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Open Education 2011: Openness and Learning Analytics
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
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
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
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
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?
What do we mean by
learning analytics? Proxies vs authentic assessment and evaluation
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.
The problem of data collection
1. Agreed upon standards 2. Core collection 3. Space for exploration • Ownership • Privacy • Policy
Ideal world •Common data standards
•Analytics-enabled OER •Commonly accepted ownership and privacy approaches •Commitment to measuring effectiveness through assessment
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
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