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!

4.
LEARNING ANALYTICS

5.
Learning
environment
Educators
Learners
Student
Information
Systems

6.
Blogs
Videos/slides
Mobile
Search
Educators
Learners
Networks
Student
Information
Systems
Learning
environment

7.
Blogs
Mobile
Search
Networks
Educators
Learners
Student
Information
Systems
Learning
environment
Videos/slides

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.

12.
INSTITUTIONAL ADOPTION:
CURRENT STATE

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!

19.
Leadership challenge

20.
Leadership challenge

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

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.
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.
One size fits all does not work in
learning analytics

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Marr, B. (Oct 2015). Forget Data Scientists - Make Everyone Data Savvy,
http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A337288

51.
FINAL REMARKS

52.
Rhetoric of
simplistic technological fixes
is unproductive

53.
Embracing complexity of
educational systems

54.
Capacity development
Multidisciplinary teams in
institutions critical

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.
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.
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.
Thank you!