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Introduction to Learning Analytics and Machine Learning: Understanding Learner Data

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17 diapositives · 307 Views · Aug 02, 2014

This introduction explores the field of Learning Analytics (LA) and its intersection with Machine Learning (ML). Learning Analytics involves measuring, collecting, analyzing, and reporting data about learners to enhance the learning experience and the environments in which it occurs. The text covers key concepts, historical influences, methods of analysis, and applications of Learning Analytics, including user modeling, trend analysis, and the personalization of educational experiences.

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

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

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

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

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

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

  10. 6. Knowledge discovery in databases Fayyad, 1996

  11. 7. Adaptive hypermedia Brusilovsky, 2001

  12. 8. Digital learning Elearning/online learning MOOCs

  13. Related Business intelligence Academic analytics

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

  15. 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

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

  17. 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