Prediction of graduation delay based on student performance

Tushar Ojha, Gregory L. Heileman, Manel Martinez-Ramon, Ahmad Slim

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

9 Scopus citations

Abstract

Numerous factors may impact a student's ability to succeed and ultimately graduate, including pre-university preparation, as well as the student support services provided by a university. In this work we study and analyze the impact of such factors on the graduation rates of a university using three predictive models: Support Vector Machines (SVMs), Gaussian Processes (GPs) and Deep Boltzmann Machines (DBMs). We train those models using actual student data. In particular, we used high school GPA, ACT score, gender and ethnicity as the main feature set for training those models. The results show that the DBMs edges out SVMs and GPs in some regards, which has been discussed in detail in the paper, although the difference in performance among the models is negligible with respect to overall accuracies obtained.

Original languageEnglish (US)
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3454-3460
Number of pages7
ISBN (Electronic)9781509061815
DOIs
StatePublished - Jun 30 2017
Externally publishedYes
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: May 14 2017May 19 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Other

Other2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States
CityAnchorage
Period5/14/175/19/17

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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