Download - Bridging the Gap from Knowledge to Action: Putting Analytics in the Hands of Academic Advisors
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Bridging the Gap from Knowledge to Action: Putting Analytics in the Hands of Academic Advisors
Steven LonnAndrew Krumm
R. Joseph WaddingtonStephanie Teasley
University of Michiganwww.umich.edu/~uselab
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Research Setting:M-STEM Academy
• Undergraduate engineering mentoring program• Historically underrepresented students • 200 Engineering students in 4 cohorts
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Goals of the project
• Utilize data stored in campus learning management system to:
• Provide timely and targeted data on student performance to M-STEM mentors
• Shorten the timespan from problem identification to intervention
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Supporting M-STEM mentors
• Iteratively develop • Metrics for comparing
students using LMS data• Classification schemes• Visualizations of student
performances• Send mentors weekly updates
Photo%Credit:%h,p://teacherrogers.wordpress.com
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How does mentor’s use of EWS affect student outcomes?
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ImprovedPerformance
Analysis
Product
StudentAudience
Action
Student
Data
Mentor
academic resources, study strategies
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Communication
EWS
Classification
Face-to-Face / Email
ImprovedPerformance
Analysis
Product
StudentAudience
Action
Student
Data
Mentor
academic resources, study strategies
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Communication
EWS
Classification
Face-to-Face / Email
Assignments
Gradebook
“Presence”
Weekly%query%of%LMS%for%courses%that%include%an%MASTEM%student%and%use%the%Gradebook%or%Assignments%tool
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Measures from LMS data
• Gradebook and Assignments tools allow up-to-date tracking of student performances
• Report student-level information for M-STEM students• Percent of available points earned• Course averages (all students)
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• “Presence” events serve as a proxy for effort and are events common to all courses• Cumulative and week-to-week “Presence”
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Measures from LMS data
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75%
25%
mean
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75%
25%
mean
Cumulative “Presence” events can be highly
predictive for students’ final course grade performance
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Classification scheme• Absolute grade thresholds• Difference from course average • Presence cutoff
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Classification scheme• Absolute grade thresholds• Difference from course average • Presence cutoff
Comparisons• Grades (course average)• Percentile Ranks (presence)
Classification schemeStudent % Relative Distance Presence Percentile Rank E3
>=0.85 . . Encourage0.75<=X<0.85 <-0.15 . Explore0.75<=X<0.85 >=-0.15 <0.25 Explore0.75<=X<0.85 >=-0.15 >=0.25 Encourage0.65<=X<0.75 <-0.15 <0.25 Engage0.65<=X<0.75 <-0.15 >=0.25 Explore0.65<=X<0.75 >=-0.15 . Explore0.55<=X<0.65 >=-0.10 . Explore0.55<=X<0.65 <-0.10 . Engage<0.55 . . Engage
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Mentor summary
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Student Detail Report
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Benefits of EWS use
• Contacting students• Shortening time to intervention• Viewing longitudinal trends• By individual course• Across all courses
• Contextualizing M-STEM student performance
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How mentors use the EWS
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Next Steps
• New infrastructure• New versions• Instructor• Students
• Messaging system• Recommendations (from person, from system)
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ImprovedPerformance
Analysis
Product
StudentAudience
Action
Student
Data
Mentor
academic resources, study strategies
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Communication
EWS
Classification
Face-to-Face / Email
USE LabDigital Media Commons
USE LabDigital Media Commons
Conclusion• Closing the gap between problem identification and
intervention
• Organizational capacity and the success of learning analytics• “Analytics” is but a small part
• Information is always subject to interpretation• How can we scaffold interpretation and effective
action-taking?20
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Collaborators
M-STEM• Cinda-Sue Davis• Guy Meadows• James Holloway• Daryl Koch• Mark Jones• Debbie Taylor
ITS• Bryan Hartman• Jeff Jenkins• Dan Kiskis
USE Lab• Gierad Laput
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Questions
www.umich.edu/~uselab
slides: www.slideshare.net/stevelonn
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Steve [email protected] @stevelonnStephanie [email protected] @stephteasley
