Additional Resources

Week 1

Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining, 1(1), 3-17.

Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. Intelligent Systems, IEEE, 24(2), 8-12.

Tansley, S., & Tolle, K. M. (Eds.). (2009). The fourth paradigm: data-intensive scientific discovery.

Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. Cambridge Handbook of the Learning Sciences.

Week 2

Shum, S. B., & Ferguson, R. (2012). Social Learning Analytics. Educational Technology & Society, 15(3), 3-26.

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist.

Duval, E. (2011, February). Attention please!: learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 9-17). ACM.

Week 3

Brandes, U. (2001). A Faster Algorithm for Betweenness Centrality. Journal of Mathematical Sociology, 25(2), 163-177. doi:10.1080/0022250X.2001.9990249 (full text)

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/P10008 (full text)

Burt, R. S., Kilduff, M., & Tasselli, S. (2013). Social network analysis: foundations and frontiers on advantage. Annual review of psychology, 64, 527-547. doi: 10.1146/annurev-psych-113011-143828 (full text)

Freeman, L. (1979). Centrality in social networks – Conceptual clarification. Social Networks, 1(3), 215-239. doi:10.1016/0378-8733(78)90021-7 (full text)

Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research. CBE-Life Sciences Education, 13(2), 167–178. doi:10.1187/cbe.13-08-0162 (full text)

Hanneman, R. A. & Riddle, M.  (2005). Introduction to social network methods.  Riverside, CA:  University of California, Riverside (full text).

Haythornthwaite, C. (1996). Social network analysis: An approach and set of techniques for the study of information exchange. Library and Information Science Research, 18(4), 323-342 (full paper).

Hirst, T. (2010, April 16). Getting Started With The Gephi Network Visualisation App – My Facebook Network, Part I, Retrieved October 18, 2014, from

Hirst, T. (2010, April 23). Getting Started With Gephi Network Visualisation App – My Facebook Network, Part II: Basic Filters I, Retrieved October 18, 2014, from

Hirst, T. (2010, May 10). Getting Started With Gephi Network Visualisation App – My Facebook Network, Part III: Ego Filters and Simple Network Stats, Retrieved October 18, 2014, from

Hirst, T. (2010, May 12). Getting Started With The Gephi Network Visualisation App – My Facebook Network, Retrieved October 18, 2014, from Part IV

Hirst, T. (2010, May 16). Getting Started With The Gephi Network Visualisation App – My Facebook Network, Part V, Retrieved October 18, 2014, from

Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577-8582 (full text).

Week 4

Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3), 224–238 (full text).

Dawson, S., Tan, J. P. L., & McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners’ creative capacity. Australasian Journal of Educational Technology27(6), 924-942 (full text).

De Laat, M., Lally, V., Lipponen, L., & Simons, R. J. (2007). Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis. International Journal of Computer-Supported Collaborative Learning, 2(1), 87-103. doi: 10.1007/s11412-007-9006-4 (full text).

Gašević, D., Dawson, S., Siemens, G. (2015). Let’s not forget: Learning analytics are about learning.
TechTrends (in press),

Gašević, D., Zouaq, A., Jenzen, R. (2013). Choose your Classmates, your GPA is at Stake!’ The Association of Cross-Class Social Ties and Academic Performance. American Behavioral Scientist, 57(10), 1459-1478. doi: 10.1177/0002764213479362 (full text).

Kovanović, V., Joksimović, S., Gašević, D., Hatala, M., “What is the source of social capital? The association between social network position and social presence in communities of inquiry,” In Proceedings of 7th International Conference on Educational Data Mining – Workshops, London, UK, 2014 (full text).

Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439-1459, doi:10.1177/0002764213479367 (full text).

Skrypnyk, O., Joksimović, S. Kovanović, V., Gasevic, D., Dawson, S. (2014). Roles of course facilitators, learners, and technology in the flow of information of a cMOOC. British Journal of Educational Technology (submitted) (full text).

Weeks 5 & 6

San Pedro, M.O.Z., Baker, R.S.J.d., Bowers, A.J., Heffernan, N.T. (2013) Predicting College Enrollment from Student Interaction with a Intelligent Tutoring System in Middle School. Proceedings of the 6th International Conference on Educational Data Mining, 177-184.

Aleven, V., McLaren, B., Roll, I., & Koedinger, K. (2006). Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence and Education, 16, 101-128.

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.

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.

Weeks 7 & 8

Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, submitted to the International Journal of Computer Supported Collaborative Learning 3(3), pp237-271.

Gweon, G., Jain, M., Mc Donough, J., Raj, B., Rosé, C. P. (2013). Measuring Prevalence of Other-Oriented Transactive Contributions Using an Automated Measure of Speech Style Accommodation, International Journal of Computer Supported Collaborative Learning 8(2), pp 245-265.

Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.

Week 9

Siemens, G., & d Baker, R. S. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.

Siemens, G. (2012, April). Learning analytics: envisioning a research discipline and a domain of practice. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 4-8). ACM.

Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Shum, S. B., Ferguson, R., … & Baker, R. S. J. D. (2011). Open Learning Analytics: an integrated & modularized platform. Proposal to design, implement and evaluate an open platform to integrate heterogeneous learning analytics techniques.

Week 10 and Beyond

Corbett, A.T., Anderson, J.R. (1995) Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction, 4, 253-278.

Beck, J.E., Chang, K-m., Mostow, J., Corbett, A. (2008) Does Help Help? Introducing the Bayesian Evaluation and Assessment MethodologyProceedings of the International Conference on Intelligent Tutoring Systems.

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.

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.

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.

Baker, R.S. (2013) Big Data and Education.