Machine learning approach to student performance prediction of online learning.
Student performance is crucial for addressing learning process problems and is also an important factor in measuring learning outcomes. The ability to improve educational systems using data knowledge has driven the development of the field of educational data mining research. Here, this paper propos...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0299018 |
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| _version_ | 1832540240671145984 |
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| author | Jing Wang Yun Yu |
| author_facet | Jing Wang Yun Yu |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | Student performance is crucial for addressing learning process problems and is also an important factor in measuring learning outcomes. The ability to improve educational systems using data knowledge has driven the development of the field of educational data mining research. Here, this paper proposes a machine learning method for the prediction of student performance based on online learning. The critical thought is that eleven learning behavioral indicators are constructed according to online learning process, following that, through analyzing the correlation between the eleven learning behavioral indicators and the scores obtained by students online learning, we filter out those learning behavioral indicators that are weakly correlated with student scores, meanwhile, retain these learning behavior indicators being strongly correlated with student scores, which are used as the eigenvalue indicators. Finally, using the eigenvalue indicators to train the proposed logistic regress model with Taylor expansion. Experimental results show that the proposed logistic regress model defeats against the comparative models in prediction ability. Results also indicate that there is a significant dependency between students' initiative in learning and learning duration, nevertheless, learning duration has a significant effect on the prediction of student performance. |
| format | Article |
| id | doaj-art-3a1f6b87b316495b8d3b12b694a40ff2 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3a1f6b87b316495b8d3b12b694a40ff22025-02-05T05:31:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e029901810.1371/journal.pone.0299018Machine learning approach to student performance prediction of online learning.Jing WangYun YuStudent performance is crucial for addressing learning process problems and is also an important factor in measuring learning outcomes. The ability to improve educational systems using data knowledge has driven the development of the field of educational data mining research. Here, this paper proposes a machine learning method for the prediction of student performance based on online learning. The critical thought is that eleven learning behavioral indicators are constructed according to online learning process, following that, through analyzing the correlation between the eleven learning behavioral indicators and the scores obtained by students online learning, we filter out those learning behavioral indicators that are weakly correlated with student scores, meanwhile, retain these learning behavior indicators being strongly correlated with student scores, which are used as the eigenvalue indicators. Finally, using the eigenvalue indicators to train the proposed logistic regress model with Taylor expansion. Experimental results show that the proposed logistic regress model defeats against the comparative models in prediction ability. Results also indicate that there is a significant dependency between students' initiative in learning and learning duration, nevertheless, learning duration has a significant effect on the prediction of student performance.https://doi.org/10.1371/journal.pone.0299018 |
| spellingShingle | Jing Wang Yun Yu Machine learning approach to student performance prediction of online learning. PLoS ONE |
| title | Machine learning approach to student performance prediction of online learning. |
| title_full | Machine learning approach to student performance prediction of online learning. |
| title_fullStr | Machine learning approach to student performance prediction of online learning. |
| title_full_unstemmed | Machine learning approach to student performance prediction of online learning. |
| title_short | Machine learning approach to student performance prediction of online learning. |
| title_sort | machine learning approach to student performance prediction of online learning |
| url | https://doi.org/10.1371/journal.pone.0299018 |
| work_keys_str_mv | AT jingwang machinelearningapproachtostudentperformancepredictionofonlinelearning AT yunyu machinelearningapproachtostudentperformancepredictionofonlinelearning |