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.
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
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
32.
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
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