Learning analytics are more than a technology
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Learning analytics aim to optimize learning through measurement, collection, analysis and reporting of student data. While interest is growing, few institutions have fully adopted analytics. Challenges include a lack of data-informed culture, focusing on solutions over research, and privacy concerns. Fully realizing analytics potential requires multidisciplinary teams, addressing complex educational systems, and developing an analytics-focused culture.
Learning analytics are more than a technology
- 1. Learning analytics are more than a technology Dragan Gašević @dgasevic October 20, 2015 ELearn 2015, Kona, HI
- 2. Educational Landscape Today “Non-traditional” students Large classrooms Long time to complete degrees Long waiting lists
- 4. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 201319030.
- 5. Feedback loops between students and instructors are missing/weak!
- 10. Learning Analytics – What? Measurement, collection, analysis, and reporting of data about learners and their contexts
- 11. Learning Analytics – Why? Understanding and optimising learning and the environments in which learning occurs
- 12. CASE STUDIES
- 13. Student retention 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Year 1 Year 2 Year 3 Year 4 Course Signals No Course Signals Arnold, K. E., & Pistilli, M. D. (2012, April). Course Signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267-270).
- 14. Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using Signals for appropriate feedback: Perceptions and practices. Computers & Education, 57(4), 2414-2422. Can teaching be improved?
- 15. Wright, M. C., McKay, T., Hershock, C., Miller, K., & Tritz, J. (2014). Better Than Expected: Using Learning Analytics to Promote Student Success in Gateway Science. Change: The Magazine of Higher Learning, 46(1), 28-34.
- 17. Very few institution-wide examples of adoption
- 18. Sophistication model Siemens, G., Dawson, S., & Lynch, G. (2014). Improving the Quality and Productivity of the Higher Education Sector - Policy and Strategy for Systems-Level Deployment of Learning Analytics. Canberra, Australia: Office of Learning and Teaching, Australian Government. Retrieved from http://solaresearch.org/Policy_Strategy_Analytics.pdf
- 19. Sophistication model Siemens, G., Dawson, S., & Lynch, G. (2014). Improving the Quality and Productivity of the Higher Education Sector - Policy and Strategy for Systems-Level Deployment of Learning Analytics. Canberra, Australia: Office of Learning and Teaching, Australian Government. Retrieved from http://solaresearch.org/Policy_Strategy_Analytics.pdf
- 20. Interest in analytics is high, but many institutions had yet to make progress beyond basic reporting Bichsel, J. (2012). Analytics in higher education: Benefits, barriers, progress, and recommendations. EDUCAUSE Center for Applied Research.
- 21. What’s necessary to move forward?
- 22. DIRECTIONS
- 23. Data – Model – Transform Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83, https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1
- 24. Data – Model – Transform Creative data sourcing Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83, https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1
- 25. Social networks are everywhere Gašević, D., Zouaq, A., Jenzen, R. (2013). ‘Choose your Classmates, your GPA is at Stake!’ The Association of Cross- Class Social Ties and Academic Performance. American Behavioral Scientist, 57(10), 1459–1478.
- 26. Data – Model – Transform Creative data sourcing Necessary IT support Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83, https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1
- 27. Awareness of limitations and challenging assumptions Kovanović, V., Gašević, D., Dawson, S., Joksimović, S., Baker, R. (in press). Does Time-on-task Estimation Matter? Implications on Validity of Learning Analytics Findings. Journal of Learning Analytics
- 28. Data – Model – Transform Question-driven, not data-driven Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83, https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1
- 30. Learning analytics is about learning Gašević, D., Dawson, S., Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
- 31. Once size fits all does not work in learning analytics
- 32. Gašević, D., Dawson, S., Rogers, T., Gašević, D. (in press). Learning analytics should not promote one size fits all: The effects of course-specific technology use in predicting academic success. The Internet and Higher Education. Learning context Instructional conditions shape learning analytics results
- 33. Learner agency Kovanović, V., Gašević, D., Joksimović, S., Hatala, M., Adesope, S. (2015). Analytics of Communities of Inquiry: Effects of Learning Technology Use on Cognitive Presence in Asynchronous Online Discussions. The Internet and Higher Education, 27, 74–89. More time online does not always mean better learning
- 34. Data – Model – Transform Participatory design of analytics tools Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83, https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1
- 35. Data – Model – Transform Participatory design of analytics tools Analytics tools for non-statistics experts Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83, https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1
- 36. Visualizations can be harmful Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In Proceedings of the ascilite 2014 conference (pp. 629-633). ascilite.
- 37. Data – Model – Transform Participatory design of analytics tools Analytics tools for non-statistics experts Develop capabilities to exploit (big) data Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83, https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1
- 38. Marr, B. (Oct 2015). Forget Data Scientists - Make Everyone Data Savvy, http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A337288
- 39. Are we ready to act on analytics?
- 40. What to do if we detect deficit models in our practice? Are we ready to act on analytics? Joksimović, S., Gašević, D., Loughin, T. M., Kovanović, V., Hatala, M. (2015). Learning at distance: Effects of interaction traces on academic achievement. Computers & Education, 87, 204–217
- 41. How do we deal with performance-oriented culture? Are we ready to act on analytics? Jovanović, J., Pardo, A., Gašević, D., Dawson, S., Mirriahi, N. (2015). Dynamic analytics of learning in flipped classrooms. Manuscript in preparation.
- 42. What’s our adoption reality?
- 43. CHALLENGES
- 44. Current state Benchmarking learning analytics status, policy and practices for Australian universities
- 47. Solution-driven approach Bought an analytics product. Analytics box ticked!
- 48. Lack of data-informed decision making culture Macfadyen, L., & Dawson, S. (2012). Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan. Educational Technology & Society, 15(3), 149-163.
- 49. Researchers not focused on scalability
- 50. LA idealized systems model Colvin, C., Rogers, T., Wade, A., Dawson, S., Gasevic, D., Buckingham Shum, S., Nelson, K., Alexander, S., Lockyer, L., Kennedy, G., Corrin, L., Fisher, J. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Australian Goverement’s Office for Learning and Teaching.
- 51. FINAL REMARKS
- 52. Embracing complexity of educational systems
- 53. Capacity development Multidisciplinary teams in institutions critical
- 54. Ethical and privacy consideration Development of data privacy agency Prinsloo, P., & Slade, S. (2015). Student privacy self-management: implications for learning analytics. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 83-92). ACM.
- 55. Sclater, N. (2014). Code of practice for learning analytics: A literature review of the ethical and legal issues. http://repository.jisc.ac.uk/5661/1/Learning_Analytics_A-_Literature_Review.pdf
- 56. Development of analytics culture Manyika, J. et al. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, http://goo.gl/Lue3qs
- 58. Mahalo!