Course Schedule
HUDK4050: Core Methods in Educational Data Mining
Fall 2014
Professor Ryan Baker

Wednesday, September 3

Monday, September 8: 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. (in press) Educational data mining and learning analytics. To appear in Sawyer, K. (Ed.) Cambridge Handbook of the Learning Sciences: 2nd Edition. [pdf]

Wednesday, September 10: CLASS CANCELED DUE TO INSTRUCTOR ILLNESS

Monday, September 15: Regression in Prediction

• Baker, R.S. (2014) Big Data and Education. Ch. 1, V2.
• Witten, I.H., Frank, E. (2011) Data Mining: Practical Machine Learning Tools and Techniques. Sections 4.6, 6.5.

Wednesday, September 17: Classification Algorithms

• Baker, R.S. (2014) Big Data and Education. Ch. 1, V3, V4, V5.
• Witten, I.H., Frank, E. (2011) Data Mining: Practical Machine Learning Tools and Techniques. Ch. 4.6, 6.1, 6.2, 6.4

Monday, September 22: Behavior Detection

• Baker, R.S. (2014) Big Data and Education. Ch.1, V6. Ch. 3, V1, V2.
• 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.[pdf]
• 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]

Wednesday, September 24: Diagnostic Metrics

• Baker, R.S. (2014) Big Data and Education. Ch. 2, V1, V2, V3, V4.
• Fogarty, J., Baker, R., Hudson, S. (2005) Case Studies in the use of ROC Curve Analysis for Sensor-Based Estimates in Human Computer Interaction. Proceedings of Graphics Interface (GI 2005), 129-136. [pdf]
• Russell, S., Norvig, P. (2010) Artificial Intelligence: A Modern Approach. Ch. 20: Learning Probabilistic Models.

Monday, September 29: No Class

Wednesday, October 1: No Class

Monday, October 6: Feature Engineering and Distillation-- What

• Baker, R.S. (2014) Big Data and Education. Ch. 3, V3.
• 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]

Wednesday, October 8: Feature Engineering and Distillation - How

• Baker, R.S. (2014) Big Data and Education. Ch. 3, V4, V5.
• vlookup Tutorial 1
• vlookup Tutorial 2
• Pivot Table Tutorial 1
• Pivot Table Tutorial 2

Monday, October 13: Advanced Detector Evaluation and Validation

• Baker, R.S. (2014) Big Data and Education. Ch. 2, V5, V6.
• Rosenthal, R., Rosnow, R.L. (1991) Essentials of Behavioral Research: Methods and Data Analysis, 2nd edition. Ch. 22: Meta-Analysis.
• Rupp, A.A., Gushta, M., Mislevy, R.J., Shaffer, D.W. (2010) Evidence-Centered Design of Epistemic Games: Measurement Principles for Complex Learning Environments. The Journal of Technology, Learning, and Assessment, 8 (4), 4-47.[pdf]

Wednesday, October 15: Bayesian Knowledge Tracing

• Baker, R.S. (2014) 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]

Monday, October 20: No Class

Wednesday, October 22: No Class

Monday, October 27: Performance Factors Analysis

• Baker, R.S. (2014) Big Data and Education. Ch. 4, V3.
• 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]

Wednesday, October 29: Advanced BKT

• Baker, R.S. (2014) Big Data and Education. Ch. 4, V5.
• Beck, J.E., Chang, K-m., Mostow, J., Corbett, A. (2008) Does Help Help? Introducing the Bayesian Evaluation and Assessment Methodology. Proceedings of the International Conference on Intelligent Tutoring Systems. [pdf]
• San Pedro, M.O.C., Baker, R., Rodrigo, M.M. (2011) Detecting Carelessness through Contextual Estimation of Slip Probabilities among Students Using an Intelligent Tutor for Mathematics. Proceedings of 15th International Conference on Artificial Intelligence in Education, 304-311.[pdf]

Monday, November 3: Knowledge Structure Discovery

• Baker, R.S. (2014) 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]
• Barnes, T. (2005) The Q-matrix Method: Mining Student Response Data for Knowledge. Proceedings of the Workshop on Educational Data Mining at the Annual Meeting of the American Association for Artificial Intelligence.[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]

Wednesday, November 5: Network Analysis

• Baker, R.S. (2014) Big Data and Education. Ch. 5, V5.
• 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
• Dawson, S. (2008) A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3), 224-238.[pdf]

Monday, November 10: No Class

Wednesday, November 12: Correlation Mining and Causal Mining

• Baker, R.S. (2014) Big Data and Education. Ch. 5, V1, V2.
• Arroyo, I., Woolf, B. (2005) Inferring learning and attitudes from a Bayesian Network of log file data. Proceedings of the 12th International Conference on Artificial Intelligence in Education, 33-40.[pdf]
• 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]

Monday, November 17: No Class

Wednesday, November 19: No Class

Monday, November 24: Discovery with Models

• Baker, R.S. (2014) Big Data and Education. Ch. 8, V1, 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]
• Hershkovitz, A., Baker, R.S.J.d., Gobert, J., Wixon, M., Sao Pedro, M. (2013) Discovery with Models: A Case Study on Carelessness in Computer-based Science Inquiry. American Behavioral Scientist, 57 (10), 1479-1498.[pdf]

Wednesday, November 26: No Class

Monday, December 1: Clustering and Factor Analysis

• Baker, R.S. (2014) Big Data and Education. Ch. 7, V1, V2, V3, V4, V5.
• Amershi, S. Conati, C. (2009) Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning Environments. Journal of Educational Data Mining, 1 (1), 18-71.[pdf]
• 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]

Wednesday, December 3: Association Rule Mining

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

Monday, December 8: Sequential Pattern Mining

• Baker, R.S. (2014) Big Data and Education. Ch. 5, V4.
• Srikant, R., Agrawal, R. (1996) Mining Sequential Patterns: Generalizations and Performance Improvements. Research Report: IBM Research Division. San Jose, CA: IBM. [pdf]
• Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaiane, O. (2009) Clustering and Sequential Pattern Mining of Online Collaborative Learning Data. IEEE Transactions on Knowledge and Data Engineering, 21, 759-772. [pdf]
• Shanabrook, D.H., Cooper, D.G., Woolf, B.P., Arroyo, I. (2010)Identifying High-Level Student Behavior Using Sequence-based Motif Discovery. Proceedings of the 3rd International Conference on Educational Data Mining, 191-200.[pdf]

Wednesday, December 10: Text Mining

1pm-2:40pm

Readings

• Baker, R.S. (2014) Big Data and Education. Ch. 8, V3.
• Rose, C.P. (2014) Online Text Mining resources TBD.
• Graesser, A. C., D'Mello, S. K., Craig, S. D., Witherspoon A., Sullins J., McDaniel B., Gholson, B. (2008) The Relationship between Affective States and Dialog Patterns during Interactions with AutoTutor. Journal of Interactive Learning Research, 19(2), 293-312. [pdf]