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Development of machine learning models to predict student performance in computer literacy courses

Anderson, George; Eyitayo, Oduronke T.

Authors

George Anderson

Oduronke T. Eyitayo



Abstract

This paper reports on a study carried out to build machine learning models for the purpose of predicting student performance in a second course of a two-course sequence, based on performance in various aspects of the first course’s exam. Detailed data is collected from the first course’s exam, which is a multiple choice exam, using an Optical Mark Recognition scanner. This data is then pre-processed and used to build machine learning models. Several machine learning models are experimented with, yielding excellent results for predicting Pass or Fail (greater than 93% accuracy), validating our hypothesis that our approach can be used to help students with preventative measures in order to increase pass rates in the courses concerned.

Journal Article Type Article
Online Publication Date Jun 30, 2018
Deposit Date Jun 11, 2024
Journal International Review on Computers and Software (IRECOS)
Print ISSN 1828-6003
Electronic ISSN 2533-1728
Peer Reviewed Peer Reviewed
Volume 13
Issue 1
DOI https://doi.org/10.15866/irecos.v13i1.16863
Public URL https://uwe-repository.worktribe.com/output/12032673