Some Thoughts on Learning Analytics and Educational Data Mining
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Professor Mark Brown discusses learning analytics, focusing on its definition, distinctions from academic analytics, and the importance of data collection for optimizing learning environments. He emphasizes the need for clarity in target audiences and outcomes, addressing the roles of various stakeholders, including managers and learners. The talk suggests that success in this field requires a change in mindset rather than merely accumulating data.
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Some Thoughts on Learning Analytics and Educational Data Mining
- 1. Some Thoughts on Learning Analytics and Educational Data Mining Professor Mark Brown National Institute for Digital Learning IUA Offices, Dublin 17th December 2015
- 2. 1. Question of scope 2. Question of audience 3. Question of outcomes My brief talk…
- 3. What do we mean by learning analytics? How can we distinguish the different dimensions? 1. Question of scope…
- 4. “According to the 1st International Conference on Learning Analytics and Knowledge, “learning analytics is 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.” “Academic analytics, in contrast, is the application of business intelligence in education and emphasizes analytics at institutional, regional, and international levels.” Long, P. & Siemens, G. (2011). Penetrating the fog: analytics in learning and education. EDUCAUSE Review, 46 (5), 31-40
- 5. Is this distinction still helpful and valid?
- 6. National… - Integrated Data Infrastructure - Graduate Outcomes Project Institutional… - Data Warehouse - Integrated Dashboard - QA / QE
- 7. • What level do you target? - All levels at risk of depth - Targeted level at risk of incompleteness - Hybrid with combination of above What can be done in the timeframe for the greatest impact?
- 8. • Managers • Heads of Schools • Programme Leaders • Lecturers • Learners • All of the above 2. Question of audience… - What? - When? - How?
- 9. - Action - Evaluation - Collection - Interpretation Lecturers…
- 10. Self-regulation | Validity | Ethics Learners…
- 11. 3. Question of outcomes… • Student success • Teaching enhancement • Evidence-based culture
- 12. “Not the big idea! Less of a dataset and more of a mindset” (Brown, 2015)