Predicting student success based on prior performance

Ahmad Slim, Gregory L. Heileman, Jarred Kozlick, Chaouki T. Abdallah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

Colleges and universities are increasingly interested in tracking student progress as they monitor and work to improve their retention and graduation rates. Ideally, early indicators of student progress, or lack thereof, can be used to provide appropriate interventions that increase the likelihood of student success. In this paper we present a framework that uses machine learning, and in particular, a Bayesian Belief Network (BBN), to predict the performance of students early in their academic careers. The results obtained show that the proposed framework can predict student progress, specifically student grade point average (GPA) within the intended major, with minimal error after observing a single semester of performance. Furthermore, as additional performance is observed, the predicted GPA in subsequent semesters becomes increasingly accurate, providing the ability to advise students regarding likely success outcomes early in their academic careers.

Original languageEnglish (US)
Title of host publicationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014
Subtitle of host publication2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages410-415
Number of pages6
ISBN (Electronic)9781479945191
DOIs
StatePublished - Jan 13 2015
Externally publishedYes
Event5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014 - Orlando, United States
Duration: Dec 9 2014Dec 12 2014

Publication series

NameIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings

Conference

Conference5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014
Country/TerritoryUnited States
CityOrlando
Period12/9/1412/12/14

ASJC Scopus subject areas

  • Signal Processing
  • Artificial Intelligence
  • Information Systems
  • Software

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