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State and Directions of Learning Analytics Adoption (Second edition)

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The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new field learning analytics and mobilized the education sector to embrace the use of data for decision-making. This talk will first introduce the field of learning analytics and touch on lessons learned from some well-known case studies. The talk will then identify critical challenges that require immediate attention in order for learning analytics to make a sustainable impact on learning, teaching, and decision making. The talk will conclude by discussing a set of milestones selected as critical for the maturation of the field of learning analytics. The most important take away from the talk will be that
- systemic approaches to the development and adoption of learning analytics are critical,
- multidisciplinary teams are necessary to unlock a full potential of learning analytics, and
- capacity development at institutional levels through the inclusion of diverse stakeholders is essential for full learning analytics adoption.

This is the second edition of the talk that previously gave under the same title on several occasions. The second edition reflects many developments happened in the field of learning analytics, especially those in the following two projects - http://he-analytics.com and http://sheilaproject.eu.

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State and Directions of Learning Analytics Adoption (Second edition)

  1. 1. State and Directions of Learning Analytics Adoption (Second edition) Dragan Gašević @dgasevic March 21, 2017 ISoTL, UBC Vancouver, BC, Canada
  2. 2. Educational Landscape Today Growing need for education Active learning Education defunding
  3. 3. Feedback loops between students and instructors are missing/weak!
  4. 4. LEARNING ANALYTICS
  5. 5. Learning environment Educators Learners Student Information Systems
  6. 6. Blogs Videos/slides Mobile Search Educators Learners Networks Student Information Systems Learning environment
  7. 7. Blogs Mobile Search Networks Educators Learners Student Information Systems Learning environment Videos/slides
  8. 8. CASE STUDIES
  9. 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. 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. 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.
  12. 12. INSTITUTIONAL ADOPTION: CURRENT STATE
  13. 13. Current state – Oz and Europe http://sheilaproject.eu/http://he-analytics.com
  14. 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. 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. 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. 17. Adoption challenge Leadership for strategic implementation & monitoring
  18. 18. Lack of leadership Bought an analytics product. Analytics box ticked!
  19. 19. Leadership challenge
  20. 20. Leadership challenge
  21. 21. Adoption challenge Equal engagement with different stakeholders
  22. 22. Adoption challenge Training opportunities to use learning analytics
  23. 23. Adoption challenge Policies for learning analytics practice
  24. 24. What’s necessary to move forward?
  25. 25. DIRECTIONS
  26. 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. 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. 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. 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. 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. 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
  32. 32. 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
  33. 33. 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.
  34. 34. One size fits all does not work in learning analytics
  35. 35. 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
  36. 36. 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
  37. 37. 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
  38. 38. 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.
  39. 39. 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.
  40. 40. 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.
  41. 41. 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.
  42. 42. 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
  43. 43. 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).
  44. 44. 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
  45. 45. 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
  46. 46. 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
  47. 47. 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.
  48. 48. 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.
  49. 49. 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
  50. 50. Marr, B. (Oct 2015). Forget Data Scientists - Make Everyone Data Savvy, http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A337288
  51. 51. FINAL REMARKS
  52. 52. Rhetoric of simplistic technological fixes is unproductive
  53. 53. Embracing complexity of educational systems
  54. 54. Capacity development Multidisciplinary teams in institutions critical
  55. 55. 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.
  56. 56. 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
  57. 57. 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. 58. Thank you!

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