Course Schedule
HUDK4050: Core Methods in Educational Data Mining
Fall 2014
Professor Ryan Baker
Thursday, September 3: Introduction
1pm-2:40pm
Readings
- 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]
Slides: [pptx]
Assignments Due: NONE
Tuesday, September 8: Regression in Prediction
1pm-2:40pm
Readings
- Baker, R.S. (2015) Big Data and Education. Ch. 1, V2, RapidMiner Walkthrough.
- 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]
Class Data Set: [csv]
Class Code: [RapidMiner xml 1] [RapidMiner xml 2]
Slides: [pptx]
Assignments Due: NONE
Thursday, September 10: Classification Algorithms
1pm-2:40pm
Readings
- 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]
- Pardos, Z.A., Baker, R.S.J.d., Gowda, S.M., Heffernan, N.T. (2011) The Sum is Greater than the Parts: Ensembling Models of Student Knowledge in Educational Software. SIGKDD Explorations, 13 (2), 37-44.[pdf]
Slides: [pptx]
Assignments Due: Basic: Classifier
Tuesday, September 15: Behavior Detection
1pm-2:40pm
Readings
- Baker, R.S. (2015) Big Data and Education. Ch.1, V5. 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]
Slides: [pptx]
Assignments Due: Creative: Behavior Detection
Thursday, September 17: Diagnostic Metrics
1pm-2:40pm
Readings
- Baker, R.S. (2015) 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]
- 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]
Slides: [pptx]
Assignments Due: None
Tuesday, September 22: Feature Engineering and Distillation-- What
1pm-2:40pm
Readings
- Baker, R.S. (2015) 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]
Slides: [pptx]
Assignments Due: Basic: Metrics
Thursday, September 24: Feature Engineering and Distillation - How
1pm-2:40pm
Readings
Slides: [pptx]
Assignments Due: NONE
Tuesday, September 29: Advanced Detector Evaluation and Validation
1pm-2:40pm
Readings
- Baker, R.S. (2015) 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]
Slides: [pptx]
Assignments Due: Creative: Feature Engineering
Thursday, October 1: Bayesian Knowledge Tracing
1pm-2:40pm
Readings
- 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]
- Gweon, G. H., Lee, H. S., Dorsey, C., Tinker, R., Finzer, W., & Damelin, D. (2015). Tracking student progress in a game-like learning environment with a Monte Carlo Bayesian knowledge tracing model. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, 166-170.[pdf]
Class Data Set: [xlsx]
Slides: [pptx]
Assignments Due: Basic: BKT
Tuesday, October 6: Performance Factors Analysis
1pm-2:40pm
Readings
- 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]
Rollinson, J., & Brunskill, E. (2015). From Predictive Models to Instructional Policies. Proceedings of the International Conference on Educational Data Mining. [pdf]
Slides: [pptx]
Assignments Due: NONE
Thursday, October 8: No Class
Tuesday, October 13: No Class
Thursday, October 15: Advanced BKT
1pm-2:40pm
Readings
- Baker, R.S. (2015) 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]
Slides: [pptx]
Assignments Due: Basic: PFA
Tuesday, October 20: No Class
Thursday, October 22: No Class
Tuesday, October 27: Knowledge Structure Discovery
1pm-2:40pm
Readings
- 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]
- 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]
Class Data Set: [xlsx]
Slides: [pptx]
Assignments Due: Creative: Knowledge Structure
Thursday, October 29: Network Analysis
1pm-2:40pm
Readings
- 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]
Slides: [pptx]
Assignments Due: Basic: SNA
Tuesday, November 3: Correlation Mining and Causal Mining
1pm-2:40pm
Readings
- 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]
Slides: [pptx]
Assignments Due: None
Thursday, November 5: No Class
Tuesday, November 10: No Class
Thursday, November 12: Discovery with Models
1pm-2:40pm
Readings
- Baker, R.S. (2015) Big Data and Education. Ch. 8, V1, V2.
- 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]
- Fancsali, S. (2014). Causal Discovery with Models: Behavior, Affect, and Learning in Cognitive Tutor Algebra. Proceedings of the International Conference on Educational Data Mining 2014.[pdf]
Slides: [pdf]
Assignments Due: Basic: Correlation Mining
Tuesday, November 17: No Class Today
Thursday, November 19: Clustering and Factor Analysis
1pm-2:40pm
Readings
- Baker, R.S. (2015) 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]
Slides: [pptx]
Assignments Due: Basic: Clustering
Tuesday, November 24: Association Rule Mining
1pm-2:40pm
Readings
- 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]
Slides: [pptx]
Assignments Due: None
Thursday, November 26: No Class, Thanksgiving
Tuesday, December 1: Sequential Pattern Mining
1pm-2:40pm
Readings
- Baker, R.S. (2015) 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]
Slides: [pptx]
Assignments Due: Basic: Sequential Pattern Mining #1
Thursday, December 3: No Class
Tuesday, December 8: Text Mining
1pm-2:40pm
Readings
- Baker, R.S. (2015) Big Data and Education. Ch. 8, V3.
- 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]
- Adamson, D., Bharadwaj, A., Singh, A., Ashe, C., Yaron, D., & Rosé, C. P. (2014). Predicting Student Learning from Conversational Cues. Proceedings of the International Conference on Intelligent Tutoring Systems, 220-229. [pdf]
- Crossley, S., McNamara, D., Baker, R.S., Wang, Y., Paquette, L., Barnes, T., Bergner, Y. (2015) Language to Completion: Success in an Educational Data Mining Massive Open Online Course. Proceedings of the 8th International Conference on Educational Data Mining, 388-391.][pdf]
Slides: [pptx]
Assignments Due: Creative: Sequential Pattern Mining #2
Thursday, December 10: Visualization of Educational Data
1pm-2:40pm
Readings
- Baker, R.S. (2015) Big Data and Education. Ch. 6, V1, V2, V3, V4, V5.
- 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]
Slides: [pptx]
Assignments Due: None
Tuesday, December 15: Hidden Markov Models
1pm-2:40pm
Readings
- Boyer, K. E., Ha, E., Wallis, M. D., Phillips, R., Vouk, M. A., & Lester, J. C. (2009). Discovering Tutorial Dialogue Strategies with Hidden Markov Models. Proceedings of the International Conference on Artificial Intelligence and Education, 141-148.[pdf]
- Jeong, H., Biswas, G., Johnson, J., & Howard, L. (2010). Analysis of productive learning behaviors in a structured inquiry cycle using hidden Markov models. Proceedings of the International Conference on Educational Data Mining.[pdf]
Slides: [pptx]
Assignments Due: Creative: Visualization
Thursday, December 17: The World Is Changing
1pm-2:40pm
Readings
- Baker, R.S. (2015) Big Data and Education. Ch. 8, V5.
Assignments Due: Creative: Final Presentation [REQUIRED]