Show me the data! Actionable insight from open courses
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Show me the data! Actionable insight from open courses
- 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
- 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 Develop custom segmentations with eye on actionable insight Define events to track in GA http://mashe.hawksey.info/?p=15238
- 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
- 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. % of Total Total Inputs # Matched # No Match # Bad Input Count 250 178 71.20% 72 28.80% 0 0.00%
- 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. 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. • 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.
Hinweis der Redaktion
- 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.