Learner Analytics Panel Session: Deja-Vu all over again?
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Transcript of Learner Analytics Panel Session: Deja-Vu all over again?
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Panelists: Kathy Fernandes, James Frazee, Andrew Roderick & Deone Zell
Moderator: John Whitmer
DET/CHE 2012November 28, 2012
Learning Analytics Panel Session
D/L slides @ http://goo.gl/UgBYM
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Slides: http://goo.gl/UgBYM
IntroductionsKathy Fernandes
Director of Academic Technology, Chico State University
Director of CSU System-wide LMSS Project, CSU Office of the Chancellor
James Frazee
Director, Instructional Technology Services, San Diego State University
Andrew Roderick
Technology Development Manager, San Francisco State University
Deone Zell
Senior Director, Academic Technology, California State University, Northridge
John Whitmer
Associate Director System-wide LMSS Project, CSU Office of the Chancellor
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Slides: http://goo.gl/UgBYM
Outline
1. L.A. Context: Definition & Examples
2. Panelist Discussion
3. Audience Q & A
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Slides: http://goo.gl/UgBYM
Learning Analytics: Working Definition for Academic Technologists
Analysis of the relationship between the use of academic technology and student achievement– Uses data, frequently “big data”, disaggregated– Builds on (and moves beyond) usage reporting
to examine impact– Focused on making change
Isn’t a yes/no dichotomy; better seen as a spectrum
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EXAMPLES OF LEARNING ANALYTICS IN PRACTICE
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Chico State Case Study: RELS 180
• Redesigned to hybrid delivery through Academy eLearning
• Enrollment: 373 students (54% increase on largest section)
• Highest LMS 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
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Slides: http://goo.gl/UgBYM
Correlation: LMS Use w/Final Grade
Scatterplot of Assessment
Activity Hits vs. Course Grade
Statistically Significant (strong to weak) r % Variance Sign.Total Hits 0.48 23% 0.0000Assessment activity hits 0.47 22% 0.0000Content activity hits 0.41 17% 0.0000Engagement activity hits 0.40 16% 0.0000Administrative activity hits 0.35 12% 0.0000
Mean value all significant variables 18%
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Slides: http://goo.gl/UgBYM
Correlation: Student Char. w/Final Grade
Scatterplot of HS GPA vs.
Course Grade
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Slides: http://goo.gl/UgBYM
Most interesting finding (so far):
LMS Use
Variables
18%(Mean % change explained by any
single LMS variable)
Student Characteristic
Variables
4% (Mean % change explained by any
single S.C. variable)
>
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SIGNALS
http://www.itap.purdue.edu/studio/signals/Purdue Signals Project
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Wordcloud of student evaluations of Course Signals (Arnold, 2010)
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Slides: http://goo.gl/UgBYM
Signals Course Outcomes
Compare same course (w/Signals v. w/o Signals)-6.41% DWF
+10.97% A/B
(Arnold, 2010)
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PANEL QUESTIONS
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Q1: What strategic question(s) does your campus hope to answer with Learning Analytics?
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Q2: What are some example(s) of Learning Analytics that you have conducted on your campus, no matter how small the sample size? What’s worked? What hasn’t? Why?
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Q3: What should educational technologists do to make progress deploying Learning Analytics on their campuses?
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Slides: http://goo.gl/UgBYM
For More Information
Learning Analytics Resources Googledoc: http://goo.gl/Fwur6
Society for Learning Analytics Research: http://goo.gl/bH9ts
Educause Learning Analytics Library: http://goo.gl/UDRMx
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Slides: http://goo.gl/UgBYM
Contact Information
Kathy Fernandes ([email protected])
James Frazee ([email protected])
Andrew Roderick ([email protected])
Deone Zell ([email protected])
John Whitmer ([email protected])