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Enhancing Academic Achievement through Online Engagement: A Comprehensive Study

Explore the impact of LMS usage on student success in a religious studies course, comparing traditional metrics and at-risk student performance. Analyze data sources, variables, and methods for insight into enhancing learning outcomes. Discover correlations between LMS hits, final grades, and student characteristics. Gain valuable insights for improving academic achievement.

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Enhancing Academic Achievement through Online Engagement: A Comprehensive Study

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  1. Research FindingsLogging on for Higher Achievement Research John Whitmer Updated: 1-25-2013

  2. 1. Chico State Learner Analytics RESEARCH study“Logging on to Improve Achievement” by John WhitmerEdD. Dissertation (UC Davis & Sonoma State)

  3. Case Study: Intro to Religious Studies • Redesigned to hybrid delivery through Academy eLearning • Enrollment: 373 students (54% increase on largest section) • Highest LMS (Vista) usage entire campus Fall 2010 (>250k hits) • Bimodal outcomes: • 10% increased SLO mastery • 7% & 11% increase in DWF • Why? Can’t tell with aggregated reporting data 54 F’s

  4. 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?

  5. Variables

  6. Clear Trend: Grade w/Mean LMS Hits

  7. Scatterplot: Grade w/Mean LMS Hits

  8. Correlation: LMS Use w/Final Grade Scatterplot of Assessment Activity Hits vs. Course Grade

  9. Correlation: Student Char. w/Final Grade Scatterplot of HS GPA vs. Course Grade

  10. Separate Variables: Correlation LMS Use & Student Characteristic with Final Grade 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 >

  11. 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 >

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

  13. Regression r2 Results Comparison

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

  15. Filtering Data – Lots of “Noise”; Low “Signal” Final data set: 72,000 records (-73%) Slides: http://goo.gl/DmT8z

  16. Feedback? Questions? John Whitmer (jwhitmer@calstate.edu)

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