The Role of Study Habits, Parental Involvement, and School Environment in Predicting Student Achievement: A Machine Learning Perspective - Noviandy, Teuku Rizky; Paristiowati, Maria; Isa, Illyas Md; Idroes, Rinaldi (Journal of Educational Management and Learning, 2025)
This study explores the application of machine learning techniques to predict student achievement based on study habits, parental involvement, and school environment. Using a dataset from Kaggle comprising academic, behavioral, and contextual variables, four machine learning algorithms, namely K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machine (SVM), and Random Forest, were implemented and evaluated. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC curve, and Precision–Recall curves. Results show that all models effectively classified students into low- and high-achievement categories, with SVM achieving the highest accuracy (94.02%) and the strongest overall performance. The findings highlight the potential of machine learning-driven predictive analytics in educational settings, enabling early identification of at-risk students and supporting evidence-based interventions. By integrating diverse factors influencing academic performance, this study demonstrates how data-driven approaches can enhance educational management, inform policy, and promote equitable learning outcomes.