identify and help at-risk students before it is too late

Department: Computer Science & Engineering
Faculty Advisor(s): William G. Griswold | Leonard E. Porter

Primary Student
Name: Soohyun Nam Liao
Phone: 858-842-7654
Grad Year: 2019

Student Collaborators
Sander Valster,

Identifying at-risk students early in the term is valuable. It is because an instructor can have more time to provide extra support, and students can also estimate how much extra effort they should put on to succeed in class. Prior work showed it is possible to predict at-risk students, but they either did not provide a specific prediction method or are too onerous to implement. Thus, our work developed and evaluated more robust, universal, and simple prediction methodology to classify at-risk students. We are currently investigating possible reasons why students struggle in class through in-person interviews. We believe the interview analysis will help our model improve and design proper intervention strategies.

Industry Application Area(s)
Software, Analytics

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