Learning analytics are more than measurement
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This document discusses the importance of learning analytics in education, emphasizing its role in measuring, analyzing, and optimizing student learning and educational environments. It highlights the current challenges faced by institutions in adopting sophisticated learning analytics and the necessity of a data-informed decision-making culture. The document also suggests the need for a collaborative approach to analytics, addressing ethical considerations and supporting student success through better data utilization.
Learning analytics are more than measurement
- 1. Learning analytics are more than measurement Dragan Gašević @dgasevic April 15, 2015 Policies for Educational Data Mining and Learning Analytics Briefing Brussels, Belgium
- 3. Feedback loops between students and instructors are missing!
- 7. Learning Analytics – What? Measurement, collection, analysis, and reporting of data about learners and their contexts
- 8. Learning Analytics – Why? Understanding and optimising learning and the environments in which learning occurs
- 9. CASE STUDIES
- 10. 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).
- 11. Better educational attainment: – reducing school drop-out rates below 10% – at least 40% of 30-34–year-olds with third level education (or equivalent) http://ec.europa.eu/europe2020/europe-2020-in-a-nutshell/priorities/smart-growth/index_en.htm
- 12. 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?
- 13. 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.
- 14. 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.
- 15. Youth on the move: – equipping young people better for the job market – improving all levels of education and training (academic excellence, equal opportunities) http://ec.europa.eu/europe2020/europe-2020-in-a-nutshell/priorities/smart-growth/index_en.htm
- 16. Learning analytics is more than measurement
- 17. CHALLENGES
- 18. Very few institution-wide examples of adoption
- 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. 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
- 21. ~70% institutions in phase 1 305 institutions, 58% at stage 1, 20% at stage 2 Yanosky, R. (2009). Institutional data management in higher education. ECAR, EDUCAUSE Center for Applied Research. Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management information and technology in higher education (Vol. 8). Educause.
- 22. 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.
- 23. What’s necessary to move forward?
- 24. DIRECTIONS
- 25. 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
- 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. 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
- 29. Once size fits all does not work in learning analytics
- 30. 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
- 32. Current state Benchmarking learning analytics status, policy and practices for Australian universities
- 35. Solution-driven approach Bought an analytics product. Analytics box ticked!
- 36. Lack of data-informed decision making culture Manyika, J., et al. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, http://goo.gl/Lue3qs
- 37. Researchers not focused on scalability
- 38. FINAL REMARKS
- 39. Embracing complexity of educational systems
- 40. Macfadyen, L. P., Dawson, S., Pardo, A., & Gasevic, D. (2014). Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge. Research & Practice in Assessment, 9(2), 17-28. Rapid Outcome Mapping Approach (ROMA)
- 41. Multidisciplinary teams in institutions critical
- 42. Learning from the successful examples
- 43. An institutional learning analytics vision Tynan, B. & Buckingham Shum, S. (2013). Designing Systemic Learning Analytics at the Open University. SoLAR Open Symposium – Strategy & Policy for Systemic Learning Analytics. http://people.kmi.open.ac.uk/sbs/2013/10/designing-systemic-analytics-at-the-open-university
- 45. Complement national plans in relation to big data and identify specific requirements of the education section
- 46. Cross-institutional/national data sharing and collaboration Esp. relevant for student mobility
- 47. Ethical and privacy consideration
- 49. 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
- 50. 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
- 52. Thank you!