ICCK Transactions on Educational Data Mining
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TY - JOUR AU - Wang, Chenxi AU - Yao, Shuilin PY - 2025 DA - 2025/12/25 TI - Enhancing Student Dropout and Academic Success Prediction Using Machine Learning and Over-sampling Techniques JO - ICCK Transactions on Educational Data Mining T2 - ICCK Transactions on Educational Data Mining JF - ICCK Transactions on Educational Data Mining VL - 1 IS - 1 SP - 36 EP - 43 DO - 10.62762/TEDM.2025.732573 UR - https://www.icck.org/article/abs/TEDM.2025.732573 KW - student dropout prediction KW - over-sampling KW - SMOTE KW - ADASYN KW - academic success AB - Predicting student dropout and academic success is important for higher education institutions for enhancing retention and deliver timely interventions. However, educational datasets often exhibit severe class imbalance, particularly when multiple academic outcomes (i.e., dropout, enrolled, and graduate) are considered simultaneously. Thus this study examines the effectiveness of three widely used over-sampling techniques (i.e., RandomOverSampler, synthetic minority oversampling technique, and adaptive synthetic sampling) for mitigating class imbalance and enhancing prediction performance. These sampling strategies are evaluated in combination with several machine learning classifiers to assess their influence on accuracy of minority-class detection. The experimental results show that appropriate over-sampling substantially improves model performance, especially for the minority categories. The findings highlight the critical role of imbalance-handling techniques in educational data mining and offer practical insights for institutions seeking to build robust early-warning systems. SN - pending PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Wang2025Enhancing,
author = {Chenxi Wang and Shuilin Yao},
title = {Enhancing Student Dropout and Academic Success Prediction Using Machine Learning and Over-sampling Techniques},
journal = {ICCK Transactions on Educational Data Mining},
year = {2025},
volume = {1},
number = {1},
pages = {36-43},
doi = {10.62762/TEDM.2025.732573},
url = {https://www.icck.org/article/abs/TEDM.2025.732573},
abstract = {Predicting student dropout and academic success is important for higher education institutions for enhancing retention and deliver timely interventions. However, educational datasets often exhibit severe class imbalance, particularly when multiple academic outcomes (i.e., dropout, enrolled, and graduate) are considered simultaneously. Thus this study examines the effectiveness of three widely used over-sampling techniques (i.e., RandomOverSampler, synthetic minority oversampling technique, and adaptive synthetic sampling) for mitigating class imbalance and enhancing prediction performance. These sampling strategies are evaluated in combination with several machine learning classifiers to assess their influence on accuracy of minority-class detection. The experimental results show that appropriate over-sampling substantially improves model performance, especially for the minority categories. The findings highlight the critical role of imbalance-handling techniques in educational data mining and offer practical insights for institutions seeking to build robust early-warning systems.},
keywords = {student dropout prediction, over-sampling, SMOTE, ADASYN, academic success},
issn = {pending},
publisher = {Institute of Central Computation and Knowledge}
}
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