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
Open Education 2011: Openness and Learning Analytics
Open Learning InitiativeProduce and improve
scientifically-based courses and course materials which enact instruction and support instructorsProvide open access to these courses and materialsDevelop communities of use, research and development that enable evaluation and continuous improvement
Introduction: OutcomesShared understanding of challenges,
tensions andpossibilities 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 challengesDescribe community-based analytics plans: • Flexible, long-range planning • Useful, short-term stepsCommit to action • Identify best existing efforts
So why dont we do
this now?• Its hard• Its expensive• Individual faculty cant do it alone• It can be threatening to educators• Disparate systems• How do we measure it?We need enabling processes and systems
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