EHCFE: Enhanced Hierarchical Clustering with Feature Engineering for Automating Labeling of Student Performance and Dropout Prediction

Educational success is a critical component of societal development, yet increasing student dropout rates present ongoing challenges. While supervised learning models are commonly used for dropout prediction, they rely on manually labeled data, a process that is time-consuming and dependent on expert annotation. Unsupervised learning models, clustering approaches, have been explored as an alternative; however, existing methods typically group students based on activity patterns without generating binary outcome labels such as dropout or success. Furthermore, their effectiveness often depends heavily on the quality of the selected features, and most current solutions utilize only limited or pre-structured subsets of institutional data. This paper addresses these challenges and proposes EHCFE (Enhanced Hierarchical Clustering with Feature Engineering), to automatically generate binary labels from unlabeled educational datasets. EHCFE applies feature engineering by generating new features from the top-ranked features identified during feature selection while retaining the original feature set, thereby improving the quality of the labeling outcomes. The approach is evaluated on three datasets and compared with current and state-of-the-art models using several evaluation metrics, including F1 score, area under the receiver operating characteristic curve (AUC), and silhouette coefficient. Experimental results show that EHCFE achieves the highest F1 score (0.709 and 0.28) and AUC values (0.766 and 0.81) on two datasets. A ranking analysis across six evaluation metrics demonstrates that EHCFE outperforms existing models, achieving the highest average ranks of 1.50 and 1.83 on two datasets and a competitive rank of 1.92 on the third. المزيد