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The role of demographic and academic features in a student performance prediction

Educational Data Mining is widely used for predicting student's performance. It’s a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level...

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Autores principales: Bilal, Muhammad, Omar, Muhammad, Anwar, Waheed, Bokhari, Rahat H., Choi, Gyu Sang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307570/
https://www.ncbi.nlm.nih.gov/pubmed/35869103
http://dx.doi.org/10.1038/s41598-022-15880-6
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author Bilal, Muhammad
Omar, Muhammad
Anwar, Waheed
Bokhari, Rahat H.
Choi, Gyu Sang
author_facet Bilal, Muhammad
Omar, Muhammad
Anwar, Waheed
Bokhari, Rahat H.
Choi, Gyu Sang
author_sort Bilal, Muhammad
collection PubMed
description Educational Data Mining is widely used for predicting student's performance. It’s a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level of study, program offered, nature of subject, and geographical location. This study attempted to predict the final semester’s results of students studying Doctor of Veterinary Medicine (DVM) based on their pre-admission academic achievements, demographics, and first semester performance. The imbalanced data led to non-generic prediction models, so it was addressed through synthetic minority oversampling technique. Among five prediction models, the Support Vector Machine led the best with 92% accuracy. The decision tree model identified key features affecting students’ performance. The analysis led to the conclusion that marks obtained in Biology, Islamiat, and Urdu at Matric and English at Intermediate level affected the students’ performance in their final semester. The findings provide useful information to predict students’ performance and guidelines for academic institutes’ management regarding improving students’ achievement. It is speculated that adoption of digital transformation may help reduce difficulty faced in data collection and analysis.
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spelling pubmed-93075702022-07-24 The role of demographic and academic features in a student performance prediction Bilal, Muhammad Omar, Muhammad Anwar, Waheed Bokhari, Rahat H. Choi, Gyu Sang Sci Rep Article Educational Data Mining is widely used for predicting student's performance. It’s a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level of study, program offered, nature of subject, and geographical location. This study attempted to predict the final semester’s results of students studying Doctor of Veterinary Medicine (DVM) based on their pre-admission academic achievements, demographics, and first semester performance. The imbalanced data led to non-generic prediction models, so it was addressed through synthetic minority oversampling technique. Among five prediction models, the Support Vector Machine led the best with 92% accuracy. The decision tree model identified key features affecting students’ performance. The analysis led to the conclusion that marks obtained in Biology, Islamiat, and Urdu at Matric and English at Intermediate level affected the students’ performance in their final semester. The findings provide useful information to predict students’ performance and guidelines for academic institutes’ management regarding improving students’ achievement. It is speculated that adoption of digital transformation may help reduce difficulty faced in data collection and analysis. Nature Publishing Group UK 2022-07-22 /pmc/articles/PMC9307570/ /pubmed/35869103 http://dx.doi.org/10.1038/s41598-022-15880-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Bilal, Muhammad
Omar, Muhammad
Anwar, Waheed
Bokhari, Rahat H.
Choi, Gyu Sang
The role of demographic and academic features in a student performance prediction
title The role of demographic and academic features in a student performance prediction
title_full The role of demographic and academic features in a student performance prediction
title_fullStr The role of demographic and academic features in a student performance prediction
title_full_unstemmed The role of demographic and academic features in a student performance prediction
title_short The role of demographic and academic features in a student performance prediction
title_sort role of demographic and academic features in a student performance prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307570/
https://www.ncbi.nlm.nih.gov/pubmed/35869103
http://dx.doi.org/10.1038/s41598-022-15880-6
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