Learning design and learning analytics
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This document discusses the relationship between learning design and learning analytics. It argues that learning design provides context that helps frame analytics questions and identify appropriate analysis. A MOOC planner is presented that prompts designers to plan different activity types like delivered content, reflection, collaboration, and assessment. Analytics can then provide insight into how learners engaged with those different activities. The document also discusses clustering analysis of learner engagement patterns in MOOCs, like samplers, strong starters, mid-way dropouts, and keen completers. Engagement patterns can vary based on pedagogy and learning design.
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Learning design and learning analytics
- 1. Learning design & learning analytics – building the links Rebecca Ferguson The Open University What the Research Says: November 2014
- 2. Why learning design? • Provides a set of tools and information to support a learner-activity based approach • Helps to show the costs and performance outcomes of design decisions • Puts the learning journey at the heart of the design process • Enables the sharing of best practice • Helps partners choose and integrate a coherent range of media, technologies and pedagogies • Enables a consistent and structured approach to review and analytics
- 3. Advantages of design for analytics Helps to frame and focus analytics questions What did they learn?… in relation to learning outcomes Were they social?... when they were collaborating Did they share links?... when encouraged to browse Did they return to steps?... when encouraged to reflect Helps to identify appropriate forms of analysis The same step, but with a focus on • Number of visits if content • Length, quality, number of comments if conversational • Dwell time and repeat visits if reflection
- 4. MOOC planner Prompts designers to block out different types of learning activity: • Delivered • Reflection • Collaboration • Conversation • Networking • Browsing • Assessment
- 5. Planner and analytics Delivered Content (reading, watching, listening and observing) Analytics: amount of content viewed, dwell time Reflection (thinking, considering and reflecting) Analytics: returns to the same material, reflection exercises completed, quality of reflection Collaboration (constructing, collaborating, defining and engaging) Analytics: collaboration exercises completed, quality of collaboration
- 6. MOOC map • Guidance & support • Content & experience • Reflection & demonstration • Communication & collaboration
- 7. Activity and learning design
- 8. Patterns of engagment: Coursera Sampling: learners explored some course videos. Auditing: learners watched most videos, but completed assessments rarely, if at all Disengaging: learners completed assessments at the start of the course and then reduced their engagement Completing: learners completed most assessments Kizilcec, R., Piech, C., and Schneider, E., 2013. Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In LAK13
- 10. Patterns of engagement On an eight-week MOOC Samplers visit only briefly [1, 0, 0, 0, 0, 0, 0, 0] Strong starters do first assessment [9, 1, 0, 0, 0, 0, 0, 0] Returners come back in Week 2 [9, 9, 0, 0, 0, 0, 0, 0] Mid-way Dropouts [9, 9, 9, 4, 1, 1, 0, 0] Nearly There drop out near the end [11, 11, 9, 11, 9, 9, 8, 0] Late Completers finish [5, 5, 5, 5, 5, 5, 9, 9, 9] Keen Completers do almost everything [11, 11, 9, 9, 11, 11, 9, 9] Patterns vary with pedagogy and learning design
- 11. Short MOOCs Surgers concentrate their effort after the first week of a three-week course. They do little in Week 1 other than submit their assessment late, engage more in Week 2, but still submit their assessment late (working on it in Week 3), and engage but do not submit in Week 3. On average, they post one or two comments. A typical engagement profile for this cluster is: [4, 6, 2] Improvers fall behind in Week 1, submitting their first assessment late. However, they engage more in Week 2 and by Week 3 they are on schedule and submit their assessment on time. They view the majority of steps on the course and typically post more than one comment. A typical engagement profile for this cluster is: [5, 6, 9]