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Leveraging CMS Data for Academic Analytics: Enhancing Teaching and Learning Engagement

This project explores the application of academic analytics in higher education, specifically through Course Management System (CMS) data. It discusses how the increasing use of information and communication technologies (ICTs) and socio-constructivist learning theories can promote student engagement and create learning communities. The focus includes the development of tools for interpreting data from CMS platforms (e.g., Bb Vista) to enhance formative evaluations and identify at-risk students. The study further delves into correlations between various engagement metrics and student success in a BIOL200 case study.

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Leveraging CMS Data for Academic Analytics: Enhancing Teaching and Learning Engagement

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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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