Show me the data!
Actionable insight from open courses
Analytics

“actionable insights through problem
definition and the application of
statistical models and analysis against
...
Overview
Data
Overview
Data

Analytic
Overview
Data

Analytic

Insight
Overview
Data

Analytic

Insight

Who?
Institution
Tutor
Self
…
Educational
Commercial
Overview
Data

Analytic
How?
Social network
Discourse
Content
Disposition
Context
…
Administration

Insight

Who?
Institut...
Overview
Data
What?
Platform
Service
…
Availability
Access

Analytic
How?
Social network
Discourse
Content
Disposition
Con...
Overview
Data
What?
Platform
Service
…
Availability
Access

Analytic
How?
Social network
Discourse
Content
Disposition
Con...
Misconception detection

Daphne Koller
Optimal Video Length for Student
Engagement

Philip Guo
Deconstructing Disengagement

Kizilcec, R. F., Piech, C., & Schneider, E. (2013)
Deconstructing disengagement: analyzing l...
Data

©Abode of Chaos 2011-11-10 16:44:58
http://www.flickr.com/40936370@N00/6332465890/
Data Access
Data Shapes
Data Flows

©Patrick Goossens 2009-03-01 12:11:05
http://www.flickr.com/8862589@N07/3333965118/
YouTube Analytics
YouTube Analytics
YouTube Analytics
YouTube Analytics
Google Analytics

http://mashe.hawksey.info/?p=15238
Google Analytics
Define events to
track in GA

http://mashe.hawksey.info/?p=15238
Google Analytics

Develop custom
segmentations
with eye on
actionable insight

Define events to
track in GA

http://mashe....
Google Analytics

Develop custom
segmentations
with eye on
actionable insight

Define events to
track in GA

Provide data ...
Canvas API
http://mashe.hawksey.info/?p=14634
Canvas API
http://mashe.hawksey.info/?p=14634
Canvas API
http://mashe.hawksey.info/?p=14634
Canvas API
http://mashe.hawksey.info/?p=14634
Canvas API
http://mashe.hawksey.info/?p=14634
Network effects
The social
network
diagrams can
be used to
identify:
• isolated
students
• group
malfunction
• users that
...
ocTEL (WordPress)
Analytically cloaked
“Learning and knowledge creation is often
distributed across multiple media and sites in
networked en...
% of
Total

Total Inputs
# Matched
# No Match
# Bad Input

Count
250
178 71.20%
72 28.80%
0
0.00%
In sample 41%
(n.103) emails
returned bio
% of
Total

Total Inputs
# Matched
# No Match
# Bad Input

Count
250
178 71.20%
...
In sample 41%
(n.103) emails
returned bio
% of
Total

API returns other
social profiles

Total Inputs
# Matched
# No Match...
• Detecting and Analyzing Subpopulations within
Connectivist MOOCs
• Retrospective investigation into learner
subpopulatio...
Summary
Data
What?
Platform
Service
…
Availability
Access

Analytic
How?
Social network
Discourse
Content
Disposition
Cont...
Thank you!
Questions?
Martin Hawksey
mashe.hawksey.info
@mhawksey
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Show me the data! Actionable insight from open courses

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  • Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
  • Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
  • Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
  • Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
  • Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
  • Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
  • Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
  • What: Coursera MCQ dataWho: tutors
  • What: edXWho: Institutions/tutors
  • These trajectories are also a useful framework for thecomparison of learner engagement between different coursestructures or instructional approachesWhat: CourseraK-meansWho: Inst.
  • HeadacheSimply getting the data in a timely fashion
  • HeadacheDo you want a database table dump?Do you need to join datasets, merge results, cleanse
  • Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
  • Show me the data! Actionable insight from open courses

