Ce diaporama a bien été signalé.
Le téléchargement de votre SlideShare est en cours. ×

Where is Theory in Learning Analytics


Consultez-les par la suite

1 sur 34 Publicité

Plus De Contenu Connexe

Diaporamas pour vous (20)

Similaire à Where is Theory in Learning Analytics (20)


Plus récents (20)


Where is Theory in Learning Analytics

  1. 1. Where is Theory in Learning Analytics? Srecko Joksimovic, University of South Australia @s_joksimovic
  2. 2. Overview • History & Drivers • Data • Theory • Overview of Relevant Studies • SRL • Engagement • Social Interactions
  3. 3. Context Exploring human and artificial cognition to understand knowledge processes and their impact on society Centre for Change and Complexity in Learning
  4. 4. History & Drivers
  5. 5. The First LAK Conference A Brief History How everything started… 2011 The First Handbook of LA2017
  6. 6. Pursuit for Personalized and Adaptive Learning
  7. 7. How can we extract value from these big sets of data?
  8. 8. Education is no different Substantial investments in analytics Ease access to learner data Increased adoption of personal technologies
  9. 9. Data
  10. 10. Data We Collect
  11. 11. No semantic or causal analysis is required Correlation is enough? Big data's impact on digital marketing landscape The Power of Data Data “All models are wrong, but some are useful.” - George E. P. Box It’s not just quantity – quality matters too
  12. 12. “What counts as a meaningful finding when the number of data points is so large that something will always be significant?” Data in Education Validity Generalizability Importance of feedback Privacy, Ethics What is missing? Wise and Shaffer (2015), pp.6
  13. 13. Theory
  14. 14. 01 02 03 What do learning sciences have to do with learning analytics? Paul Kirschner Available at https://goo.gl/5Uzf12 LAK'16 keynote - Just about everything!
  15. 15. our learning analytics are our pedagogy - Buckingham Shum (2012) - Knight et al (2014) Epistemology PedagogyAssessment
  16. 16. Designing for Learning (Goodyear and Carvalho, 2014)
  17. 17. 01 02 … with larger amounts of data, theory plays an ever-more critical role in analysis Theory matters… Wise and Shaffer (2015)
  18. 18. Adoption of theory Consolidated Model Contribute to theory Design Theory Data Science Collection Measurement Analysis Reporting Interaction & Visualization Learning Study
  19. 19. Self-Regulated Learning
  20. 20. Four-stage Model of SRL (Winne and Hadwin) A strong metacognitive perspective A Socio-cognitive Perspective of SRL Grounded by Three Models (Zimmerman) SRL model is organized in three phases: forethought, performance and self-reflection The Role of Motivation In SRL (Pintrich) The integration of motivational constructs in SRL Model of Adaptable Learning (Boekaerts) SRL is assumed to necessitate interaction between diverse (e.g. metacognitive, motivational and emotional) control systems Socially Shared Regulation (Järvelä and Hadwin) Collaboration poses cognitive, motivational, social, and environmental challenges Models of SRL (Panadero, 2017)
  21. 21. Winne and Hadwin’s (1998) model of SRL
  22. 22. Learning Analytics Dashboards Systematic Review - are rarely grounded in learning theory; - cannot be suggested to support metacognition; - do not offer any information about effective learning tactics and strategies; and - have significant limitations in how their evaluation is conducted and reported (Matcha, et al., 2019)
  23. 23. Technology Use Profiles - task-focused users, - content-focused no users, - no users, - highly intensive users, - content-focused intensive users, and - socially-focused intensive users (Kovanović, et al., 2015)
  24. 24. Engagement
  25. 25. Reschly and Christenson (2012, p.10)
  26. 26. (Joksimović, et al., 2019)
  27. 27. Operationalization of Engagement - Cognitive engagement, - Affective engagement, - Behavioural + Academic engagement (Fincham, et al., 2019)
  28. 28. Social Interactions
