Learning analytics are about learning
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  • http://www.census.gov/prod/2013pubs/acsbr11-14.pdf
  • Students generally have poor self-regulation skills:Weak metacomprehension – assessment of own knowledge – stop learning, when they don’t know enoughConfusion of the rate of learning - stop learning, when they don’t know enoughExternally-generated self-monitoring prompts – AzevedoWeak metacognitive awareness – inefficient study tactics used
  • Use of unreliable sources Poor querying skills
  • Students are asked to seek information about domains they do not have sufficient background knowledge They will stop seeking information even if the proper one hasn’t been found
  • Students are asked to seek information about domains they do not have sufficient background knowledge They will stop seeking information even if the proper one hasn’t been found
  • Word count: Triggering - 82.03 (55.00, 99.50)Exploration - 122.71(73.25, 149.50)Integration - 185.53 (115.00, 221.00)Resolution 291.24 (168.00, 338.00)

Learning analytics are about learning Presentation Transcript

  • 1. Learning Analytics is about Learning Dragan Gasevic @dgasevic
  • 2. Growing demand for education!
  • 3. Scalability is possible Low effect size of class-size John Hattie
  • 4. Delivery
  • 5. Delivery
  • 6. Scientific American, March 13, 2013 http://www.scientificamerican.com/article.cfm?id=massive-open-online-courses-transformhigher-education-and-science
  • 7. MOTIVATION
  • 8. Feedback loops between students and instructors are missing! Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81-112.
  • 9. Learners Registrations Educators Learning and Collaborating
  • 10. Learners Registrations Networks Mobile Search Educators Learning and Collaborating Networks Videos/slides
  • 11. Learners Registrations Networks Mobile Search Educators Learning and Collaborating Networks Videos/slides
  • 12. DANGER
  • 13. Predict-o-mania The same predictive models for everything and everyone
  • 14. Student diversity http://www.census.gov/prod/2013pubs/acsbr11-14.pdf
  • 15. Population Diversity 100% 90% 80% 70% ACCT 1 (n = 746) BIOL 1 (n = 220) 60% BIOL 2 (n = 657) 50% COMM 1 (n = 499) COMP 1 (n = 242) 40% ECON 1 (n = 661) 30% GRAP 1 (n = 192) MARK 1 (n = 723) 20% MATH 1 (n = 194) 10% 0% Females International students Other Living in nonlanguage at urban home Part time student Previously enrolled to a course Early access Did not access Late access
  • 16. LMS Functionality Diversity ACCT 1 Light Box Gallery Forum Course Resource Turn-it-in Assignment Book Quiz Feedback Map Virtual Classroom Lesson Glossary Chat X X X X X X X BIOL 1 X X X BIOL 2 X X X X X X X X X X COMM 1 COMP 1 ECON 1 X X X X X X X X X X X X X X X X X X GRAP 1 X X X MARK 1 MATH 1 X X X X X X X X X X X X X
  • 17. Predictive Power Diversity 100.00% 90.00% 80.00% 70.00% 60.00% Model 1 50.00% Moodle 40.00% Model 1 + Moodle 30.00% 20.00% 10.00% 0.00% All courses ACCT 1 together BIOL 1 BIOL 2 COMM 1 COMP 1 Model 1 – demographic and socio-economic variables * - not statistically significant ECON 1 * GRAP 1 MARK 1 MATH 1
  • 18. Retention is not the only challenge It is important, of course! But, where is learning?
  • 19. How do we enhance learning if the focus is on outcomes only?
  • 20. DIRECTION
  • 21. Learning Analytics – What? Measurement, collection, analysis, and reporting of data about learners and their contexts
  • 22. Learning Analytics – Why? Understanding and optimising learning and the environments in which learning occurs
  • 23. Modern Educational Psychology Human agency is central to learning Bandura, A. (1989). Human agency in social cognitive theory. American psychologist, 44(9), 1175-1184.
  • 24. Winne and Hadwin's model of self-regulated learning
  • 25. Knowledge society and knowledge economy
  • 26. Why does it matter?! Challenge Metacognitive skills Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-Regulated Learning: Beliefs, Techniques, and Illusions. Annual Review of Psychology, 64, 417-444. doi:10.1146/annurev-psych-113011-143823
  • 27. Why does it matter?! Challenge Information seeking skills Judd, T., & Kennedy, G. (2011). Expediency-based practice? Medical students’ reliance on Google and Wikipedia for biomedical inquiries. British Journal of Educational Technology, 42 (2), 351-360. doi:10.1111/j.1467-8535.2009.01019.x
  • 28. Why does it matter?! Challenge Sensemaking paradox Butcher, K. R., & Sumner, R. (2011). Self-Directed Learning and the Sensemaking Paradox. Human– Computer Interaction, 26(1-2), 123-159. doi:10.1080/07370024.2011.556552
  • 29. Why does it matter?! Challenge Asking questions and critical thinking Graesser, A. C., & Olde, B. (2003). How does one know whether a person understands a device? The quality of the questions the person asks when the device breaks down. Journal of Educational Psychology, 95(3), 524–536..
