Learning Analytics: A short introduction Learning Analytics & Machine Learning March 25, 2014 #LAK14

Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs. LAK11 Conference

Analytics is the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data Cooper, 2012

Historical influences

1. Citation analysis Garfield, 1955 Page et al., 1999

2. Social network analysis Milgram, 1967 Granovetter, 1973 Wellman, 1999 Haythornthwaite, 2002

3. User modeling Rich, 1979 Fischer, 2001 (HCI)

4. Education/cognitive models Anderson et al., 1995

5. Tutors (intelligent) Burns, 1989 Anderson et al., 1995

6. Knowledge discovery in databases Fayyad, 1996

7. Adaptive hypermedia Brusilovsky, 2001

8. Digital learning Elearning/online learning MOOCs

Related Business intelligence Academic analytics

Technique: Baker and Yacef (2009) five primary areas of analysis: -Prediction -Clustering -Relationship mining -Distillation of data for human judgment -Discovery with models

Application: Bienkowski, Feng, and Means (2012) five areas of LA/EDM application: - Modeling user knowledge, behavior, and experience - Creating profiles of users - Modeling knowledge domains - Trend analysis - Personalization and adaptation

Why ML? Two main areas of promise for LA: Neuroscience ML (wearable (ambient) computing)

Cooper, A. (2012a). What is analytics? Definitions and essential characteristics. JISC CETIS Analytics Series, 1(5). Retrieved on March 10, 2013 from content/uploads/2012/11/What-is-Analytics-Vol1-No-5.pdf Garfield, E. (1955). Citation indexes for Science: A new dimension in documentation through association of ideas. Science, 122(3159), Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. Retrieved on March 10 from Milgram, S. (1967) ‘The small world problem.’ Psychology Today 2, Granovetter, M. (1973) ‘The strength of weak ties.’ American Journal of Sociology, 78(6), Wellman, B. (1999) Networks in the global village: life in contemporary communities. Boulder: Westview Press Haythornthwaite, C. (2002) ‘Strong, weak, and latent ties and the impact of new media.’ The Information Society 18, Rich, E. (1979). User modeling via stereotypes. Cognitive Science 3, Fischer, G. (2001). User Modeling in Human-Computer Interactions. User Modeling and User-Adapted Interaction, 11, Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), Burns, H. L. (1989). Foundations of intelligent tutoring systems: An introduction. In Richardson, J. J., & Polson, M. C. (Eds.), Proceedings of the Air Force Forum for Intelligent Tutoring Systems. Retrieved on March 10, 2013 from Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. American Association for Artificial Intelligence, 17(30), Brusilovsky, P. (2001). Adaptive hypermedia: From intelligent tutoring systems to web-based education. User Modeling and User-Adapted Interaction, 11(1-2), 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). Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics. U.S. Department of Education. Retrieved on March 10, 2013 from