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 ass... More >

In educational data mining, accurate prediction of student performance is important for supporting timely intervention for at-risk students. However, educational datasets often include irrelevant or redundant features that could reduce the performance of prediction models. To tackle this issue, this study proposes a gradient boosting-based feature selection framework that can automatically identify and obtain the most important features for student performance prediction. The proposed framework leverages the gradient boosting model to calculate feature importance and refine the feature subset, aiming to achieve comparable or superior prediction performance using fewer but important input fea... More >

To address the student performance problem in educational data mining, this study proposes a stacking-based RF-CatBoost model that integrates the complementary strengths of ensemble learning methods to enhance prediction accuracy and robustness. In the proposed framework, Random Forest (RF) and CatBoost are employed as the base learners to capture both global feature interactions and complex non-linear relationships within multi-source educational data. Their outputs are then stacked and fused using a combination strategy to generate the final prediction. Experimental results based on two educational datasets demonstrate that the stacking-based RF-CatBoost model consistently achieves superio... More >

Student performance prediction represents a core task in educational data mining, facilitating early interventions, personalized learning support, and data-driven decision-making. While machine learning models have demonstrated strong predictive capabilities in this domain, their effectiveness remains constrained by hyperparameter selection. To overcome this limitation, we introduce an automated hyperparameter optimization framework that leverages the jellyfish search optimizer to identify optimal configurations. To mitigate the variability introduced by data partitioning, we adopt 10-fold cross-validation with 10 repeated trials. Experimental results indicate that the proposed framework sig... More >

This editorial presents the motivations underlying the establishment of the ICCK Transactions on Educational Data Mining (TEDM), an international, peer-reviewed journal dedicated to advancing theoretical, methodological, and applied research in Educational Data Mining (EDM). The journal is conceived as a platform to bring together researchers, educators, and practitioners from diverse disciplines, fostering cross-disciplinary dialogue and innovation in data-driven educational research. In particular, this editorial introduces the journal's objectives and scope, outlines representative techniques and methodological approaches employed in EDM, and highlights key trends, challenges, and opportu... More >