E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors
This study focused on predicting at-risk groups of students at the Open University (OU), a UK university that offers distance-learning courses and adult education. The research was conducted by drawing on publicly available data provided by the Open University for the year 2013–2014. The semester’s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Education Sciences |
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| Online Access: | https://www.mdpi.com/2227-7102/13/11/1130 |
| _version_ | 1797459549928554496 |
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| author | Chenglong Zhang Hyunchul Ahn |
| author_facet | Chenglong Zhang Hyunchul Ahn |
| author_sort | Chenglong Zhang |
| collection | DOAJ |
| description | This study focused on predicting at-risk groups of students at the Open University (OU), a UK university that offers distance-learning courses and adult education. The research was conducted by drawing on publicly available data provided by the Open University for the year 2013–2014. The semester’s time series was considered, and data from previous semesters were used to predict the current semester’s results. Each course was predicted separately so that the research reflected reality as closely as possible. Three different methods for selecting training data were listed. Since the at-risk prediction results needed to be provided to the instructor every week, four representative time points during the semester were chosen to assess the predictions. Furthermore, we used eight single and three integrated machine-learning algorithms to compare the prediction results. The results show that using the same semester code course data for training saved prediction calculation time and improved the prediction accuracy at all time points. In week 16, predictions using the algorithms with the voting classifier method showed higher prediction accuracy and were more stable than predictions using a single algorithm. The prediction accuracy of this model reached 81.2% for the midterm predictions and 84% for the end-of-semester predictions. Finally, the study used the Shapley additive explanation values to explore the main predictor variables of the prediction model. |
| first_indexed | 2024-03-09T16:52:57Z |
| format | Article |
| id | doaj.art-a12cc339a27c4fe0adad043ff9390ebf |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-09T16:52:57Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-a12cc339a27c4fe0adad043ff9390ebf2023-11-24T14:38:40ZengMDPI AGEducation Sciences2227-71022023-11-011311113010.3390/educsci13111130E-Learning at-Risk Group Prediction Considering the Semester and Realistic FactorsChenglong Zhang0Hyunchul Ahn1College of Business Administration, Kookmin University, Seoul 02707, Republic of KoreaGraduate School of Business IT, Kookmin University, Seoul 02707, Republic of KoreaThis study focused on predicting at-risk groups of students at the Open University (OU), a UK university that offers distance-learning courses and adult education. The research was conducted by drawing on publicly available data provided by the Open University for the year 2013–2014. The semester’s time series was considered, and data from previous semesters were used to predict the current semester’s results. Each course was predicted separately so that the research reflected reality as closely as possible. Three different methods for selecting training data were listed. Since the at-risk prediction results needed to be provided to the instructor every week, four representative time points during the semester were chosen to assess the predictions. Furthermore, we used eight single and three integrated machine-learning algorithms to compare the prediction results. The results show that using the same semester code course data for training saved prediction calculation time and improved the prediction accuracy at all time points. In week 16, predictions using the algorithms with the voting classifier method showed higher prediction accuracy and were more stable than predictions using a single algorithm. The prediction accuracy of this model reached 81.2% for the midterm predictions and 84% for the end-of-semester predictions. Finally, the study used the Shapley additive explanation values to explore the main predictor variables of the prediction model.https://www.mdpi.com/2227-7102/13/11/1130at-risk predictiondropout predictionOULADvoting classifierSHAP |
| spellingShingle | Chenglong Zhang Hyunchul Ahn E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors Education Sciences at-risk prediction dropout prediction OULAD voting classifier SHAP |
| title | E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors |
| title_full | E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors |
| title_fullStr | E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors |
| title_full_unstemmed | E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors |
| title_short | E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors |
| title_sort | e learning at risk group prediction considering the semester and realistic factors |
| topic | at-risk prediction dropout prediction OULAD voting classifier SHAP |
| url | https://www.mdpi.com/2227-7102/13/11/1130 |
| work_keys_str_mv | AT chenglongzhang elearningatriskgrouppredictionconsideringthesemesterandrealisticfactors AT hyunchulahn elearningatriskgrouppredictionconsideringthesemesterandrealisticfactors |