using learning analytics to understand student achievement
DESCRIPTION
Case study presentation for the Learning and Knowledge Analytics 2013 MOOC.TRANSCRIPT
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John Whitmer, Ed.D.Associate Director, Academic Technology Services
California State University, Office of the Chancellor
Society for Learning Analytics Research | LAK 2013 Case StudyFebruary 19, 2013
Using Learner Analytics to Understand Student Achievement in
a Large Enrollment Hybrid Courseslides posted:
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Outline
1. Context
2. Methods & Tools
3. Findings
4. Conclusions & Next Steps
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1. CONTEXT
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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)
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CSU Budget Proposed Increase!
Source: CSU Chancellor’s Officehttp://bit.ly/X7LYeK
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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
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Driving Conceptual Questions
1. 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?
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Gender Freq. PercentUniversity 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-represented Minority
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%
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2. METHODS & TOOLS
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Methods at a Glance
Data Sources: 1) LMS logfiles, 2) SIS data, 3) Course data
Process1. Clean/filter/transform/reduce data (70% effort)
2. Descriptive / exploratory analysis (20% effort)
3. Statistical analysis (10% effort) Factor analysis Correlation single variables Regression multiple variables; partial & complete
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Tools Used
App Function
Excel Early data exploration; simple sorting; tables for print/publication
Tableau Complex data summaries and explorations; complex charts; presentation charts
Final/formal descriptive data; statistical analysis; some charts (scatterplots)
Statistical analysis (factor analysis)
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Variables
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Missing Data On Critical Indicators
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Final data set: 72,000 records (-73%)
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LMS Use Consistent across Categories
Factor Analysis of LMS Use Categories
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3. FINDINGS
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Clear Trend: Grade w/Mean LMS Hits
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Question 1 Results: Correlation LMS Use w/Final Grade
Scatterplot of Assessment Activity
Hits vs. Course Grade
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Question 2 Results: Correlation: Student Char. w/Final Grade
Scatterplot of HS GPA vs.
Course Grade
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Conclusion: LMS Use Variables better Predictors than Student Characteristics
LMS Use
Variables
18% 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
>
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Smallest LMS Use Variable
(Administrative Activities)
r = 0.35
Largest Student
Characteristic
(HS GPA)
r = 0.31
>
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Combined Variables Regression Final Grade by LMS Use & Student Characteristic Variables
LMS Use
Variables
25% (r2=0.25)
Explanation of change in final grade
Student Characteristic
Variables
+10%(r2=0.35)
Explanation of change in final grade
>
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Question 3 Results:Regression by “At Risk” Population Subsamples
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At-Risk Students: “Over-Working Gap”
24
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Activities by Pell and Gradegrade / pelleligible
A B+ C C-
Pell-Eligible Not Pell-Eligible Pell-Eligible Not Pell-Eligible Pell-Eligible Not Pell-Eligible Pell-Eligible Not Pell-Eligible
0K
5K
10K
15K
20K
25K
30K
35K
Value
Content
Content
Engage
Engage
Assess
Assess
Admin
Admin
Content
Content
Engage
Engage
Assess
Assess
Admin
Content
Content
Engage
Engage
Assess
Assess
Content
Content Engage
Engage
Assess
Assess
Admin
Admin
Measure Names
Admin
Assess
Engage
Content
Extra effort in content-related activities
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4. CONCLUSIONS & NEXT STEPS
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Conclusions
1. 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.
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More Conclusions
4. 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!
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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)
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Feedback? Questions?
John Whitmer [email protected]
Slides
Complete monographhttp://bit.ly/15ijySP
Twitter: johncwhitmer