Machine Learning Based Engagement Prediction for Online Courses
Within the constraints of the epidemic, the demand for distance learning in education is growing rapidly, and technological advances are opening up new possibilities for online education. This study investigates the performance of three machine learning models (decision trees. SVMs, and random fores...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04014.pdf |
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| author | Wang Wanning |
| author_facet | Wang Wanning |
| author_sort | Wang Wanning |
| collection | DOAJ |
| description | Within the constraints of the epidemic, the demand for distance learning in education is growing rapidly, and technological advances are opening up new possibilities for online education. This study investigates the performance of three machine learning models (decision trees. SVMs, and random forests) in predicting online course participation. To ensure the accuracy and generalizability of the results, the paper evaluated the models using k-fold cross-validation. Performance metrics such as accuracy, precision, recall and F1 score were used for comparison. The results show that the Random Forest model outperforms the other models on all metrics while the SVM model performs the weakest among the three models. Therefore, this study conducted a feature importance analysis specifically for the decision tree and random forest models to gain insight into the predictive power of individual features. This helps educators and course designers to develop strategies to improve engagement and retention. In summary, this study emphasizes the effectiveness of random forests in predicting engagement in online courses and highlights the potential of machine learning in improving the quality of e-learning environments. The findings can help optimize ongoing online education discussions and can guide future research in the field of e-learning. |
| format | Article |
| id | doaj-art-916210bd1aa34f75be5ad3f180a97814 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-916210bd1aa34f75be5ad3f180a978142025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700401410.1051/itmconf/20257004014itmconf_dai2024_04014Machine Learning Based Engagement Prediction for Online CoursesWang Wanning0Qingdao University of Science and Technology, Department of Information Sciences and TechnologyWithin the constraints of the epidemic, the demand for distance learning in education is growing rapidly, and technological advances are opening up new possibilities for online education. This study investigates the performance of three machine learning models (decision trees. SVMs, and random forests) in predicting online course participation. To ensure the accuracy and generalizability of the results, the paper evaluated the models using k-fold cross-validation. Performance metrics such as accuracy, precision, recall and F1 score were used for comparison. The results show that the Random Forest model outperforms the other models on all metrics while the SVM model performs the weakest among the three models. Therefore, this study conducted a feature importance analysis specifically for the decision tree and random forest models to gain insight into the predictive power of individual features. This helps educators and course designers to develop strategies to improve engagement and retention. In summary, this study emphasizes the effectiveness of random forests in predicting engagement in online courses and highlights the potential of machine learning in improving the quality of e-learning environments. The findings can help optimize ongoing online education discussions and can guide future research in the field of e-learning.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04014.pdf |
| spellingShingle | Wang Wanning Machine Learning Based Engagement Prediction for Online Courses ITM Web of Conferences |
| title | Machine Learning Based Engagement Prediction for Online Courses |
| title_full | Machine Learning Based Engagement Prediction for Online Courses |
| title_fullStr | Machine Learning Based Engagement Prediction for Online Courses |
| title_full_unstemmed | Machine Learning Based Engagement Prediction for Online Courses |
| title_short | Machine Learning Based Engagement Prediction for Online Courses |
| title_sort | machine learning based engagement prediction for online courses |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04014.pdf |
| work_keys_str_mv | AT wangwanning machinelearningbasedengagementpredictionforonlinecourses |