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Session:
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Machine Learning in Physics and the Physics Curriculum
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Paper Type:
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Contributed
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Title:
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Statistical Modeling and Machine Learning Techniques for Predicting Student Outcomes
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Meeting:
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2019 Summer Meeting: Provo, UT |
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Location:
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N/A |
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Date:
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Time:
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1:00PM
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Author:
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Devyn Elizabeth Shafer
University of Illinois at Urbana-Champaign
3038750788, deshafe2@illinois.edu
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Co-Author(s):
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None
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Abstract:
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New machine learning techniques may offer insight into complex data that violates assumptions of standard regression methods. I will describe and compare several methods used to analyze course-level and institution-level data from the University of Illinois at Urbana-Champaign with the goal of predicting outcomes such as student performance in courses and retention in the engineering program.
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Footnotes:
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None
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Presentation:
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AAPT Talk V3.pdf
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