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Open Education 2011:
Openness and Learning Analytics
John Rinderle   @johnrinderle
Norman Bier     @normanbier
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
Driving Feedback Loops
Infinite Points of Light
Infinite Points of Light
Infinite Points of Light
Infinite Points of Light
Infinite Proliferation

The 4 R’s
Reuse
Redistribute
Revise
Remix
Infinite Proliferation

The 4 R’s           NOT:
Reuse               Recreate
Redistribute
Revise
Remix               Add:
                    Evaluate
Proliferation isn’t just OER…

Intro to CS @ CMU   Statistics @ everywhere
                    Core Statistics
                    Business Statistics

                    Research Statistics

                    Medical 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 to evaluate
• Impossible to improve
• Hard to scale success up
Effectiveness
 is hit or miss
Effectiveness
What is working in open
education? Why? And how
do you know?
Effectiveness


     Demonstrably support students in meeting
   articulated, measurable learning outcomes in a
                 given set of contexts
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
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 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.
Types of analytics

• Educational/Academic Management analytics
• Classroom Management analytics
• Learning Outcomes analytics
The problem of data collection

1. Agreed upon standards
2. Core collection
3. Space for exploration
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
Bring Together What Already
Works
1) Data Collection Systems
   Data Schemas

2) Communities of Evidence

3) Analysis Tools
Learning Dashboard
DataShop
Evidence Hub
Learning Registry
Communities of Evidence
And build new things

1) Data Collection Systems
   Data Schemas

2) Communities of Evidence

3) Analysis Tools
Driven by different types of data


             Metadata

             Paradata      Synthetic Data
             Contextual

             Behavioral

             Interaction    Semantic

             Raw
Share Alike and Share Data
Community Based Approach
A middle ground?


Infinite
                           The One
Variety                    True Course
             Communities
              Coalesce
Can we put these together?

"Full spectrum" analytics to drive different types of decision
making, address different feedback loops
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
                  •   Community Definition of
                      Analytics-enabled OER

                  •   Common approach to data

                  •   Shared and private analytics
                      platforms
“Improvement in Post Secondary
Education will require converting
teaching from a „solo sport‟ to a
community based research activity.”
                        —Herbert Simon
Questions

• Do you believe in this approach to analytics-enabled OER?
• Can this better address the pedagogy vs. reuse value curve?
A Virtuous Cycle

        Educational
        Technology             Data
         & Practice




                      Theory

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Open Education 2011: Openness and Learning Analytics

  • 1. Open Education 2011: Openness and Learning Analytics John Rinderle @johnrinderle Norman Bier @normanbier
  • 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
  • 9. Infinite Proliferation The 4 R’s Reuse Redistribute Revise Remix
  • 10. Infinite Proliferation The 4 R’s NOT: Reuse Recreate Redistribute Revise Remix 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
  • 15. Effectiveness What is working in open education? Why? And how do you know?
  • 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.
  • 22. Types of analytics • Educational/Academic Management analytics • Classroom Management analytics • Learning Outcomes analytics
  • 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
  • 32. And build new things 1) Data Collection Systems Data Schemas 2) Communities of Evidence 3) Analysis Tools
  • 33. Driven by different types of data Metadata Paradata Synthetic Data Contextual Behavioral Interaction Semantic Raw
  • 34. Share Alike and Share Data
  • 36. A middle ground? Infinite The One Variety True Course Communities Coalesce
  • 37. Can we put these together? "Full spectrum" analytics to drive different types of decision making, address different feedback loops
  • 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 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?
  • 44. A Virtuous Cycle Educational Technology Data & Practice Theory

Editor's Notes

  1. Increase access, improve outcomes; educate more, better.
  2. 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
  3. Making assumptions about data is difficultbecause of variety.
  4. Each OER is collecting and capturing different things or not collecting data at all.
  5. And many more on OER Commons. Whatdrives this…
  6. Challenge: building alignment around common learning outcomes