://chrismaclellan.com/
ITS
Aleven, V., McLaren, B. M., Sewall, J., van Velsen, M., Popescu, O., Demi, S., . . . Koedinger, K. R. (2016). Example-Tracing tutors: Intelligent tutor development for non-programmers. International Journal of Artificial Intelligence in Education, 26(1), 224-269. doi:10.1007/s40593-015-0088-2 [pdf]
Aleven, V., & Sewall, J. (2016). The frequency of tutor behaviors: A case study. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Proceedings of the 13th International Conference on Intelligent Tutoring Systems, ITS 2016 (pp. 396-401). Springer International Publishing. doi:10.1007/978-3-319-39583-8_47 [pdf]
Aleven, V., Sewall, J., Popescu, O., Ringenberg, M., van Velsen, M., & Demi, S. (2016). Embedding intelligent tutoring systems in MOOCs and e-learning platforms. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Proceedings of the 13th International Conference on Intelligent Tutoring Systems, ITS 2016 (pp. 409-415). Springer International Publishing. doi:10.1007/978-3-319-39583-8_49 [pdf]
Aleven, V. (2010). Rule-based cognitive modeling for intelligent tutoring systems. In R. Nkambou, J. Bourdeau, & R. Mizoguchi (Eds.), Advances in Intelligent Tutoring Systems. Berlin: Springer.[pdf]
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. [pdf]
Koedinger, K. R., Anderson, J.R., Hadley, W.H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30-43. Earlier version published inProceedings of the 7th World Conference on Artificial Intelligence in Education, (pp. 421-428). Charlottesville, VA: Association for the Advancement of Computing in Education. [pdf]
VanLehn, K. (2006) The behavior of tutoring systems. International Journal of Artificial Intelligence in Education. 16, 3, 227-265. [pdf]
Authoring GUI-based ITS
Aleven, V., McLaren, B. M., & Sewall, J. (2009). Scaling up programming by demonstration for intelligent tutoring systems development: An open-access website for middle-school mathematics learning. IEEE Transactions on Learning Technologies, 2(2), 64-78. [pdf]
Aleven, V., McLaren, B. M., Sewall, J., & Koedinger, K. R. (2009). A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education, 19(2), 105-154. [pdf]
Koedinger, K. R., Aleven, V., Heffernan, N., McLaren, B. M., and Hockenberry, M. 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). [pdf]
Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2005). Applying Programming by Demonstration in an Intelligent Authoring Tool for Cognitive Tutors. In AAAI Workshop on Human Comprehensible Machine Learning (Technical Report WS-05-04) (pp. 1-8). Menlo Park, CA: AAAI association. [pdf]
Matsuda, N., Cohen, W., Sewall, J., and Koedinger, K. (2006). What characterizes a better demonstration for cognitive modeling by demonstration? Technical report CMU-ML-06-106, School of Computer Science, Carnegie Mellon University. [pdf]
Authoring NL-based ITS
Jordan, Pamela; Ringenberg, Michael; Hall, Brian. Rapidly Developing Dialogue Systems that Support Learning Studies. Proceedings of ITS06 Workshop on Teaching with Robots, Agents, and NLP. 2006. [pdf]
Jordan P, Ros C and VanLehn, K. Tools for authoring tutorial dialogue knowledge. (2001) In J. D. Moore, C. L. Redfield & W. L. Johnson (Eds). AI in Education: AI-ED in the Wired and Wireless Future. Amsterdam: IOS Press (pp. 222-233). [pdf]
C. P. Ros and B. S. Hall (2004). A Little Goes a Long Way: Quick Authoring of Semantic Knowledge Sources for Interpretation. Proceedings of the Second International Workshop on Scalable Natural Language Understanding. [pdf]
Rose, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F. (In Press). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning , submitted to the International Journal of Computer Supported Collaborative Learning. [pdf]
Computer Supported Collaborative Learning
Cui, Y., Chaudhuri, S., Kumar, R., Gweon, G., Ros, C. P. (in press). Helping Agents in VMT, in G. Stahl (Ed.) Studying Virtual Math Teams, Springer CSCL Series, Springer.[pdf]
Kumar, R., Ros, C. P., Wang, Y. C., Joshi, M., Robinson, A. (2007). Tutorial Dialogue as Adaptive Collaborative Learning Support, Proceedings of Artificial Intelligence in Education. [pdf]
Wang, H. C., Ros, 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 for Scientific Inquiry, Proceedings of Computer Supported Collaborative Learning. [pdf]
Educational Data Mining (EDM)
Andrew Arnold, Joseph E. Beck and Richard Scheines (2006). “Feature Discovery in the Context of Educational Data Mining: An Inductive Approach.”Proceedings of the AAAI2006 Workshop on Educational Data Mining, Boston, MA, 7-13. [pdf]
Beck, J.E. (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), 21-28.[pdf]
Cen, Hao; Koedinger, Ken; Junker, Brian. Automating Cognitive Model Improvement by A*Search and Logistic Regression. Proceedings of AAAI 2005 Workshop on Educational Data Mining. 2005. [pdf]
Cen, Hao; Koedinger, Ken; Junker, Brian. Learning Factors Analysis – A General Method for Cognitive Model Evaluation and Improvement. the 8thInternational Conference on Intelligent Tutoring Systems. 2006. [pdf]
Donmez, P., Ros, C. P., Stegmann, K., Weinberger, A., & Fischer, F. (in press). Supporting CSCL with Automatic Corpus Analysis Technology. CSCL 2005. The next 10 years!. Taipei, Taiwan. [pdf]
Koedinger, K., McLaughlin, E., Stamper, J., Automated Student Model Improvement. In Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012). Chania, Greece. Jun 19-21, 2012. pp. 17-24. [BEST PAPER Award][pdf]
Stamper, J., Koedinger, K.R. (2011) Human-machine Student Model Discovery and Improvement Using DataShop. In Kay, J., Bull, S. and Biswas, G. eds. Proceeding of the 15th International Conference on Artificial Intelligence in Education (AIED2011). pp. 353-360. Berlin Germany:Springer. [pdf]
Building online courses with the Open Learning Initiative (OLI)
Bier, N., Moore., S. and Van Velsen, M. (2019). Instrumenting Courseware and Leveraging Data with the Open Learning Initiative (OLI). Companion Proceedings of the 9th International Learning Analytics and Knowledge Conference (LAK’19). Tempe, AZ.
Bier, N., Lip., S., Strader, R., Thille., C. & Zimmaro, D. (2014). An Approach to Skill Mapping in Online Courses. Paper presented at the Learning with MOOCs, Cambridge, MA.
Koedinger, K. R., McLaughlin, E. A., Jia, J. Z., & Bier, N. L. (2016). Is the doer effect a causal relationship?: how can we tell and why it’s important. Paper presented at the Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, United Kingdom.
Koedinger, K. R., Kim, J., Jia, J. Z., McLaughlin, E. A., & Bier, N. (2015). Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC. Proceedings of the Second ACM Conference on Learning@Scale (pp. 111-120): ACM.
Koedinger, K. R., Scheines, R., & Schaldenbrand, P. (2018). Is the Doer Effect Robust across Multiple Data Sets?. International Educational Data Mining Society.
Lovett, M., Meyer, O. & Thille, C. (2008) The Open Learning Initiative: Measuring the effectiveness of the OLI statistics course in accelerating student learning. Journal of Interactive Media in Education.
Thille, Candace & Smith, Joel. (2011). Cold Rolled Steel and Knowledge: What Can Higher Education Learn About Productivity?. Change: The Magazine of Higher Learning. 43. 21-27. 10.1080/00091383.2011.55698