    1. 1. Show me the data! Actionable insight from open courses
    2. 2. Analytics “actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data” Cooper, A. 2012 – Cetis Analytics Series What-is-Analytics-Vol1-No-5
    3. 3. Overview Data
    4. 4. Overview Data Analytic
    5. 5. Overview Data Analytic Insight
    6. 6. Overview Data Analytic Insight Who? Institution Tutor Self … Educational Commercial
    7. 7. Overview Data Analytic How? Social network Discourse Content Disposition Context … Administration Insight Who? Institution Tutor Self … Educational Commercial
    8. 8. Overview Data What? Platform Service … Availability Access Analytic How? Social network Discourse Content Disposition Context … Administration Insight Who? Institution Tutor Self … Educational Commercial
    9. 9. Overview Data What? Platform Service … Availability Access Analytic How? Social network Discourse Content Disposition Context … Administration Insight Who? Institution Tutor Self … Educational Commercial
    10. 10. Misconception detection Daphne Koller
    11. 11. Optimal Video Length for Student Engagement Philip Guo
    12. 12. Deconstructing Disengagement Kizilcec, R. F., Piech, C., & Schneider, E. (2013) Deconstructing disengagement: analyzing learner subpopulations in massive open online courses
    13. 13. Data ©Abode of Chaos 2011-11-10 16:44:58 http://www.flickr.com/40936370@N00/6332465890/
    14. 14. Data Access
    15. 15. Data Shapes
    16. 16. Data Flows ©Patrick Goossens 2009-03-01 12:11:05 http://www.flickr.com/8862589@N07/3333965118/
    17. 17. YouTube Analytics
    18. 18. YouTube Analytics
    19. 19. YouTube Analytics
    20. 20. YouTube Analytics
    21. 21. Google Analytics http://mashe.hawksey.info/?p=15238
    22. 22. Google Analytics Define events to track in GA http://mashe.hawksey.info/?p=15238
    23. 23. Google Analytics Develop custom segmentations with eye on actionable insight Define events to track in GA http://mashe.hawksey.info/?p=15238
    24. 24. Google Analytics Develop custom segmentations with eye on actionable insight Define events to track in GA Provide data using developed templated queries http://mashe.hawksey.info/?p=15238
    25. 25. Canvas API http://mashe.hawksey.info/?p=14634
    26. 26. Canvas API http://mashe.hawksey.info/?p=14634
    27. 27. Canvas API http://mashe.hawksey.info/?p=14634
    28. 28. Canvas API http://mashe.hawksey.info/?p=14634
    29. 29. Canvas API http://mashe.hawksey.info/?p=14634
    30. 30. Network effects The social network diagrams can be used to identify: • isolated students • group malfunction • users that are information brokers Hansen, D. L., Shneiderman, B., & Smith, M. (2010). Visualizing threaded conversation networks: mining message boards and email lists for actionable insights.
    31. 31. ocTEL (WordPress)
    32. 32. Analytically cloaked “Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs.” Suthers, D. D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning
    33. 33. % of Total Total Inputs # Matched # No Match # Bad Input Count 250 178 71.20% 72 28.80% 0 0.00%
    34. 34. In sample 41% (n.103) emails returned bio % of Total Total Inputs # Matched # No Match # Bad Input Count 250 178 71.20% 72 28.80% 0 0.00%
    35. 35. In sample 41% (n.103) emails returned bio % of Total API returns other social profiles Total Inputs # Matched # No Match # Bad Input Count 250 178 71.20% 72 28.80% 0 0.00%
    36. 36. • Detecting and Analyzing Subpopulations within Connectivist MOOCs • Retrospective investigation into learner subpopulation detection within the connectivist courses. • Using free and open source tools we will attempt to resolve activity data from multiple sources to permit the analysis of any engagement patterns.
    37. 37. Summary Data What? Platform Service … Availability Access Analytic How? Social network Discourse Content Disposition Context … Administration Insight Who? Institution Tutor Self … Educational Commercial
    38. 38. Thank you! Questions? Martin Hawksey mashe.hawksey.info @mhawksey

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