Course Schedule
EDUC6191: Core Methods in Educational Data Mining
Fall 2024
Professor Ryan Baker

Class 1: Introduction

• Baker, R.S. (2023) Big Data and Education. Ch. 1, V1.
• 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. [pdf]
• Baker, R., Siemens, G. (2022) Educational data mining and learning analytics. In Sawyer, K. (Ed.) Cambridge Handbook of the Learning Sciences: 3rd Edition. [pdf]

Class 2: Prediction Modeling and Algorithmic Bias

• Baker, R.S. (2023) Big Data and Education. Ch. 1, V2, V3, V4, V6, V7. Ch. 2, V7.
• Deho, O. B., Joksimovic, S., Li, J., Zhan, C., Liu, J., & Liu, L. (2022). Should Learning Analytics Models Include Sensitive Attributes? Explaining the Why. IEEE Transactions on Learning Technologies. [pdf]
• Baker, R.S., Esbenshade, L., Vitale, J.M., Karumbaiah, S. (2023) Using Demographic Data as Predictor Variables: a Questionable Choice. Journal of Educational Data Mining. [pdf]
• Baker, R.S., Hawn, M.A. (2022) Algorithmic Bias in Education. International Journal of Artificial Intelligence and Education, 32, 1052-1092. [pdf]
• Cohausz, L., Kappenberger, J., & Stuckenschmidt, H. (2024). What fairness metrics can really tell you: A case study in the educational domain. Proceedings of the International Conference on Learning Analytics and Knowledge. [pdf]

Optional Session: An Inappropriately Brief Introduction to Frequentist Statistics

Class 3: Behavior and Affect Detection

• Baker, R.S. (2023) Big Data and Education. Ch.1, V5. Ch. 2, V5. Ch. 3, V1, V2, V7.
• Hutt, S., Grafsgaard, J. F., & D'Mello, S. K. (2019). Time to scale: Generalizable affect detection for tens of thousands of students across an entire school year. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-14).[pdf]
• Zhang, J., Andres, J.M.A.L., Hutt, S., Baker, R.S., Ocumpaugh, J., Mills, C., Brooks, J., Sethuraman, S., Young, T. (2022) Detecting SMART Model Cognitive Operations in Mathematical Problem-Solving Process. Proceedings of the International Conference on Educational Data Mining. [pdf]

Class 4: Diagnostic Metrics

• Baker, R.S. (2023) Big Data and Education. Ch. 2, V1, V2, V3, V4.
• Jeni, L. A., Cohn, J. F., & De La Torre, F. (2013). Facing Imbalanced Data--Recommendations for the Use of Performance Metrics. Proceedings of Affective Computing and Intelligent Interaction (ACII), 245-251.[pdf]
• Kitto, K., Shum, S. B., & Gibson, A. (2018). Embracing imperfection in learning analytics. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 451-460). ACM.[pdf]

Class 5: Feature Engineering and Tweaking Towards Optimality

• Baker, R.S. (2023) Big Data and Education. Ch. 3, V3, V4, V5, V6.
• Sao Pedro, M., Baker, R.S.J.d., Gobert, J. (2012) Improving Construct Validity Yields Better Models of Systematic Inquiry, Even with Less Information. Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization (UMAP 2012),249-260. [pdf]
• Slater, S., Baker, R. S., & Wang, Y. (2020). Iterative Feature Engineering through Text Replays of Model Errors. Proceedings of the International Conference on Educational Data Mining. [pdf]
• Cochran, K., Cohn, C., Rouet, J.F., Hastings, P. (2023). Improving Automated Evaluation of Student Text Responses Using GPT-3.5 for Text Data Augmentation [pdf]

Class 6: Network Analysis

• Baker, R.S. (2023) Big Data and Education. Ch. 5, V4, V5. Ch. 8, V2.
• Karimi, H., Knake, K.T., Frank, K.A. (2023) An Analysis of Diffusion of Teacher-curated Resources on Pinterest.[pdf]
• Gasevic, D., Zouaq, A., & Janzen, R. (2013) "Choose Your Classmates, Your GPA Is at Stake!": The Association of Cross-Class Social Ties and Academic Performance. American Behavioral Scientist [pdf]
• Pantic, N., Galey, S., Florian, L., Joksimovic, S., Viry, G., Gasevic, D., ... & Kyritsi, K. (2021). Making sense of teacher agency for change with social and epistemic network analysis. Journal of Educational Change, 1-33. [pdf]

Class 7: Bayesian Knowledge Tracing

• Baker, R.S. (2023) Big Data and Education. Ch. 4, V1, V2, V5; Ch. 6, V8.
• Corbett, A.T., Anderson, J.R. (1995) Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction, 4, 253-278. [pdf]
• Kang, J., Baker, R., Feng, Z., Na, C., Granville, P., Feldon, D.F. (2022) Detecting threshold concepts through Bayesian knowledge tracing: examining research skill development in biological sciences at the doctoral level. Instructional Science. [pdf]

Class 8: Logistic Knowledge Tracing

• Baker, R.S. (2023) Big Data and Education. Ch. 4, V3, V7.
• Pavlik, P.I., Cen, H., Koedinger, K.R. (2009) Performance Factors Analysis -- A New Alternative to Knowledge Tracing. Proceedings of AIED2009.[pdf]
• Choffin, B., Popineau, F., Bourda, Y., & Vie, J. J. (2019). DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills. Proceedings of the International Conference on Educational Data Mining (EDM 2019). [pdf]
• Pavlik, P. I., Eglington, L. G., & Harrell-Williams, L. M. (2021). Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling. IEEE Transactions on Learning Technologies, 14(5), 624-639. [pdf]

