State and Directions of Learning Analytics Adoption (Second edition)
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The document discusses the current state and challenges of learning analytics adoption in higher education, emphasizing the need for effective leadership, stakeholder engagement, and training opportunities. It highlights the importance of integrating diverse data sources and customizing analytics to promote student success while addressing privacy concerns and the complexity of educational systems. Overall, it calls for a collaborative approach to implementing analytics to enhance learning outcomes and institutional practices.
State and Directions of Learning Analytics Adoption (Second edition)
- 1. State and Directions of Learning Analytics Adoption (Second edition) Dragan Gašević @dgasevic March 21, 2017 ISoTL, UBC Vancouver, BC, Canada
- 2. Educational Landscape Today Growing need for education Active learning Education defunding
- 3. Feedback loops between students and instructors are missing/weak!
- 8. CASE STUDIES
- 9. 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).
- 10. 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?
- 11. 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.
- 13. Current state – Oz and Europe http://sheilaproject.eu/http://he-analytics.com
- 14. Very few institution-wide examples of adoption Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education – challenges and policies: a review of eight learning analytics policies. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 233-242).
- 15. 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
- 16. 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
- 17. Adoption challenge Leadership for strategic implementation & monitoring
- 18. Lack of leadership Bought an analytics product. Analytics box ticked!
- 21. Adoption challenge Equal engagement with different stakeholders
- 22. Adoption challenge Training opportunities to use learning analytics
- 23. Adoption challenge Policies for learning analytics practice
- 24. What’s necessary to move forward?
- 25. DIRECTIONS
- 26. Data – Model – Transformation 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 – Transformation Creative data sourcing Gašević, D., Dawson, S., Pardo, A. (2016). How do we start? State and Directions of Learning Analytics Adoption. Oslo, Norway: International Council for Open and Distance Education. http://bit.ly/icde_la_16
- 28. 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.
- 29. Data – Model – Transformation Creative data sourcing Necessary IT support Gašević, D., Dawson, S., Pardo, A. (2016). How do we start? State and Directions of Learning Analytics Adoption. Oslo, Norway: International Council for Open and Distance Education. http://bit.ly/icde_la_16
- 30. Awareness of limitations and challenging assumptions Kovanović, V., Gašević, D., Dawson, S., Joksimović, S., Baker, R. (2015). Does Time-on-task Estimation Matter? Implications on Validity of Learning Analytics Findings. Journal of Learning Analytics, 2(3), 81-110.
- 31. Data – Model – Transformation Question-driven, not data-driven Gašević, D., Dawson, S., Pardo, A. (2016). How do we start? State and Directions of Learning Analytics Adoption. Oslo, Norway: International Council for Open and Distance Education. http://bit.ly/icde_la_16
- 33. Field of research and practice 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(2), in press. doi:10.1080/23735082.2017.1286142
- 34. 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.
- 35. One size fits all does not work in learning analytics
- 36. Gašević, D., Dawson, S., Rogers, T., Gašević, D. (2016). 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, 28, 68–84. Learning context Instructional conditions shape learning analytics results
- 37. Learner agency Jovanović, J., Gašević, D., Dawson, S., Pardo, A., & Mirriahi, N. (2017). Learning analytics to unveil learning strategies in a flipped classroom. The Internet and Higher Education, 33, 74-85. More time online does not always mean better learning
- 38. Data – Model – Transformation Gašević, D., Dawson, S., Pardo, A. (2016). How do we start? State and Directions of Learning Analytics Adoption. Oslo, Norway: International Council for Open and Distance Education. http://bit.ly/icde_la_16
- 39. Systemic Adoption Model Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office for Learning and Teaching.
- 40. Strategic capability Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office for Learning and Teaching.
- 41. Solution-focused Model Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office for Learning and Teaching.
- 42. Process-focused Model Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office for Learning and Teaching.
- 43. Data – Model – Transformation Inclusive approaches to adoption Gašević, D., Dawson, S., Pardo, A. (2016). How do we start? State and Directions of Learning Analytics Adoption. Oslo, Norway: International Council for Open and Distance Education. http://bit.ly/icde_la_16
- 44. What do students want? Representation on committees Student expectation of learning analytics Focus group interviews Whitelock-Wainwright, A., Gašević, D., & Tejeiro, R. (2017). What do students want?: towards an instrument for students' evaluation of quality of learning analytics services. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 368-372).
- 45. Expert’s perspective to LA policy importance ease privacy & transparency privacy & transparency risks & challenges risks & challenges roles & responsibilities (of all stakeholders) roles & responsibilities (of all stakeholders) objectives of LA (learner and teacher support) objectives of LA (learner and teacher support) data management data management research & data analysis research & data analysis 3.79 3.79 6.03 6.03 r = 0.66
- 46. Learning analytics purposes Quality, equity, personalized feedback, coping with scale, student experience, skills, and efficiency The University of Edinburgh (2017). Learning Analytics Policy, http://www.ed.ac.uk/academic-services/projects/learning-analytics-policy
- 47. Data – Model – Transformation Inclusive approaches to adoption Analytics tools for non-statistics experts Gašević, D., Dawson, S., Pardo, A. (2016). How do we start? State and Directions of Learning Analytics Adoption. Oslo, Norway: International Council for Open and Distance Education. http://bit.ly/icde_la_16
- 48. 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.
- 49. Students don’t perceive dashboards as feedback Pardo, A., Jovanovic, J. Dawson, S., Gasevic, D. (in preparation). Using Learning Analytics to Scale the Provision of Personalised Feedback.
- 50. Data – Model – Transformation Participatory design of analytics tools Analytics tools for non-statistics experts Develop capabilities to exploit (big) data Gašević, D., Dawson, S., Pardo, A. (2016). How do we start? State and Directions of Learning Analytics Adoption. Oslo, Norway: International Council for Open and Distance Education. http://bit.ly/icde_la_16
- 51. Marr, B. (Oct 2015). Forget Data Scientists - Make Everyone Data Savvy, http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A337288
- 52. FINAL REMARKS
- 53. Rhetoric of simplistic technological fixes is unproductive
- 54. Embracing complexity of educational systems
- 55. Capacity development Multidisciplinary teams in institutions critical
- 56. 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.
- 57. 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
- 58. 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
- 60. Thank you!