Background Readings

Essential reading, very highly recommended

• 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. link pdf

• Aleven, V., Borchers, C., Huang, Y., Nagashima, T., McLaren, B., Carvalho, P., Popescu, O., Sewall, J., & Koedinger, K. (2024). An integrated platform for studying learning with intelligent tutoring systems: CTAT+TutorShop. Proceedings of the Fifth Annual Workshop on Learning@Scale 2024: A/B Testing and Platform-Enabled Learning Research. link pdf

Optional

• Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2025). Instruction based on adaptive learning technologies. In R. E. Mayer, P. Alexander, & L. Fiorella (Eds.), Handbook of Research on Learning and Instruction (3rd ed., pp. 477-518). New York: Routledge. link pdf

• Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning sciences to the classroom. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (pp. 61-78). New York: Cambridge University Press. link pdf

Essential reading if you would like to learn to build rule-based Cognitive Tutors

This option is recommended for people with programming experience. During the summer school, we will provide some guidance as you decide.

• Aleven, V. (2010). Rule-based cognitive modeling for intelligent tutoring systems. In R. Nkambou, J. Bourdeau, & R. Mizoguchi (Eds.), Advances in Intelligent Tutoring Systems (pp. 33-62). Berlin: Springer. Note: Skip section 4. link pdf

• Aleven, V., Benson, M., Popescu, O., Schmalfeldt, T., Huang, Y., Yaron, D., King, E., Sutner, K., & Sewall, J. (in progress). Web-based authoring of model-tracing intelligent tutoring systems. pdf

• Nools reference pages. Just skim. Focus on use of the Nools DSL. Ignore flows and sessions. link

• JavaScript Model Tracer reference pages. link

Want to go deeper in the ITS Track?

If the ITS readings interest you, these optional certificate courses can help you continue from the research foundations of intelligent tutoring systems into the design, evaluation, and authoring of adaptive learning experiences.

Best next step: Adaptive Learning and Intelligent Tutoring Systems

Also relevant:

• Build Intelligent Tutors with CTAT, No Programming Required
• Rule-Based Cognitive Modeling for Intelligent Tutors
• Predictive Modeling and Knowledge Tracing for Adaptive Learning

• Cen, H., Koedinger, K., & Junker, B. (2006). Learning Factors Analysis: A general method for cognitive model evaluation and improvement. Proceedings of the 8th International Conference on Intelligent Tutoring Systems. pdf

• Koedinger, K., McLaughlin, E., & Stamper, J. (2012). Automated Student Model Improvement. In Proceedings of the 5th International Conference on Educational Data Mining (pp. 17-24). Chania, Greece. [BEST PAPER Award] pdf

• Stamper, J., & Koedinger, K. R. (2011). Human-machine Student Model Discovery and Improvement Using DataShop. In J. Kay, S. Bull, & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education (pp. 353-360). Berlin, Germany: Springer. pdf

• Eagle, M., & Barnes, T. (2014). Survival analysis on duration data in intelligent tutors. In International Conference on Intelligent Tutoring Systems (pp. 178-187). Springer. pdf

• Pardos, Z. A., & Dadu, A. (2018). dAFM: Fusing Psychometric and Connectionist Modeling for Q-matrix Refinement. Journal of Educational Data Mining, 10(2), 1-27. link pdf

• Wei, Y., Carvalho, P. F., & Stamper, J. (2025). KCluster: An LLM-based Clustering Approach to Knowledge Component Discovery. In C. Mills, G. Alexandron, D. Taibi, G. Lo Bosco, & L. Paquette (Eds.), Proceedings of the 18th International Conference on Educational Data Mining (pp. 228-240). International Educational Data Mining Society. link pdf

• Moore, S., Schmucker, R., Mitchell, T., & Stamper, J. (2024). Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions. arXiv preprint arXiv:2405.20526. link pdf

Want to go deeper in the Educational Data Mining Track?

If the EDM readings interest you, these optional certificate courses can help you build practical skills in learning analytics, educational data mining, learning curves, predictive modeling, and knowledge tracing.

