2. Open Learning Initiative
Produce and improve scientifically-based courses and
course materials which enact instruction and support
instructors
Provide open access to these courses and materials
Develop communities of use, research and development
that enable evaluation and continuous improvement
3. Introduction: Outcomes
Shared understanding of challenges, tensions and
possibilities in learning analytics, around the dimensions of:
• Potential of well-used OER in a use-driven design context
• Adaptability (Variety)← → Analytics (Coherence)
• Analytics Tools and Approach
• Data—needs and challenges
Describe community-based analytics plans:
• Flexible, long-range planning
• Useful, short-term steps
Commit to action
• Identify best existing efforts
11. Proliferation isn’t just OER…
Intro to CS @ CMU Statistics @ everywhere
Core Statistics
Business Statistics
Research Statistics
Medical Statistics
12. What drives change in these scenarios?
• Data
• Intuition
• Market demand
• Instructor preferences
13. The problems of variety
• Quality is highly variable
• Much duplication of effort
• Difficult to choose appropriately
• Hard to evaluate
• Impossible to improve
• Hard to scale success up
16. Effectiveness
Demonstrably support students in meeting
articulated, measurable learning outcomes in a
given set of contexts
17. So why don't we do this now?
• It's hard
• It's expensive
• Individual faculty can't do it alone
• It can be threatening to educators
• Disparate systems
• How do we measure it?
We need enabling processes and systems
19. Great, but:
What does it mean when we get out of the realm of
discussion and into the realm of practice?
Learning Analytics
What are they?
How do we create and use them?
20. What do we mean by learning
analytics?
Proxies vs authentic assessment and evaluation
21. Analytics Definition
Data Collection Reporting Decision Making
Intervention
Action
Collecting the data is not enough. We also need to
make sense of if in ways that are actionable.
23. The problem of data collection
1. Agreed upon standards
2. Core collection
3. Space for exploration
24. The problem of data collection
1. Agreed upon standards
2. Core collection
3. Space for exploration
• Ownership
• Privacy
• Policy
25. Ideal world
•Common data standards
•Analytics-enabled OER
•Commonly accepted ownership and privacy approaches
•Commitment to measuring effectiveness through assessment
26. Bring Together What Already
Works
1) Data Collection Systems
Data Schemas
2) Communities of Evidence
3) Analysis Tools
41. Conclusion: next steps
• Innovate • Commitment to Assessment
• Standardize and Evaluation
• Scale
• Community Definition of
Analytics-enabled OER
• Common approach to data
• Shared and private analytics
platforms
42. “Improvement in Post Secondary
Education will require converting
teaching from a „solo sport‟ to a
community based research activity.”
—Herbert Simon
43. Questions
• Do you believe in this approach to analytics-enabled OER?
• Can this better address the pedagogy vs. reuse value curve?
Disclaimer (we were ambitious): Out of curiosity, how many of you read our abstract? Did it strike you as awfully ambitious for a short presentation? Yeah, us too… Quick review of abstractOutcomesPoint to discussion
Making assumptions about data is difficultbecause of variety.
Each OER is collecting and capturing different things or not collecting data at all.
And many more on OER Commons. Whatdrives this…
Challenge: building alignment around common learning outcomes