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The impact of engineering students' performance in the first three years on their graduation result using educational data mining
Research studies on educational data mining are on the increase due to the benefits obtained from the knowledge acquired from machine learning processes which help to improve decision making processes in higher institutions of learning. In this study, predictive analysis was carried out to determine...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6395785/ https://www.ncbi.nlm.nih.gov/pubmed/30886917 http://dx.doi.org/10.1016/j.heliyon.2019.e01250 |
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author | Adekitan, Aderibigbe Israel Salau, Odunayo |
author_facet | Adekitan, Aderibigbe Israel Salau, Odunayo |
author_sort | Adekitan, Aderibigbe Israel |
collection | PubMed |
description | Research studies on educational data mining are on the increase due to the benefits obtained from the knowledge acquired from machine learning processes which help to improve decision making processes in higher institutions of learning. In this study, predictive analysis was carried out to determine the extent to which the fifth year and final Cumulative Grade Point Average (CGPA) of engineering students in a Nigerian University can be determined using the program of study, the year of entry and the Grade Point Average (GPA) for the first three years of study as inputs into a Konstanz Information Miner (KNIME) based data mining model. Six data mining algorithms were considered, and a maximum accuracy of 89.15% was achieved. The result was verified using both linear and pure quadratic regression models, and R(2) values of 0.955 and 0.957 were recorded for both cases. This creates an opportunity for identifying students that may graduate with poor results or may not graduate at all, so that early intervention may be deployed. |
format | Online Article Text |
id | pubmed-6395785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-63957852019-03-18 The impact of engineering students' performance in the first three years on their graduation result using educational data mining Adekitan, Aderibigbe Israel Salau, Odunayo Heliyon Article Research studies on educational data mining are on the increase due to the benefits obtained from the knowledge acquired from machine learning processes which help to improve decision making processes in higher institutions of learning. In this study, predictive analysis was carried out to determine the extent to which the fifth year and final Cumulative Grade Point Average (CGPA) of engineering students in a Nigerian University can be determined using the program of study, the year of entry and the Grade Point Average (GPA) for the first three years of study as inputs into a Konstanz Information Miner (KNIME) based data mining model. Six data mining algorithms were considered, and a maximum accuracy of 89.15% was achieved. The result was verified using both linear and pure quadratic regression models, and R(2) values of 0.955 and 0.957 were recorded for both cases. This creates an opportunity for identifying students that may graduate with poor results or may not graduate at all, so that early intervention may be deployed. Elsevier 2019-02-26 /pmc/articles/PMC6395785/ /pubmed/30886917 http://dx.doi.org/10.1016/j.heliyon.2019.e01250 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Adekitan, Aderibigbe Israel Salau, Odunayo The impact of engineering students' performance in the first three years on their graduation result using educational data mining |
title | The impact of engineering students' performance in the first three years on their graduation result using educational data mining |
title_full | The impact of engineering students' performance in the first three years on their graduation result using educational data mining |
title_fullStr | The impact of engineering students' performance in the first three years on their graduation result using educational data mining |
title_full_unstemmed | The impact of engineering students' performance in the first three years on their graduation result using educational data mining |
title_short | The impact of engineering students' performance in the first three years on their graduation result using educational data mining |
title_sort | impact of engineering students' performance in the first three years on their graduation result using educational data mining |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6395785/ https://www.ncbi.nlm.nih.gov/pubmed/30886917 http://dx.doi.org/10.1016/j.heliyon.2019.e01250 |
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