Open Course Data Insights
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This document discusses how educational institutions and others can gain actionable insights from open online courses through data analytics. It explores analyzing data from sources like social networks, video viewing patterns, and discussion forums to understand student engagement and identify at-risk groups. Various analytic tools are described like YouTube Analytics, Google Analytics, and APIs that can access data from learning management systems to help tutors, researchers, and institutions optimize courses and learning outcomes.
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Open Course Data Insights
- 1. Show me the data! Actionable insight from open courses
- 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
- 7. Overview Data Analytic Insight Who? Institution Tutor Self … Educational Commercial How? Social network Discourse Content Disposition Context … Administration
- 8. Overview Data Analytic Insight Who? Institution Tutor Self … Educational Commercial How? Social network Discourse Content Disposition Context … Administration What? Platform Service … Availability Access
- 9. Overview Data Analytic Insight Who? Institution Tutor Self … Educational Commercial How? Social network Discourse Content Disposition Context … Administration What? Platform Service … Availability Access
- 11. Optimal Video Length for Student Engagement Philip Guo
- 12. Deconstructing Disengagement Kizilcec, R. F., Piech, C., & Schneider, E. (2013) Deconstructing disengagement: analyzing learner subpopulations in massive open online courses
- 13. Data ©Abode of Chaos 2011-11-10 16:44:58 http://www.flickr.com/40936370@N00/6332465890/
- 14. Data Access
- 15. Data Shapes
- 16. Data Flows ©Patrick Goossens 2009-03-01 12:11:05 http://www.flickr.com/8862589@N07/3333965118/
- 22. Google Analytics Define events to track in GA http://mashe.hawksey.info/?p=15238
- 23. Google Analytics Define events to track in GADevelop custom segmentations with eye on actionable insight http://mashe.hawksey.info/?p=15238
- 24. Google Analytics Define events to track in GADevelop custom segmentations with eye on actionable insight Provide data using developed templated queries http://mashe.hawksey.info/?p=15238
- 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.
- 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. Count % of Total Total Inputs 250 # Matched 178 71.20% # No Match 72 28.80% # Bad Input 0 0.00%
- 34. Count % of Total Total Inputs 250 # Matched 178 71.20% # No Match 72 28.80% # Bad Input 0 0.00% In sample 41% (n.103) emails returned bio
- 35. Count % of Total Total Inputs 250 # Matched 178 71.20% # No Match 72 28.80% # Bad Input 0 0.00% In sample 41% (n.103) emails returned bio API returns other social profiles
- 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. Summary Data Analytic Insight Who? Institution Tutor Self … Educational Commercial How? Social network Discourse Content Disposition Context … Administration What? Platform Service … Availability Access
Editor's Notes
- 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 platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context 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 platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context 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 platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context 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 platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context 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 platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context 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 platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context 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 platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context analytics — mobile computing is transforming access to both people and content.
- What: Coursera MCQ data Who: tutors
- What: edX Who: Institutions/tutors
- These trajectories are also a useful framework for the comparison of learner engagement between different course structures or instructional approaches What: Coursera K-means Who: Inst.
- Headache Simply getting the data in a timely fashion
- Headache Do 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 platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context analytics — mobile computing is transforming access to both people and content.