Using machine learning to predict factors affecting academic performance: the case of college students on academic probation.
Academic under probation
Data Mining
Education Data Mining
Higher education
Oman
Predictive models
Student Academic performance
Supervised learning
Journal
Education and information technologies
ISSN: 1360-2357
Titre abrégé: Educ Inf Technol (Dordr)
Pays: Netherlands
ID NLM: 101705199
Informations de publication
Date de publication:
10 Mar 2023
10 Mar 2023
Historique:
received:
30
05
2022
accepted:
27
02
2023
pubmed:
26
6
2023
medline:
26
6
2023
entrez:
26
6
2023
Statut:
aheadofprint
Résumé
This study aims to employ the supervised machine learning algorithms to examine factors that negatively impacted academic performance among college students on probation (underperforming students). We used the Knowledge Discovery in Databases (KDD) methodology on a sample of
Identifiants
pubmed: 37361752
doi: 10.1007/s10639-023-11700-0
pii: 11700
pmc: PMC9999331
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1-26Informations de copyright
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Déclaration de conflit d'intérêts
Conflict of interestNone