Using Learner Analytics toUnderstand Student Achievement in a Large Enrollment Hybrid Course slides posted: John Whitmer, Ed.D. Associate Director, Academic Technology Services California State University, Office of the Chancellor Society for Learning Analytics Research | LAK 2013 Case Study February 19, 2013
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)
Case Study: Intro to Religious Studies• Undergraduate, introductory, high demand• Redesigned to hybrid delivery format 54 F’s 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
Driving Conceptual Questions1. How is student LMS use related to academic achievement in a single course section?2. How does that finding compare to the relationship of achievement with traditional student characteristic variables?3. How are these relationships different for “at-risk” students (URM & Pell-eligible)?4. What data sources, variables and methods are most useful to answer these questions?
UniversityGender Freq. Percent Average Difference Female 231 62% 51% 11% Male 142 38% 48% -10%Age 0% 17 22 6% 18-21 302 81% 22-30 22 6% 31+ 1 0%Under-representedMinority No 264 71% 73% -2% Yes 109 29% 27% 2%Pell-eligible Freq. Percent No 210 56% Yes 163 44%First Attend College Freq. No 268 72% Yes 105 28%Enrollment Status Freq. Continuing Student 217 58% Transfer 17 5% First-Time Student 139 37%
Question 1 Results: Correlation LMS Use w/Final Grade Scatterplot ofAssessment Activity Hits vs. Course Grade
Question 2 Results:Correlation: Student Char. w/Final Grade Scatterplot of HS GPA vs. Course Grade
Conclusion: LMS Use Variables better Predictors than Student Characteristics LMS Student Use Characteristic Variables Variables 18% Average (r = 0.35–0.48) > 4% Average (r = -0.11–0.31)Explanation of change Explanation of change in final grade in final grade
Smallest LargestLMS Use Variable Student > Characteristic (Administrative Activities) (HS GPA) r = 0.35 r = 0.31
Combined Variables Regression Final Grade by LMS Use & Student Characteristic Variables LMS Student Use Characteristic Variables Variables 25% (r2=0.25) > +10% (r2=0.35)Explanation of change Explanation of change in final grade in final grade
Question 3 Results:Regression by “At Risk” Population Subsamples
Conclusions1. At the course level, LMS use better predictor of academic achievement than student demographics (what do, not who are).2. Small strength magnitude of complete model demonstrates relevance of data, but suggests that better methods could produce stronger results.3. LMS data requires extensive filtering to be useful; student variables need pre-screening for missing data.
More Conclusions4. LMS use frequency is a proxy for effort. Not a very complex indicator.5. Student demographic measures need revision for utility in Postmodern era (importance to student, more frequent sampling, etc.).6. LMS effectiveness for at-risk students may be caused by non-technical barriers. Need additional research!
Ideas & FeedbackPotential for improved LMS analysis methods: social learning activity patterns discourse content analysis time series analysisGroup students by broader identity, with uniquevariables: Continuing student (Current college GPA, URM, etc. First-time freshman (HS GPA, SAT/Act, etc)