Empowering instructors through customizable collection and analyses of actionable information
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The document discusses the implementation and evolution of learning analytics at the University of Sydney, highlighting the need for customizable and actionable data to support both instructors and students. It emphasizes the co-evolution of capabilities, the importance of personalization and ease of use, and offers lessons learned from past experiences. The aim is to facilitate a wider roll-out and evaluate the impact of these analytics on educational outcomes.
Empowering instructors through customizable collection and analyses of actionable information
- 1. The University of Sydney Page 1 Empowering instructors through customizable collection and analyses of actionable information @dannydotliu danny.liu@sydney.edu.au
- 2. The University of Sydney Page 2 Four tensions of learning analytics Research Teaching Top-down Bottom-up Human Computer Macro Micro Data & algorithmsStudents & teachers Massified & aggregate Contextualised Practical & actionableTheoretical Institutional Bespoke
- 3. The University of Sydney Page 3 The realities of learning and teaching at scale Research Teaching Top-down Bottom-up Human Computer Macro Micro Data & algorithmsStudents & teachers Massified & aggregate Contextualised Practical & actionableTheoretical Institutional Bespoke
- 4. The University of Sydney Page 4 Attendance Interim grades LMS metrics, other 3rd party data The Student Relationship Engagement System
- 5. The University of Sydney Page 5 Personalising connections with students – Empowering staff – Flexible & intuitive – Targeted and personalised – Multi-channel – Benefits – Highly customisable – Efficient – key data in one place, operating at scale – Connect staff and all students (not just at-risk)
- 6. The University of Sydney Page 6 Student outcomes Discontinued Failed Passed F P C D HD 1st year arts unit ~500 enrolment 1st year science unit ~1000 enrolment “Many thanks Adam. Yes, things are going much better this semester. I really appreciate how you keep in contact and keep an eye on us. It's such a big class, I don't know how you do it.” “Just to let you know that your emails really helped me survive last semester. I never realised how big a change it would be from school.”
- 7. The University of Sydney Page 7 Co-evolution of the SRES – Organic adoption by academics – Co-evolution of capabilities 0 5000 10000 15000 20000 25000 0 10 20 30 40 50 60 70 2012 2013 2014 2015 Numberofstudents Numberofunitsorschools Number of units Number of schools Number of students Pilot EWS introduced EWS integrated New analyses More data types Data import
- 8. The University of Sydney Page 8 Learning analytics by stealth? – Co-evolving capabilities and competencies around data-driven pedagogy and curriculum
- 9. The University of Sydney Page 9 Practical learning analytics Research Teaching Top-down Bottom-up Human Computer Macro Micro Data & algorithmsStudents & teachers Massified & aggregate Contextualised Practical & actionableTheoretical Institutional Bespoke
- 10. The University of Sydney Page 10 Lessons learned and next steps – Looking back – It was ugly but it worked – Personalisation at scale – Ease of use is important – does it save time? – Attendance was a (unsurprisingly?) popular metric – Everyone uses a customisable system differently – Looking forward – Trans-Tasman redevelopment effort – Facilitate wider roll-out – Research & evaluation of impact on students and staff
- 11. The University of Sydney Page 11 Adoption pipeline Colvin et al. (2015) Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Office of Learning and Teaching, Sydney. “First, implementers require an analytic tool or combination of tools that manage data inputs and generate outputs in the form of actionable feedback… As these increasingly meet the real needs of learners and educators the organisational uptake is accelerated.”
- 12. The University of Sydney Page 12 “Learning analytics is not an elixir for ineffective teaching, nor does it reveal an ideal pedagogy; instead, it provides data-driven tools or suggestions to help instructors make changes that can be measured in terms of student outcomes.” Pistilli, M. D., Willis III, J. E., & Campbell, J. P. (2014). Analytics Through an Institutional Lens: Definition, Theory, Design, and Impact. In Learning Analytics (pp. 79-102). Springer New York.
- 13. The University of Sydney Page 13 – Adam Bridgeman – Charlotte Taylor – Abelardo Pardo – Kathryn Bartimote-Aufflick – Melanie Nguyen – Hong Dao Nguyen – And many staff and students @dannydotliu danny.liu@sydney.edu.au It takes a village