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Research Findings Logging on for Higher Achievement Research

Research Findings Logging on for Higher Achievement Research. John Whitmer Updated: 1-25-2013. 1. Chico State Learner Analytics RESEARCH study “Logging on to Improve Achievement” by John Whitmer EdD . Dissertation (UC Davis & Sonoma State). Case Study: Intro to Religious Studies.

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Research Findings Logging on for Higher Achievement Research

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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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