Class 9: Deep Knowledge Tracing

• Baker, R.S. (2023) Big Data and Education. Ch. 4, V6.
• Yeung, C. K., & Yeung, D. Y. (2018). Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale (p. 5-14). ACM.[pdf]
• Pandey, S., & Karypis, G. (2019). A Self-Attentive Model for Knowledge Tracing. Proceedings of the International Conference on Educational Data Mining. [pdf]
• Gervet, T., Koedinger, K., Schneider, J., & Mitchell, T. (2020). When is deep learning the best approach to knowledge tracing?. Journal of Educational Data Mining, 12(3), 31-54. [pdf]

Class 10: Association Rule Mining and Sequential Pattern Mining

• Baker, R.S. (2023) Big Data and Education. Ch. 5, V2, V3.
• Merceron, A., Yacef, K. (2008) Interestingness Measures for Association Rules in Educational Data. Proceedings of the 1st International Conference on Educational Data Mining,57-66. [pdf]
• Kinnebrew, J. S., Loretz, K. M., & Biswas, G. (2013). A contextualized, differential sequence mining method to derive students' learning behavior patterns. JEDM-Journal of Educational Data Mining, 5(1), 190-219.[pdf]
• Cukurova, M., Khan-Galaria, M., Millán, E., & Luckin, R. (2022). A Learning Analytics Approach to Monitoring the Quality of Online One-to-one Tutoring. Journal of Learning Analytics, 1-16. [pdf]

Class 11: Clustering

• Baker, R.S. (2023) Big Data and Education. Ch. 6, V1, V2, V3, V4, V5.
• Bowers, A.J. (2010) Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students: Grades, Data Driven Decision Making, Dropping Out and Hierarchical Cluster Analysis. Practical Assessment, Research & Evaluation (PARE), 15(7), 1-18. [pdf]
• Lee, J., Recker, M., Bowers, A.J., Yuan, M. (2016). Hierarchical Cluster Analysis Heatmaps and Pattern Analysis: An Approach for Visualizing Learning Management System Interaction Data. Poster presented at the annual International Conference on Educational Data Mining (EDM). [pdf]
• Shakya, A., Rus, V., Venugopal, D. (2023). Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data [pdf]

Class 12: Knowledge Structure Discovery

• Baker, R.S. (2023) Big Data and Education. Ch. 6, V6, V7.
• Desmarais, M.C., Meshkinfam, P., Gagnon, M. (2006) Learned Student Models with Item to Item Knowledge Structures. User Modeling and User-Adapted Interaction, 16, 5, 403-434.[pdf]
• Desmarais, M. C., & Naceur, R. (2013). A matrix factorization method for mapping items to skills and for enhancing expert-based Q-Matrices. Proceedings of the International Conference on Artificial Intelligence in Education, 441-450. [pdf]
• Koedinger, K.R., McLaughlin, E.A., Stamper, J.C. (2012) Automated Student Modeling Improvement. Proceedings of the 5th International Conference on Educational Data Mining, 17-24.[pdf]
• Chen, P., Lu, Y., Zheng, V. W., Chen, X., & Yang, B. (2018). KnowEdu: a system to construct knowledge graph for education. IEEE Access, 6, 31553-31563.[pdf]

Class 13: Correlation Mining

• Baker, R.S. (2023) Big Data and Education. Ch. 5, V1.
• Slater, S., Ocumpaugh, J., Baker, R., Scupelli, P., Inventado, P.S., Heffernan, N. (2016) Semantic Features of Math Problems: Relationships to Student Learning and Engagement. Proceedings of the 9th International Conference on Educational Data Mining, 223-230.[pdf]
• Matayoshi, J., & Karumbaiah, S. (2021). Investigating the Validity of Methods Used to Adjust for Multiple Comparisons in Educational Data Mining. Proceedings of the International Conference on Educational Data Mining. [pdf]

No Class Thanksgiving Break

Class 14: Large Language Models

• Baker, R.S. (2023) Big Data and Education. Ch. 7, V1, V2, V3, V4, V5.
• DAIR.AI (2023) Prompt Engineering Guide. [html]
• Nardo, C. (2023) The Waluigi Effect. [html]
• Barany, A., Nasiar, N., Porter, C., Zambrano, A.F., Andres, A., Bright, D., Choi, J., Gao, S., Giordano, C., Liu, X., Mehta, S., Shah, M., Zhang, J., Baker, R.S. (2024) ChatGPT for Education Research: Exploring the Potential of Large Language Models for Qualitative Codebook Development. Proceedings of the International Conference on Artificial Intelligence and Education. [pdf]

Class 15: Reinforcement Learning and RLHF

• Baker, R.S. (2023) Big Data and Education. Ch.8, V4. Ch.7, V6. Ch. 8, V6.
• Rafferty, A., Ying, H., & Williams, J. (2019). Statistical consequences of using multi-armed bandits to conduct adaptive educational experiments. Journal of Educational Data Mining, 11(1), 47-79. [pdf]
• Ju, S., Zhou, G., Barnes, T., & Chi, M. (2020). Pick the Moment: Identifying Critical Pedagogical Decisions Using Long-Short Term Rewards. Proceedings of the International Conference on Educational Data Mining. [pdf]
• Bassen, J., Balaji, B., Schaarschmidt, M., Thille, C., Painter, J., Zimmaro, D., ... & Mitchell, J. C. (2020). Reinforcement learning for the adaptive scheduling of educational activities. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-12). [pdf]