Open Education 2011: Openness and Learning Analytics

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  • Increase access, improve outcomes; educate more, better.
  • 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

    1. Open Education 2011:Openness and Learning AnalyticsJohn Rinderle @johnrinderleNorman Bier @normanbier
    2. 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
    3. 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
    4. Driving Feedback Loops
    5. Infinite Points of Light
    6. Infinite Points of Light
    7. Infinite Points of Light
    8. Infinite Points of Light
    9. Infinite ProliferationThe 4 R’sReuseRedistributeReviseRemix
    10. Infinite ProliferationThe 4 R’s NOT:Reuse RecreateRedistributeReviseRemix Add: Evaluate
    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
    14. Effectiveness is hit or miss
    15. EffectivenessWhat is working in openeducation? Why? And howdo you know?
    16. Effectiveness Demonstrably support students in meeting articulated, measurable learning outcomes in a given set of contexts
    17. 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
    18. Driving Feedback Loops
    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.
    22. Types of analytics• Educational/Academic Management analytics• Classroom Management analytics• Learning Outcomes analytics
    23. The problem of data collection1. Agreed upon standards2. Core collection3. Space for exploration
    24. The problem of data collection1. Agreed upon standards2. Core collection3. 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 AlreadyWorks1) Data Collection Systems Data Schemas2) Communities of Evidence3) Analysis Tools
    27. Learning Dashboard
    28. DataShop
    29. Evidence Hub
    30. Learning Registry
    31. Communities of Evidence
    32. And build new things1) Data Collection Systems Data Schemas2) Communities of Evidence3) Analysis Tools
    33. Driven by different types of data Metadata Paradata Synthetic Data Contextual Behavioral Interaction Semantic Raw
    34. Share Alike and Share Data
    35. Community Based Approach
    36. A middle ground?Infinite The OneVariety True Course Communities Coalesce
    37. Can we put these together?"Full spectrum" analytics to drive different types of decisionmaking, address different feedback loops
    38. Learning Intelligence Systems
    39. What would we be giving up? This approach forces us to allow our minds to be changed by evidence.
    40. Conclusion: next steps• Innovate• Standardize• Scale
    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 SecondaryEducation will require convertingteaching from a „solo sport‟ to acommunity 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?
    44. A Virtuous Cycle Educational Technology Data & Practice Theory

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