Learning Analytics - An Overview of a Research Community
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The document is a presentation by Doug Clow on learning analytics, presented on November 12, 2012. It defines learning analytics as the measurement and reporting of learner data to optimize learning environments and mentions various related fields such as predictive modeling and educational data mining. The presentation outlines the use of analytics to track student performance, identify at-risk learners, and improve teaching practices through social network analysis and other methods.
Learning Analytics - An Overview of a Research Community
- 1. Learning Analytics Presentation to IET SSST Doug Clow, 12 November 2012 This work by Doug Clow is licensed under a Creative Commons Attribution 3.0 Unported License.
- 2. What is learning analytics? • the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs – First International Conference on Learning Analytics And Knowledge (LAK11), Banff, Alberta, Feb 27-Mar 1 2011 .
- 3. • Learning analytics • Learner analytics • Academic analytics • Business Intelligence • Educational Data Mining • Web analytics • Social network analysis • Predictive modeling • Latent semantic analysis • … and more! (cc) gareth1953 http://www.flickr.com/photos/gareth1953/5477477947/
- 4. • An abundance of data • Qualitative and quantitative • To improve learning
- 5. • Predictive modeling, datamining (Blackboard) • Fit students in to one of three risk groups => traffic light • Trigger for intervention emails • Consistent grade performance improvement
- 7. • iSpot reputation: proxy measure of learning
- 8. SNAPP • Social Networks Adapting Pedagogic Practice • Network visualisations of forum activity data from VLE • See patterns • Spot central and disconnected • Identify at-risk • Improve teaching
- 10. Social network analysis • Suthers and Chu, 2012
- 11. Content/semantic analysis • Lárusson and White, 2012
- 12. Usage tracking • Santos, Govaerts, Verbert and Duval, 2012
- 13. Social learning analytics • Ferguson and Buckingham Shum, 2012
- 14. Dispositions analytics • Buckingham Shum and Deakin Crick, 2012
Editor's Notes
- #3: Data mining, academic analytics, learner analytics – focus here is on the learning, not the management and administration of learning International profile
- #7: Predict students at risk of non-completion OU data, mainly VLE Clickstream predictive – if your usage drops (absolute level not so good) Not submitting a TMA is predictive, low mark too Easier to predict non-completion earlier than later (not submitting the first TMA is a worse sign than not submitting the last)
- #14: Builds on Neil Mercer, content analysis
- #16: Speed and length of cycles: instant feedback as you learn, through to govt policy