Current State and Trends in
Learning Analytics
Dragan Gašević
@dgasevic
January 22, 2016
Open University of Hong Kong
Hong Hong
Educational Landscape Today
“Non-traditional” students
Redefining the role of universities
Changing labor market
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 201319030.
Feedback loops between
students and instructors
Learning
environment
Educators
Learners
Student
Information
Systems
Blogs
Videos/slides
Mobile
Search
Educators
Learners
Networks
Student
Information
Systems
Learning
environment
Blogs
Mobile
Search
Networks
Educators
Learners
Student
Information
Systems
Learning
environment
Videos/slides
Learning Analytics – What?
Measurement, collection,
analysis, and reporting of data about
learners and their contexts
Learning Analytics – Why?
Understanding and optimising
learning and the environments
Pass/Fail, Retention
Concept understanding
Learning motivation/engagement
Learning strategy and metacognition
Learning dispositions
Graduate qualities
Learning experience
Satisfaction, community
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).
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.
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable devices as facilitators, not drivers, of health behavior change. The Journal of the American Medical Association, 313(5), 459-460.
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.
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.
INSTITUTIONAL ADOPTION:
CURRENT STATE
Very few institution-wide
examples of adoption
Stage 1: Extraction and reporting of transaction-level data
Stage 2: Analysis and monitoring of operational performance
Stage 3: “What-if” decision support (such as scenario building)
Stage 4: Predictive modeling & simulation
Stage 5: Automatic triggers and alerts (interventions)
Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management information and technology in higher education (Vol. 8). Educause.
~70% institutions in phase 1
305 institutions, 58% at stage 1, 20% at stage 2
Yanosky, R. (2009). Institutional data management in higher education. ECAR, EDUCAUSE Center for Applied Research.
Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management information and technology in higher education (Vol. 8). Educause.
Interest in analytics is high, but
many institutions had yet to make progress
beyond basic reporting
Bichsel, J. (2012). Analytics in higher education: Benefits, barriers, progress, and recommendations. EDUCAUSE Center for Applied Research.
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
Current state
Benchmarking learning analytics status, policy and
practices for Australian universities
Solution-driven approach
Bought an analytics product.
Analytics box ticked!
Lack of data-informed
decision making culture
Macfadyen, L., & Dawson, S. (2012). Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan. Educational Technology & Society, 15(3), 149-163.
Researchers not focused on
scalability
Learning from
An institutional learning analytics vision
Tynan, B. & Buckingham Shum, S. (2013). Designing Systemic Learning Analytics at the Open University. SoLAR Open Symposium – Strategy & Policy for Systemic Learning Analytics.
What’s necessary to
move forward?
Data – Model – Transform
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
Data
– Model – Transform
Creative data sourcing
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
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 – Transform
Creative data sourcing
Necessary IT support
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
Awareness of limitations and
challenging assumptions
Kovanović, V., Gašević, D., Dawson, S., Joksimović, S., Baker, R. (in press). Does Time-on-task Estimation Matter? Implications on Validity of Learning Analytics Findings. Journal of Learning Analytics, 2(3).
Data –
Model
– Transform
Question-driven, not data-driven
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
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.
Once size fits all does not work
in learning analytics
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
Kovanović, V., Gašević, D., Joksimović, S., Hatala, M., Adesope, S. (2015). Analytics of Communities of Inquiry: Effects of Learning Technology Use on Cognitive Presence in Asynchronous Online Discussions. The Internet and Higher Education, 27, 74–89.
More time online does not
always mean better learning
Data – Model
–
Transform
Participatory design of analytics tools
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
Data – Model
–
Transform
Participatory design of analytics tools
Analytics tools for non-statistics experts
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
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.
Data – Model
–
Transform
Participatory design of analytics tools
Analytics tools for non-statistics experts
Develop capabilities to exploit (big) data
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
Marr, B. (Oct 2015). Forget Data Scientists - Make Everyone Data Savvy,
What to do if we detect
deficit models in our practice?
Are we ready to act on analytics?
Joksimović, S., Gašević, D., Loughin, T. M., Kovanović, V., Hatala, M. (2015). Learning at distance: Effects of interaction traces on academic achievement. Computers & Education, 87, 204–217.
How do we deal with
performance-oriented culture?
Are we ready to act on analytics?
Jovanović, J., Pardo, A., Gašević, D., Dawson, S., Mirriahi, N. (2015). Dynamic analytics of learning in flipped classrooms. Manuscript in preparation.
LA idealized systems model
Colvin, C., Rogers, T., Wade, A., Dawson, S., Gasevic, D., Buckingham Shum, S., Nelson, K., Alexander, S., Lockyer, L., Kennedy, G., Corrin, L., Fisher, J. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Australian Goverement’s Office for Learning and Teaching.
Macfadyen, L. P., Dawson, S., Pardo, A., & Gasevic, D. (2014). Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge. Research & Practice in Assessment, 9(2), 17-28.
Rapid Outcome Mapping Approach
(ROMA)
Embracing complexity of
educational systems
Capacity development
Multidisciplinary teams in
institutions critical
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.
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