  29. 29. Social Network Analysis  Actions are viewed as interdependent  Ties as channels for flow of resources  Structural environment as opportunity or constraint  Structure (e.g., social, economic, political) as lasting patterns of relations among actors
  30. 30. Russo and Koesten (2005) prestige (in-degree) Cognitive learning outcomecentrality (out-degree) degree centrality Course grade Cho et al. (2007) closeness centrality betweenness centrality Jiang et al. (2014) degree centrality GPAcloseness centrality betweenness centrality eccentrality Gašević et al. (2013) degree centrality Course grade closeness centrality betweenness centrality degree centrality Course grade closeness centrality betweenness centrality Positive, statistically significant association Note: No statistically significant association Social Centrality and Learning Outcome Does higher social centrality imply more benefit? NOT NECESSARILY… (Krachardt, 1999)
  31. 31. Importance of Social Dynamics • Structural properties: degree, betweenness, closeness centrality Network • Reciprocity • Strength of ties • Homophily Social dynamics • Final course grade • Learning gain Learning outcome
  32. 32. Designing a Course with Learning Analytics in Mind Summary
  33. 33. Where is Theory in Learning Analytics? Srecko Joksimovic, University of South Australia @s_joksimovic
  34. 34. References Joksimović S., Kovanović V., & Dawson S.: “A Decade Later: The Journey of Learning Analytics”, HERDSA Review of Higher Education, 2019 Vol 6, pp 37-63; Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired magazine, 16(7), 16-07. Wise, A. F., & Shaffer, D. W. (2015). Why Theory Matters More than Ever in the Age of Big Data. Journal of Learning Analytics, 2(2), 5-13. https://doi.org/10.18608/jla.2015.22.2 Knight, S.; Buckingham Shum, S., & Littleton, K. (2013). Epistemology, pedagogy, assessment and learning analytics. In: Third Conference on Learning Analytics and Knowledge (LAK 2013), 8-12 Apr 2013, Leuven, Belgium, pp. 75–84. Gašević, D., Kovanović, V., & Joksimović, S. (2017) Piecing the learning analytics puzzle: a consolidated model of a field of research and practice, Learning: Research and Practice, 3:1, 63-78, DOI: 10.1080/23735082.2017.1286142 Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in psychology, 8, 422. Greene, J. A., & Azevedo, R. (2007). A theoretical review of Winne and Hadwin’s model of self-regulated learning: New perspectives and directions. Review of educational research, 77(3), 334-372. Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. Motivation and self-regulated learning: Theory, research, and applications, 2, 297- 314. Matcha, W., Ahmad Uzir, N., Gasevic and A. Pardo, "A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective," in IEEE Transactions on Learning Technologies. doi: 10.1109/TLT.2019.2916802 Kovanović, V, Gašević, D, Joksimović, S, Hatala, M & Adesope, O 2015, 'Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in asynchronous online discussions’, Internet and Higher Education, vol. 27, pp. 74–89. https://doi.org/10.1016/j.iheduc.2015.06.002 Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evolution and future directions of the engagement construct. In Handbook of research on student engagement (pp. 3-19). Springer, Boston, MA. Joksimović, S., Poquet, O., Kovanović, V., Dowell, N., Mills, C., Gašević, D., ... & Brooks, C. (2018). How do we model learning at scale? A systematic review of research on MOOCs. Review of Educational Research, 88(1), 43-86. Fincham, E., Whitelock-Wainwright, A., Kovanović, V., Joksimović,S., van Staalduinen, JP, and Gašević, D. 2019. Counting Clicks is Not Enough: Validating a Theorized Model of Engagement in Learning Analytics. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK19). ACM, New York, NY, USA, 501-510. DOI: https://doi-org.access.library.unisa.edu.au/10.1145/3303772.3303775 Joksimović, S., Manataki, A., Gašević, D., Dawson, S., Kovanović, V., and de Kereki, I.F. 2016. Translating network position into performance: importance of centrality in different network configurations. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York, NY, USA, 314-323. DOI: https://doi.org/10.1145/2883851.2883928