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Using Learner Analytics to Understand Student Achievement in a Large Enrollment Hybrid Course

Using Learner Analytics to Understand Student Achievement in a Large Enrollment Hybrid Course. John Whitmer, Ed.D. Updated: February 19, 2013. Outline. Context Methods & Tools Findings Conclusions & Next Steps. 1. Context. Case Study: Intro to Religious Studies.

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Using Learner Analytics to Understand Student Achievement in a Large Enrollment Hybrid Course

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  1. Using Learner Analytics to Understand Student Achievement in a Large Enrollment Hybrid Course John Whitmer, Ed.D. Updated: February 19, 2013

  2. Outline • Context • Methods & Tools • Findings • Conclusions & Next Steps

  3. 1. Context

  4. Case Study: Intro to Religious Studies • Undergraduate, introductory, high demand • Redesigned to hybrid delivery format through “academy eLearning program” • Enrollment: 373 students (54% increase on largest section) • Highest LMS (Vista) usage entire campus Fall 2010 (>250k hits) • Bimodal outcomes: • 10% increase on final exam • 7% & 11% increase in DWF • Why? Can’t tell with aggregated data 54 F’s

  5. Founded in 1887 • 15,257 FTES, 95% from California, serves 12 counties • Primarily residential, undergraduate teaching college • Campus in California State University system (23 colleges, 44,000 faculty and staff, 437,000 students)

  6. CSU Budget Proposed Increase! Source: CSU Chancellor’s Officehttp://bit.ly/X7LYeK

  7. Driving Conceptual Questions • How is student LMS use related to academic achievement in a single course section? • How does that finding compare to the relationship of achievement with traditional student characteristic variables? • How are these relationships different for “at-risk” students (URM & Pell-eligible)? • What data sources, variables and methods are most useful to answer these questions?

  8. 2. Methods & TOols

  9. Methods at a Glance • Data Sources: 1) LMS logfiles, 2) SIS data, 3) Course data • Process • Clean/filter/transform/reduce data (70% effort) • Descriptive / exploratory analysis (20% effort) • Statistical analysis (10% effort) • Factor analysis • Correlation single variables • Regression multiple variables; partial & complete

  10. Tools Used

  11. Variables

  12. Missing Data On Critical Indicators

  13. Final data set: 72,000 records (-73%)

  14. LMS Use Consistent across Categories Factor Analysis of LMS Use Categories

  15. 3. Findings

  16. Clear Trend: Grade w/Mean LMS Hits

  17. Question 1 Results: Correlation LMS Use w/Final Grade Scatterplot of Assessment Activity Hits vs. Course Grade

  18. Question 2 Results: Correlation: Student Char. w/Final Grade Scatterplot of HS GPA vs. Course Grade

  19. Question 2 Results: Correlation: Student Char. w/Final Grade

  20. Conclusion: LMS Use Variables better Predictors than Student Characteristics LMS Use Variables18% Average(r = 0.35–0.48)Explanation of change in final grade Student Characteristic Variables 4% Average(r = -0.11–0.31) Explanation of change in final grade >

  21. SmallestLMS Use Variable(Administrative Activities) r = 0.35 Largest Student Characteristic (HS GPA) r = 0.31 >

  22. Combined Variables Regression Final Grade by LMS Use & Student Characteristic Variables LMS Use Variables25% (r2=0.25)Explanation of change in final grade Student Characteristic Variables +10%(r2=0.35) Explanation of change in final grade >

  23. Question 3 Results:Regressionby “At Risk” Population Subsamples

  24. At-Risk Students: “Over-Working Gap”

  25. Activities by Pell and Grade Extra effort in content-related activities

  26. Previous Studies Relating LMS Use to Course Grade

  27. 4. Conclusions & Next Steps

  28. Conclusions • At the course level, LMS use better predictor of academic achievement than student demographics (what do, not who are). • Small strength magnitude of complete model demonstrates relevance of data, but suggests that better methods could produce stronger results. • LMS data requires extensive filtering to be useful; student variables need pre-screening for missing data.

  29. More Conclusions • LMS use frequency is a proxy for effort. Not a very complex indicator. • Student demographic measures need revision for utility in Postmodern era (importance to student, more frequent sampling, etc.). • LMS effectiveness for at-risk students may be caused by non-technical barriers. Need additional research!

  30. Ideas & Feedback Potential for improved LMS analysis methods: • social learning • activity patterns • discourse content analysis • time series analysis Group students by broader identity, with unique variables: • Continuing student (Current college GPA, URM, etc. • First-time freshman (HS GPA, SAT/Act, etc)

  31. Feedback? Questions? John Whitmer jwhitmer@calstate.edu Slideshttp://slidesha.re/15iokzE Complete monographhttp://bit.ly/15ijySP Twitter: johncwhitmer

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