  • 30. Process and context focus for learning analytics needed to understand learning
  • 31. OPPORTUNITIES
  • 32. Learning Analytics Effects of learning context External conditions (e.g., instructional design)
  • 33. Cognitive presence the extent to which the participants in any particular configuration of a CoI are able to construct meaning via sustained communication Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical Thinking and Computer Conferencing: A Model and Tool to Assess Cognitive Presence. American Journal of Distance Education ,15(1), 7-23.
  • 34. Effect size of the moderator role on critical thinking Cohen’s d = 0.66
  • 35. Effect size of an intervention on critical thinking in online discussions d = 0.95 (non-moderators) and d = 0.61 (moderators)
  • 36. Cognitive presence TMA1 TMA2 TMA3 TMA4 Final Control group Cognitive Presence in Online Discussions – Association w/ Grades Triggering event Exploration Integration Resolution Other -.226 -.001 .128 .201 -.028 .005 .141 .060 .027 .078 -.046 .009 .034 -.023 .113 -.050 -.037 .043 -.054 .106 -.010 .048 .113 .074 .154 ** p < 0.01; * p < 0.05
  • 37. Cognitive Presence in Online Discussions – Association w/ Grades Intervention group Control group Cognitive presence ** p TMA1 TMA2 TMA3 TMA4 Final Triggering event Exploration Integration Resolution Other Triggering event Exploration -.226 -.001 .128 .201 -.028 .149 .216 .005 .141 .060 .027 .078 -.077 .197 -.046 .009 .034 -.023 .113 -.070 .163 -.050 -.037 .043 -.054 .106 .000 .223 -.010 .048 .113 .074 .154 .016 .243 Integration .156 .396** .417** .338* .454** Resolution Other -.041 .219 .060 .046 .154 .050 .083 .075 .129 .088 < 0.01; * p < 0.05
  • 38. Integration posts: effect on final grades 100 80 60 40 20 0 Q1 Q2 Q3 p < .001, Q1 vs. Q2; Q1 vs. Q3, Q1 vs. Q4 Q4
  • 39. Learning Analytics Are students only driven by assessments? Effects of external conditions
  • 40. Self-reflections in video annotations Course 1 (non-graded) Course 3 (graded) Course 2 (graded) Course 4 (non-graded)
  • 41. Self-reflections in video annotations 120.00 100.00 80.00 Course 1 (non-graded) Course 2a (graded) 60.00 Course 2b (graded) Course 3 (graded) 40.00 Course 4 (non-graded) 20.00 0.00 Annotation total Annotation postion Annotation postion Annotation postion Annotation postion Annotation general Q1 Q2 Q3 Q4
  • 42. Self-reflections in video annotations 1800 1600 1400 1200 Course 1 (non-graded) 1000 Course 2a (graded) Course 2b (graded) 800 Course 3 (graded) 600 Course 4 (non-graded) 400 200 0 Cognitive processes Perceptual processes Positive emotions Negative emotions Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24-54.
  • 43. Learning Analytics Effects of students’ own decisions Beyond external conditions
  • 44. Learner profiles – use of LMS Effect size .75 on critical thinking & academic success 3 4
  • 45. Learner profiles – use of LMS 14 12 10 Triggering 8 Exploration Integration 6 Resolution Other 4 2 0 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Effect size .75 on critical thinking and academic success
  • 46. CHALLENGES
  • 47. Learning Analytics What to measure? We don’t need page access counts only! Wilson, T.D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249 - 270, doi:10.1108/EUM0000000007145
  • 48. Instrumentation About specific contexts and constructs
  • 49. Instrumentation Capturing interventions Previous learning and (memory of) experience Social networks (e.g., communication, cross-class) Interaction types (e.g., transactional distances)
  • 50. Motivation in Information Interaction Zhou, M., & Winne, P. H. (2012). Modeling academic achievement by self-reported versus traced goal orientation. Learning and Instruction, 22(6), 413–419. doi:10.1016/j.learninstruc.2012.03.004
  • 51. Motivation in Information Interaction Achievement goal orientation (2x2) Zhou, M., & Winne, P. H. (2012). Modeling academic achievement by self-reported versus traced goal orientation. Learning and Instruction, 22(6), 413–419. doi:10.1016/j.learninstruc.2012.03.004
  • 52. Technology and process of self-regulated learning Siadaty, M. (2013). Semantic Web-Enabled Interventions to Support Workplace Learning, PhD Thesis, Simon Fraser University, Surrey, BC, Canada.
  • 53. Scaling up qualitative analysis
  • 54. Temporal processes beyond coding and counting
  • 55. Longitudinal studies
  • 56. Generating reports and nice visualization is not enough
  • 57. Building data-driven culture in institutions Manyika, J., et al., Big Data: The Next Frontier for Innovation, Competition, and Productivity, 2011, McKinsey Global Institute, http://goo.gl/Lue3qs
  • 58. Privacy and ethics
  • 59. Data sharing and mobility
  • 60. Thank you!