State and Directions of Learning Analytics Adoption
(Second edition)
Dragan Gašević@dgasevic
March 21, 2017ISoTL, UBCVancouver, BC, Canada
Blogs
Videos/slides
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Educators
Learners
Networks
Student Information
Systems
Learning environment
Blogs
Mobile
Search
Networks
Educators
LearnersStudent
Information Systems
Learning environment
Videos/slides
Student retention
Year 1 Year 2 Year 3 Year 40.00%
10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%
100.00%
Course SignalsNo 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).
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?
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.
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).
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
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
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
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
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.
Data – Model – Transformation
Creative data sourcingNecessary 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
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.
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
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
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.
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
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
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
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.
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.
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.
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.
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
What do students want?
Representation on committeesStudent 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).
Expert’s perspective to LA policyimportance 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
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
Data – Model – Transformation
Inclusive approaches to adoptionAnalytics 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
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.
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.
Data – Model – Transformation
Participatory design of analytics toolsAnalytics tools for non-statistics expertsDevelop 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
Marr, B. (Oct 2015). Forget Data Scientists - Make Everyone Data Savvy, http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A337288
Ethical and privacy considerationDevelopment 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.
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
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