Course Schedule
HUDK5199: Special Topics in Educational Data Mining
Spring 2013
Professor Ryan S.J.d. Baker

Wednesday, January 23: 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]

Monday, January 28: Bayesian Knowledge Tracing

• 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]

Wednesday, January 30: Educational Databases

• Koedinger, K.R., Baker, R.S.J.d., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J. (2010) A Data Repository for the EDM community: The PSLC DataShop. Handbook of Educational Data Mining. Boca Raton, FL: CRC Press, pp. 43-56.[pdf]

Monday, February 4: Performance Factors Analysis

• 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, February 6: Diagnostic Metrics

• 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, February 11: Advanced BKT

• 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]
• Pardos, Z.A., Heffernan, N.T. (2010) Modeling individualization in a bayesian networks implementation of knowledge tracing. Proceedings of User Modeling and Adaptive Personalization.[pdf]

Wednesday, February 13: Knowledge Structure Discovery

• 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]

Monday, February 18: Classification Algorithms

• Witten, I.H., Frank, E. (2011) Data Mining: Practical Machine Learning Tools and Techniques. Ch. 4.6, 6.1, 6.2, 6.4

Wednesday, February 20: Behavior Detection

• 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]

Monday, February 25: Feature Engineering and Distillation-- What

• 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, February 27:Feature Engineering and Distillation - How

• Google Refine User Guide (skim through this)
• Pivot Table Tutorial 1
• Pivot Table Tutorial 2
• vlookup Tutorial
• Witten, I.H., Frank, E. (2011) Data Mining: Practical Machine Learning Tools and Techniques. Ch. 7.

Monday, March 4: Reinforcement Learning and POMDPs

• Chi, M., VanLehn, K, Litman, D. & Jordan, P. (2011). Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical tactics. User Modeling and User Adapted Instruction, 21 (1-2), 137-180.[pdf]
• Folsom-Kovarik, J., Sukthankar, G., Schatz, S. (2012) Integrating Learner Help Requests Using a POMDP in an Adaptive Training System. Proceedings of the 24th Innovative Applications of Artificial Intelligence Conference, 2287-2292. [pdf]

Wednesday, March 6: Advanced Detector Evaluation and Validation

• 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]

Monday, March 11: Regression in Prediction

• Witten, I.H., Frank, E. (2011) Data Mining: Practical Machine Learning Tools and Techniques. Sections 4.6, 6.5.

Wednesday, March 13: Imputation in Prediction

• Schafer, J.L., Graham, J.W. (2002) Missing Data: Our View of the State of the Art. Psychological Methods, 7 (2), 147-177

Monday, March 18 SPRING BREAK

Wednesday, March 20 SPRING BREAK

Monday, March 25: Social Network Analysis

• 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]

Wednesday, March 27: Correlation Mining and Causal Mining

• 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, April 1: Discovery with Models

• Pardos, Z.A., Baker, R.S.J.d., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M. (in press) Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. To appear in Proceedings of the 3rd International Conference on Learning Analytics and Knowledge.
• Hershkovitz, A., Baker, R.S.J.d., Gobert, J., Wixon, M., Sao Pedro, M. (in press) Discovery with Models: A Case Study on Carelessness in Computer-based Science Inquiry. To appear in American Behavioral Scientist.

Wednesday, April 3: Factor Analysis

• Alpaydin, E. (2004) Introduction to Machine Learning. pp. 116-120.

Monday, April 8 CLASS CANCELLED DUE TO LAK CONFERENCE

Wednesday, April 10 CLASS CANCELLED DUE TO LAK CONFERENCE

Monday, April 15: Clustering

• Witten, I.H., Frank, E. (2011) Data Mining: Practical Machine Learning Tools and Techniques. Ch. 4.8, 6.6
• 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]

Wednesday, April 17: Association Rule Mining

• Witten, I.H., Frank, E. (2011) Data Mining: Practical Machine Learning Tools and Techniques. Ch. 4.5
• 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]

Monday, April 22 CLASS CANCELLED DUE TO CREA CONFERENCE

Wednesday, April 24: Sequential Pattern Mining

• 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]

Monday, April 29: Learnograms

NONE

Wednesday, May 1 CLASS CANCELLED DUE TO AERA CONFERENCE

Monday, May 6: Advanced Visualization of Educational Data

• Kay, J., Maisonneuve, N., Yacef, K., Reimann, P. (2006) The big five and visualisations of team work activity. Intelligent Tutoring Systems: Proceedings 8th International Conference, ITS 2006, 197-206.[pdf]
• Ritter, S., Harris, T., Nixon, T., Dickinson, D., Murray, R.C., Towle, B. (2009) Reducing the Knowledge Tracing Space. Proceedings of the 2nd International Conference on Educational Data Mining, 151-160.[pdf]
• Martinez, R., Kay, J., Yacef, K. (2011) Visualisations for longitudinal participation, contribution and progress of a collaborative task at the tabletop. International Conference on Computer Supported Collaborative Learning, CSCL 2011, 25-32.[pdf]

Wednesday, May 8: Collaborative Filtering and Recommender Systems

• Su, X., Khoshgoftaar, T.M. (2011) A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence. Article ID 421425. [pdf]
• Garcia, E., Romero, C., Ventura, S., Castro, C. (2009). An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, 19, 99-132. [pdf]

Monday, May 13: The World Is Changing

NONE