Can learning analytics transform the way that I teach?

1. Can learning analytics transform the way that I teach? George Siemens, PhD June 19, 2013

2. 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

3. 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

4. Historical influences

5. (Foundational) • Statistics • Mathematics • Artificial intelligence • Machine learning

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

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

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

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

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

11. 6. Knowledge discovery in databases Fayyad, 1996

12. 7. Adaptive hypermedia Brusilovsky, 2001

13. 8. Digital learning Elearning/online learning MOOCs

14. Related Business intelligence Academic analytics

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

16. 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

19. Scope of focus is important factor EDM: specific variables and factors LA: systemic and in-context factors (but this distinction is not hard; overlap and blurring is occurring)

20. Inter-disciplinary emphasis Bringing technical, pedagogical, and social domains into dialogue with each other.

21. Trends in LA

22. Mobile/wearable computing Increase the scope of data capture

23. Check my activity Predictive Analytics Reporting

25. Predictive analytics

26. Personalization and adaptation

27. Recommender systems

28. SNAPP

29. Rio Salado College Student Support Model Image source: EDUCAUSE

30. MOOCs & Analytics

31. Distributed content and conversations

32. Analytics around social interactions Analytics around learning content Analytics in different spaces (digital/F2F) Analytics on interaction with the learning system (university/k-12) Analytics on intervention and adaptation Assessment of analytics

33. How can LA help improve your teaching? Identify students who are at risk Develop student’s self-regulated learning skills Detail curricular elements that confuse students Recognize problem areas in content/instruction Adapt and personalize for learners

35. https://tekri.athabascau.ca/analytics/ Cooper, A. (2012a). What is analytics? Definitions and essential characteristics. JISC CETIS Analytics Series, 1(5). Retrieved on March 10, 2013 from http://publications.cetis.ac.uk/wp-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), 108-111. 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 http://ilpubs.stanford.edu:8090/422/ Milgram, S. (1967) ‘The small world problem.’ Psychology Today 2, 60-67. Granovetter, M. (1973) ‘The strength of weak ties.’ American Journal of Sociology,78(6), 1360-1380. 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, 285-401 Rich, E. (1979). User modeling via stereotypes. Cognitive Science 3, 329-354. Fischer, G. (2001). User Modeling in Human-Computer Interactions. User Modeling and User-Adapted Interaction, 11, 65-86. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167-207. 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 http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA207096#page=16 Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. American Association for Artificial Intelligence, 17(30), 37-54. Brusilovsky, P. (2001). Adaptive hypermedia: From intelligent tutoring systems to web-based education. User Modeling and User-Adapted Interaction, 11(1-2), 87-110. 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). http://www.educationaldatamining.org/JEDM/images/articles/vol1/issue1/JEDMVol1Issue1_BakerYacef.pdf 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 http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf

36. Twitter/Gmail: gsiemens