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Using CMS Data as a Force for Good? Applying Academic Analytics to Teaching and Learning

Using CMS Data as a Force for Good? Applying Academic Analytics to Teaching and Learning. Leah P. Macfadyen Science Centre for Learning and Teaching, UBC, Canada Shane Dawson Queensland University of Technology/University of Wollongong, Australia. Project Foundations.

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Using CMS Data as a Force for Good? Applying Academic Analytics to Teaching and Learning

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  1. Using CMS Data as a Force for Good? Applying Academic Analytics to Teaching and Learning Leah P. Macfadyen Science Centre for Learning and Teaching, UBC, Canada Shane Dawson Queensland University of Technology/University of Wollongong, Australia

  2. Project Foundations • Emergence of “Academic Analytics” • Increased use of ICTs in teaching and learning • Increasing availability and detail of Course Management System data • Increasinging interest in socio-constructivist learning theory (and its implications)

  3. Teaching and Learning for Engagement • Socio-constructivist theories of learning • Importance of engagement and learning communities • Increasing use of ICTs • Questions • Which web-based tools and activities can promote student engagement and community online? • How do engagement and sense of community correlate with student achievement?

  4. CMS data • CMS usage is now prevalent (US data 2006: 93% student adoption in average of 2.5 courses; UBC data: >25,000 student users of Bb Vista) • CMS data is immediate (can be mined at any time) • CMS data is non-intrusive (does not require faculty intervention) • (Bart Collins, Purdue University, 2006)

  5. Bb Vista Tracking Data

  6. Data points available to instructors

  7. Project goals • Develop a data interpretation and visualization tool to: • aid faculty and students in the interpretation of the vast array of data currently captured by Bb Vista • permit ongoing formative evaluation of student engagement in learning activities and allow early identification of at risk students • provide administrators and institutions with benchmarks of activity, usage trends, disciplinary differences

  8. Case Study: BIOL200

  9. Descriptive Data

  10. Summary of correlations (bivariate)

  11. Bivariate Correlations • Categories of variables: • Measures of efforttime online, number of sessions online, time on specific activities • Engagement and community activitiesdiscussion forums, chat • Administrative activitiesmail, calendar, announcements, tracking, grades • Content-related activitiesfiles, folders, media • Assessment activitiesassignments, assessments

  12. Predictive modelling • BIOL200 online multiple regression (with variables for tools used) • BIOL200 web-supported multiple regression (with variables for tools used): • (Compare to: Morris, Finnegan & Wu (2005): R2 = .310 for online courses)

  13. Discussion participation….

  14. Visualizing student engagement Instructor http://www.randomsyntax.com/blackboard-forum-social-network-analysis/

  15. Disconnected students Instructor

  16. Institutional tool use Percentage of total interactions 27 Aug 2007 06 Jan 2008

  17. Lessons learned so far… • Some (but not all) CMS data variables are useful predictors of eventual student achievement • Several seem to support theoretical propositions regarding the importance of community in learning • Correlation ≠ causality… • Significance of variables depends on course design

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