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Open Education 2011:Openness and Learning AnalyticsJohn Rinderle   @johnrinderleNorman Bier     @normanbier
Open Learning InitiativeProduce and improve scientifically-based courses and  course materials which enact instruction and...
Introduction: OutcomesShared understanding of challenges, tensions andpossibilities in learning analytics, around the dime...
Driving Feedback Loops
Infinite Points of Light
Infinite Points of Light
Infinite Points of Light
Infinite Points of Light
Infinite ProliferationThe 4 R’sReuseRedistributeReviseRemix
Infinite ProliferationThe 4 R’s           NOT:Reuse               RecreateRedistributeReviseRemix               Add:      ...
Proliferation isn’t just OER…Intro to CS @ CMU   Statistics @ everywhere                    Core Statistics               ...
What drives change in these scenarios?•   Data•   Intuition•   Market demand•   Instructor preferences
The problems of variety• Quality is highly variable• Much duplication of effort• Difficult to choose appropriately• Hard t...
Effectiveness is hit or miss
EffectivenessWhat is working in openeducation? Why? And howdo you know?
Effectiveness     Demonstrably support students in meeting   articulated, measurable learning outcomes in a               ...
So why dont we do this now?• Its hard• Its expensive• Individual faculty cant do it alone• It can be threatening to educat...
Driving Feedback Loops
Great, but:What does it mean when we get out of the realm of discussion and into the realm of practice?Learning Analytics ...
What do we mean by learning analytics?Proxies vs authentic assessment and evaluation
Analytics Definition   Data Collection  Reporting  Decision Making                                Intervention         ...
Types of analytics• Educational/Academic Management analytics• Classroom Management analytics• Learning Outcomes analytics
The problem of data collection1. Agreed upon standards2. Core collection3. Space for exploration
The problem of data collection1. Agreed upon standards2. Core collection3. Space for exploration• Ownership• Privacy• Policy
Ideal world•Common data standards•Analytics-enabled OER•Commonly accepted ownership and privacy approaches•Commitment to m...
Bring Together What AlreadyWorks1) Data Collection Systems   Data Schemas2) Communities of Evidence3) Analysis Tools
Learning Dashboard
DataShop
Evidence Hub
Learning Registry
Communities of Evidence
And build new things1) Data Collection Systems   Data Schemas2) Communities of Evidence3) Analysis Tools
Driven by different types of data             Metadata             Paradata      Synthetic Data             Contextual    ...
Share Alike and Share Data
Community Based Approach
A middle ground?Infinite                           The OneVariety                    True Course             Communities  ...
Can we put these together?"Full spectrum" analytics to drive different types of decisionmaking, address different feedback...
Learning Intelligence Systems
What would we be giving up?   This approach forces us to allow our minds to            be changed by evidence.
Conclusion: next steps• Innovate• Standardize• Scale
Conclusion: next steps• Innovate        •   Commitment to Assessment• Standardize         and Evaluation• Scale           ...
“Improvement in Post SecondaryEducation will require convertingteaching from a „solo sport‟ to acommunity based research a...
Questions• Do you believe in this approach to analytics-enabled OER?• Can this better address the pedagogy vs. reuse value...
A Virtuous Cycle        Educational        Technology             Data         & Practice                      Theory
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Open Education 2011: Openness and Learning Analytics

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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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