Course Schedule
EDUC691: Core Methods in Educational Data Mining
Spring 2019
Professor Ryan Baker

Class 1: Introduction

• 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. (2014) Educational data mining and learning analytics. In Sawyer, K. (Ed.) Cambridge Handbook of the Learning Sciences: 2nd Edition, pp. 253-274. [pdf]

Class 2: Regression in Prediction

• Baker, R.S. (2018) Big Data and Education. Ch. 1, V2.
• Pardos, Z.A., Baker, R.S., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M. (2014) Affective states and state tests: Investigating how affect and engagement during the school year predict end of year learning outcomes. Journal of Learning Analytics, 1 (1), 107-128[pdf]

Class 3: Classification in Prediction

• Baker, R.S. (2018) Big Data and Education. Ch. 1, V3, V4.
• Hand, D. J. (2006). Classifier technology and the illusion of progress. Statistical science, 21(1), 1-14.[pdf]

Class 4: Behavior and Affect Detection

• Baker, R.S. (2018) Big Data and Education. Ch.1, V5. Ch. 3, V1, V2.
• Sao Pedro, M.A., Baker, R.S.J.d., Gobert, J., Montalvo, O. Nakama, A. (2013) Leveraging Machine-Learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill. User Modeling and User-Adapted Interaction, 23 (1), 1-39. [pdf]
• Kai, S., Paquette, L., Baker, R.S., Bosch, N., D'Mello, S., Ocumpaugh, J., Shute, V., Ventura, M. (2015) A Comparison of Face-based and Interaction-based Affect Detectors in Physics Playground. Proceedings of the 8th International Conference on Educational Data Mining, 77-84. [pdf]

Snow day -- no class

Class 5: Diagnostic Metrics

• Baker, R.S. (2018) 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]
• Knowles, J. E. (2014). Of needles and haystacks: Building an accurate statewide dropout early warning system in Wisconsin. Madison, WI: Wisconsin Department of Public Instruction. [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]

Spring Break -- no class

Class 6: Feature Engineering and Distillation

• Baker, R.S. (2018) Big Data and Education. Ch. 3, V3, V4, V5.
• 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]
• vlookup Tutorial 1
• vlookup Tutorial 2
• Pivot Table Tutorial 1
• Pivot Table Tutorial 2

Class 7: Clustering

• Baker, R.S. (2018) Big Data and Education. Ch. 7, 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]

Class 8: Association Rule Mining and Sequential Pattern Mining

• Baker, R.S. (2018) Big Data and Education. Ch. 5, V3, V4.
• 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]
• Bazaldua, D.A.L., Baker, R.S., San Pedro, M.O.Z. (2014) Combining Expert and Metric-Based Assessments of Association Rule Interestingness. Proceedings of the 7th International Conference on Educational Data Mining.[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]

Class 9: Bayesian Knowledge Tracing

• Baker, R.S. (2018) Big Data and Education. Ch. 4, V1, V2.
• 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]
• Baker, R.S.J.d., Corbett, A.T., 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, 406-415.[pdf]
• 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.[pdf]

Class 10: Performance Factors Analysis and Deep Knowledge Tracing

• Baker, R.S. (2018) Big Data and Education. Ch. 4, V3, V6.
• Pavlik, P.I., Cen, H., Koedinger, K.R. (2009) Performance Factors Analysis -- A New Alternative to Knowledge Tracing. Proceedings of AIED2009.[pdf]
• Pavlik, P.I., 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.[pdf]
• Khajah, M., Lindsey, R. V., & Mozer, M. C. (2016) How Deep is Knowledge Tracing? Proceedings of the International Conference on Educational Data Mining. [pdf]
• 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]

Class 11: Knowledge Structure Discovery

• Baker, R.S. (2018) Big Data and Education. Ch. 7, 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]
• Cen, H., Koedinger, K., Junker, B. (2006) Learning Factors Analysis - A General Method for Cognitive Model Evaluation and Improvement. Proceedings of the International Conference on Intelligent Tutoring Systems, 164-175.[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]

Class 12: Correlation Mining and Causal Mining

• Baker, R.S. (2018) Big Data and Education. Ch. 5, V1, V2.
• Rai, D., Beck, J.E. (2011) Exploring user data from a game-like math tutor: a case study in causal modeling. Proceedings of the 4th International Conference on Educational Data Mining, 307-313.[pdf]
• Rau, M. A., Scheines, R. (2012) Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations. Proceedings of the 5th International Conference on Educational Data Mining, 110-117. [pdf]
• 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]

Class 13: Network Analysis

• Baker, R.S. (2018) Big Data and Education. Ch. 5, V5. Ch. 8, V2.
• Haythornthwaite, C. (2001) Exploring Multiplexity: Social Network Structures in a Computer-Supported Distance Learning Class. The Information Society: An International Journal, 17 (3), 211-226.[pdf]
• Dawson, S. (2008) A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3), 224-238.[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]

Class 14: Final Presentations

• Baker, R.S. (2018) Big Data and Education. Ch. 8, V5.