Best next step: Learning Analytics Foundations: Predicting Student Success

Also relevant:

• Learning Curves and Data Mining for Course Improvement
• Predictive Modeling and Knowledge Tracing for Adaptive Learning
• A/B Testing for Learning Design
• From Signals to Support: Detecting and Responding to Motivation

• Bier, N., Moore, S., & 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 2019). Tempe, AZ. pdf

• Bier, N., Lip, S., Strader, R., Thille, C., & Zimmaro, D. (2014). An Approach to Skill Mapping in Online Courses. Paper presented at Learning with MOOCs, Cambridge, MA. link

• 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 is important. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, United Kingdom. pdf

• 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. pdf

• Koedinger, K. R., Scheines, R., & Schaldenbrand, P. (2018). Is the Doer Effect Robust across Multiple Data Sets? International Educational Data Mining Society. pdf

• 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. pdf

• Thille, C., & Smith, J. (2011). Cold Rolled Steel and Knowledge: What Can Higher Education Learn About Productivity? Change: The Magazine of Higher Learning, 43, 21-27. DOI: 10.1080/00091383.2011.55698. link

Want to go deeper in the OLI Track?

If the OLI readings interest you, these optional certificate courses can help you apply learning science principles to online course design, assessment design, practice, feedback, and iterative improvement.

Best next step: Evidence-Based Backward Design for Online Learning

Also relevant:

• Active Learning: Practice and Feedback
• UX Design for Better Learning Experiences
• Uncovering Implicit Knowledge with Cognitive Task Analysis
• Segmenting and Pretraining for Online Learning

Basic introduction, especially if you are less familiar with CER

• Ko, A. J. Computing Education Research FAQ. This FAQ gives an overview of computing education research for those who do not come from a CER background. link

• Malmi, L., Sheard, J., Kinnunen, P., Simon, & Sinclair, J. (2019). Computing education theories: What are they and how are they used? In Proceedings of the 2019 ACM Conference on International Computing Education Research (pp. 187-197). This paper introduces how and why educational theories shape computing education research. link pdf

• Brown, N. C., & Wilson, G. (2018). Ten quick tips for teaching programming. PLOS Computational Biology, 14(4), e1006023. This is an example of how computing education research can inform teaching and practice by evaluating interventions, though it also includes opinions and interpretations of that research. link

SPLICE and Smart Learning Content

• Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., & Wollowski, M. (2014). Increasing Adoption of Smart Learning Content for Computer Science Education. In Proceedings of the Working Group Reports of the 2014 Innovation and Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, 31-57. This is an introduction to “smart” learning content, especially content that is interactive. link pdf

CER and LLMs

• Prather, J., Reeves, B., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J., & Briggs, B. (2024). The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers. link pdf

• Ma, Q., et al. (2024). What You Say = What You Want? Teaching Humans to Articulate Requirements for LLMs. link pdf

• Prather, J., Leinonen, J., Kiesler, N., Gorson Benario, J., Lau, S., MacNeil, S., Norouzi, N., Opel, S., Pettit, V., Porter, L., Reeves, B. N., Savelka, J., Smith IV, D. H., Strickroth, S., & Zingaro, D. (2024). Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools. Note: This is a more recent working group report and could replace the older report on the list. link pdf

• Smith IV, D. H., Denny, P., & Fowler, M. (2024). Prompting for Comprehension: Exploring the Intersection of Explain in Plain English Questions and Prompt Writing. link pdf

• Denny, P., Leinonen, J., Prather, J., Luxton-Reilly, A., Amarouche, T., Becker, B. A., & Reeves, B. N. (2024). Prompt Problems: A New Programming Exercise for the Generative AI Era. link pdf

CER and Educational Data Mining: CSEDM

• Shi, Y., Chi, M., Barnes, T., & Price, T. W. (2022). Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks. Proceedings of the 15th International Conference on Educational Data Mining. This is an example of integrating code analysis into classical student modeling approaches. link pdf

• Marwan, S., Akram, B., Barnes, T., & Price, T. W. (2022). Adaptive immediate feedback for block-based programming: Design and evaluation. IEEE Transactions on Learning Technologies, 15(3), 406-420. This paper is a case study of the design, evaluation, and evolution of an intervention in computing classrooms that leverages data to adapt to users. pdf

Want to go deeper in the Computing Education Research Track?

If the CER readings interest you, these optional certificate courses can help you connect computing education research to practical learning design, assessment design, smart learning content, adaptive support, and evaluation of educational interventions.

Best next step: Active Learning: Practice and Feedback

Also relevant:

• Adaptive Learning and Intelligent Tutoring Systems
• Learning Analytics Foundations: Predicting Student Success
• A/B Testing for Learning Design