Interpretable Ensemble Learning Approach for Predicting Student Adaptability in Online Education Environments
The COVID-19 pandemic has accelerated the shift towards online education, making it a critical focus for educational institutions. Understanding students’ adaptability to this new learning environment is crucial for ensuring their academic success. This study aims to predict students’ adaptability l...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Knowledge |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-9585/5/2/10 |
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| Summary: | The COVID-19 pandemic has accelerated the shift towards online education, making it a critical focus for educational institutions. Understanding students’ adaptability to this new learning environment is crucial for ensuring their academic success. This study aims to predict students’ adaptability levels in online education using a dataset of 1205 observations that incorporates sociodemographic factors and information collected across different educational levels (school, college, and university). Various machine learning (ML) and deep learning (DL) models, including decision tree (DT), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), XGBoost, and artificial neural networks (ANNs), are applied for adaptability prediction. The proposed ensemble model achieves superior performance with 95.73% accuracy, significantly outperforming traditional ML and DL models. Furthermore, explainable AI (XAI) techniques, such as LIME and SHAP, were employed to uncover the specific features that significantly impact the adaptability level predictions, with financial condition, class duration, and network type emerging as key factors. By combining robust predictive modeling and interpretable AI, this study contributes to the ongoing efforts to enhance the effectiveness of online education and foster student success in the digital age. |
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| ISSN: | 2673-9585 |