Employing markov networks on curriculum graphs to predict student 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 data mining and machine learning techniques, and in particular, linear regression and a Markov network (MN), 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 publicationProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
EditorsCesar Ferri, Guangzhi Qu, Xue-wen Chen, M. Arif Wani, Plamen Angelov, Jian-Huang Lai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages415-418
Number of pages4
ISBN (Electronic)9781479974153
DOIs
StatePublished - Feb 5 2014
Externally publishedYes
Event2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - Detroit, United States
Duration: Dec 3 2014Dec 6 2014

Publication series

NameProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

Conference

Conference2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
Country/TerritoryUnited States
CityDetroit
Period12/3/1412/6/14

Keywords

  • Markov Network
  • educational analytics
  • linear regression
  • student success

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

Fingerprint

Dive into the research topics of 'Employing markov networks on curriculum graphs to predict student performance'. Together they form a unique fingerprint.

Cite this