EDUC545-010: Core Methods in Educational Data Mining

Spring 2017

Professor Ryan Baker

- 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]

- Baker, R.S. (2015) 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. A poster presented at the annual International Conference on Educational Data Mining (EDM)

- Baker, R.S. (2015) 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.
- 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]

- Baker, R.S. (2015) Big Data and Education. Ch. 1, V3, V4.
- Witten, I.H., Frank, E. (2011) Data Mining: Practical Machine Learning Tools and Techniques. Ch. 4.6, 6.1, 6.2, 6.4
- Hand, D. J. (2006). Classifier technology and the illusion of progress. Statistical science, 21(1), 1-14.[pdf]

- Baker, R.S. (2015) 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]

- Baker, R.S. (2015) 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]

- Baker, R.S. (2015) 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

- Baker, R.S. (2015) 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. (2014) Combining Expert and Metric-Based Assessments of Association Rule Interestingness.
*Proceedings of the 7th International Conference on Educational Data Mining*.[pdf]

- Baker, R.S. (2015) Big Data and Education. Ch. 5, V4.
- 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]
- 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]

- Baker, R.S. (2015) 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]

- Baker, R.S. (2015) 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]
- Khajah, M., Lindsey, R. V., & Mozer, M. C. (2016) How Deep is Knowledge Tracing? Proceedings of the International Conference on Educational Data Mining. [pdf]

- Baker, R.S. (2015) 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]

- Baker, R.S. (2015) 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]

- Baker, R.S. (2015) 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
- 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]

- Baker, R.S. (2015) Big Data and Education. Ch. 8, V5.