Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods
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| Formato: | Artículo científico |
| Lenguaje: | en |
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Universidad Distrital Francisco José de Caldas
2023
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| _version_ | 1876465865612853248 |
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| author | Leonardo Emiro Contreas-Bravo |
| author_facet | Leonardo Emiro Contreas-Bravo |
| contents | Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods Leonardo Emiro Contreas-Bravo Nayive Nieves-Pimiento Karolina González Guerrero Ingeniería Machine Learning higher education Educational data analysis Context: In the education sector, variables have been identified which considerably affect students’ academic performance. In the last decade, research has been carried out from various fields such as psychology, statistics, and data analytics in order to predict academic performance. Method: Data analytics, especially through Machine Learning tools, allows predicting academic performance using supervised learning algorithms based on academic, demographic, and sociodemographic variables. In this work, the most influential variables in the course of students’ academic life are selected through wrapping, embedded, filter, and assembly methods, as well as the most important characteristics semester by semester using Machine Learning algorithms (Decision Trees, KNN, SVC, Naive Bayes, LDA), which were implemented using the Python language. Results: The results of the study show that the KNN is the model that best predicts academic performance for each of the semesters, followed by Decision Trees, with precision values that oscillate around 80 and 78,5% in some semesters. Conclusions: Regarding the variables, it cannot be said that a student’s per-semester academic average necessarily influences the prediction of academic performance for the next semester. The analysis of these results indicates that the prediction of academic performance using Machine Learning tools is a promising approach that can help improve students’ academic life allow institutions and teachers to take actions that contribute to the teaching-learning process. 2023 artículo científico 0121-750X https://www.redalyc.org/articulo.oa?id=498875016004 https://www.redalyc.org/journal/4988/498875016004/ https://www.redalyc.org/journal/4988/498875016004/html/ https://www.redalyc.org/journal/4988/498875016004/498875016004.epub https://www.redalyc.org/journal/4988/498875016004/movil 10.14483/23448393.19514 en http://www.redalyc.org/revista.oa?id=4988 Ingeniería application/pdf Universidad Distrital Francisco José de Caldas Ingeniería (Colombia) Num.1 Vol.28 |
| format | Artículo científico |
| id | redalyc_498875016004 |
| institution | Redalyc |
| language | en |
| publishDate | 2023 |
| publisher | Universidad Distrital Francisco José de Caldas |
| spellingShingle | Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods Leonardo Emiro Contreas-Bravo Ingeniería Machine Learning higher education Educational data analysis Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods Leonardo Emiro Contreas-Bravo Nayive Nieves-Pimiento Karolina González Guerrero Ingeniería Machine Learning higher education Educational data analysis Context: In the education sector, variables have been identified which considerably affect students’ academic performance. In the last decade, research has been carried out from various fields such as psychology, statistics, and data analytics in order to predict academic performance. Method: Data analytics, especially through Machine Learning tools, allows predicting academic performance using supervised learning algorithms based on academic, demographic, and sociodemographic variables. In this work, the most influential variables in the course of students’ academic life are selected through wrapping, embedded, filter, and assembly methods, as well as the most important characteristics semester by semester using Machine Learning algorithms (Decision Trees, KNN, SVC, Naive Bayes, LDA), which were implemented using the Python language. Results: The results of the study show that the KNN is the model that best predicts academic performance for each of the semesters, followed by Decision Trees, with precision values that oscillate around 80 and 78,5% in some semesters. Conclusions: Regarding the variables, it cannot be said that a student’s per-semester academic average necessarily influences the prediction of academic performance for the next semester. The analysis of these results indicates that the prediction of academic performance using Machine Learning tools is a promising approach that can help improve students’ academic life allow institutions and teachers to take actions that contribute to the teaching-learning process. 2023 artículo científico 0121-750X https://www.redalyc.org/articulo.oa?id=498875016004 https://www.redalyc.org/journal/4988/498875016004/ https://www.redalyc.org/journal/4988/498875016004/html/ https://www.redalyc.org/journal/4988/498875016004/498875016004.epub https://www.redalyc.org/journal/4988/498875016004/movil 10.14483/23448393.19514 en http://www.redalyc.org/revista.oa?id=4988 Ingeniería application/pdf Universidad Distrital Francisco José de Caldas Ingeniería (Colombia) Num.1 Vol.28 |
| title | Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods |
| topic | Ingeniería Machine Learning higher education Educational data analysis |
| url | https://www.redalyc.org/articulo.oa?id=498875016004 https://www.redalyc.org/journal/4988/498875016004/ https://www.redalyc.org/journal/4988/498875016004/html/ https://www.redalyc.org/journal/4988/498875016004/498875016004.epub https://www.redalyc.org/journal/4988/498875016004/movil |