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Issues, Challenges, and Lessons Learned When Scaling Up a Learning Analytics Intervention
Steven Lonn@stevelonn
Stephen Aguilar@stephenaguilar
Stephanie D. Teasley@stephteasley
USE LabUniversity of Michigan
USE Labwww.umich.edu/~uselab
slideshare.net/stevelonn
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On the “road” to scale...
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Goal: Help “at risk” engineering students succeed
Problem: Advisors can’t help until it’s “too late”
Solution: Early warning system for advisors
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STUDENTEXPL RER
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STUDENTEXPL RER
How do we scale?
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New DatabaseExtract, Transform, and
Load (ETL)Presentation Layer
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Usability Gaps
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Calculation Gaps
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Access Gaps
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Performance Gaps
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☞ Crashing the LMS is not so good
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Automatization Gaps
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☞Advisor = Student / Cohort data needs to be added to data warehouse
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Alternate Routes
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Challenge(s) Possible Solution(s)
Institutional Governance & Resources
Benefits all partnersKeep communicating
Usability Gaps Flexibility & TrainingModifications where poss.
Calculation Gaps Nimble tweaksSoftware adjustments
Automatization Gaps New auto processes with new data fields
Access Gaps Work-aroundsData / Policy changes
Performance Gaps Accurate load testing w/peak scenarios
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Lessons fromOur Road
Lessons forYour Road?
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Acknowledgements
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Student Explorer TeamAmine BoudaliaAndrew KrummGierad LaputSungJin NamR. Joseph Waddington
M-STEM Academies Partners
LSA Academic File Partners
USE Labwww.umich.edu/~uselab
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Contacts
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Steven [email protected]@stevelonn
Stephen [email protected]@stephenaguilar
Stephanie D. [email protected]@stephteasley
USE Labwww.umich.edu/~uselab
Slides: slideshare.net/stevelonn