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Analysis and Prediction of Influencing Factors of College Student Achievement Based on Machine Learning
To effectively improve students’ performance and help educators monitor students’ learning situations, many colleges are committed to establishing systems that explore the influencing factors and predict student academic performance. However, because different colleges have different situations, the...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9072789/ https://www.ncbi.nlm.nih.gov/pubmed/35529577 http://dx.doi.org/10.3389/fpsyg.2022.881859 |
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author | Wang, Dongxuan Lian, Dapeng Xing, Yazhou Dong, Shiying Sun, Xinyu Yu, Jia |
author_facet | Wang, Dongxuan Lian, Dapeng Xing, Yazhou Dong, Shiying Sun, Xinyu Yu, Jia |
author_sort | Wang, Dongxuan |
collection | PubMed |
description | To effectively improve students’ performance and help educators monitor students’ learning situations, many colleges are committed to establishing systems that explore the influencing factors and predict student academic performance. However, because different colleges have different situations, the previous research results may not be applicable to ordinary Chinese colleges. This paper has two main objectives: to analyze the fluctuation of Chinese ordinary college student academic performance and to establish systems to predict performance. First, according to previous research results and the current situation of Chinese college students, a questionnaire was designed to collect data. Second, the chi-square test was used to analyze the contents of the questionnaire and identify the main features. Third, taking the main features as input, four classification prediction models are established by machine learning. Some traits of the students who did not pass all the examinations were also discovered. It might help student counselors and educators to take targeted measures. The experiment shows that the support vector machine classifier (SVC) model has the best and most stable effect. The average recall rate, precision rate, and accuracy rate reached 82.83%, 86.18%, and 80.96%, respectively. |
format | Online Article Text |
id | pubmed-9072789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90727892022-05-07 Analysis and Prediction of Influencing Factors of College Student Achievement Based on Machine Learning Wang, Dongxuan Lian, Dapeng Xing, Yazhou Dong, Shiying Sun, Xinyu Yu, Jia Front Psychol Psychology To effectively improve students’ performance and help educators monitor students’ learning situations, many colleges are committed to establishing systems that explore the influencing factors and predict student academic performance. However, because different colleges have different situations, the previous research results may not be applicable to ordinary Chinese colleges. This paper has two main objectives: to analyze the fluctuation of Chinese ordinary college student academic performance and to establish systems to predict performance. First, according to previous research results and the current situation of Chinese college students, a questionnaire was designed to collect data. Second, the chi-square test was used to analyze the contents of the questionnaire and identify the main features. Third, taking the main features as input, four classification prediction models are established by machine learning. Some traits of the students who did not pass all the examinations were also discovered. It might help student counselors and educators to take targeted measures. The experiment shows that the support vector machine classifier (SVC) model has the best and most stable effect. The average recall rate, precision rate, and accuracy rate reached 82.83%, 86.18%, and 80.96%, respectively. Frontiers Media S.A. 2022-04-22 /pmc/articles/PMC9072789/ /pubmed/35529577 http://dx.doi.org/10.3389/fpsyg.2022.881859 Text en Copyright © 2022 Wang, Lian, Xing, Dong, Sun and Yu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Wang, Dongxuan Lian, Dapeng Xing, Yazhou Dong, Shiying Sun, Xinyu Yu, Jia Analysis and Prediction of Influencing Factors of College Student Achievement Based on Machine Learning |
title | Analysis and Prediction of Influencing Factors of College Student Achievement Based on Machine Learning |
title_full | Analysis and Prediction of Influencing Factors of College Student Achievement Based on Machine Learning |
title_fullStr | Analysis and Prediction of Influencing Factors of College Student Achievement Based on Machine Learning |
title_full_unstemmed | Analysis and Prediction of Influencing Factors of College Student Achievement Based on Machine Learning |
title_short | Analysis and Prediction of Influencing Factors of College Student Achievement Based on Machine Learning |
title_sort | analysis and prediction of influencing factors of college student achievement based on machine learning |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9072789/ https://www.ncbi.nlm.nih.gov/pubmed/35529577 http://dx.doi.org/10.3389/fpsyg.2022.881859 |
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