76. identifying at-risk students before it is late

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

Primary Student
Name: Soohyun Nam Liao
Email: snam@ucsd.edu
Phone: 858-842-7654
Grad Year: 2019

Abstract
As the labor demand increases in computer science, the student enrollment for computer science courses increases significantly these days. However, many students still fail an introductory computer science course and leave computer science major. Therefore, it is critical for computing education researchers to know who they are and to help them stay and succeed in computer science major. Prior work showed it is possible to predict at-risk students, but they either did not provide a specific prediction method, identify at-risk students too late, or are too onerous to implement. This work suggests an early, lightweight, and fine-grained identification method of at-risk students in a course within the early weeks of a term. Future work will explore possible intervention techniques to help the identified at-risk students before it is too late.

Industry Application Area(s)
Software, Analytics

Related Links:

  1. http://dl.acm.org/citation.cfm?id=2960315&CFID=723353193&CFTOKEN=74817453

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