Explainable Machine Learning for Career Path Prediction of Student Scholars Using Educational Data Mining

Abstract

Career motivation is influenced by a variety of academic experiences, family influences, socioeconomic factors, and individual characteristics. Understanding how these factors drive students' career decisions is important for developing effective career guidance programmes and enhancing graduate employability. This study uses an explainable machine learning method to predict the desired career paths of scholarship students at Occidental Mindoro State University, Philippines, based on Educational Data Mining (EDM) techniques. A total of 1,300 student scholars were surveyed, and the data included demographic, academic, socioeconomic, family, and career-development variables. Five supervised learning algorithms—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN)—were developed and compared using accuracy, precision, recall, F1-score, and a confusion matrix. Random Forest feature importance and SHapley Additive exPlanations (SHAP) were used to identify influential features and analyze feature interactions, thereby improving model interpretability. Random Forest was the best-performing model, with an accuracy of 97.31%, precision of 97.40%, recall of 97.31%, and F1-score of 97.29% among the evaluated models. The explainability analysis revealed that the factors most strongly associated with students’ career choices were academic program and parents’ career expectations; extracurricular activities, mathematics achievement, family income, and academic performance were also associated with the prediction results. The SHAP interaction results indicated meaningful interactions among academic variables, emphasizing the interrelated nature of career decision-making. The findings underscore the value of integrating predictive analytics and XAI to support transparent, data-informed career counselling and educational planning for higher education students. The proposed framework provides a practical tool to inform career preferences and student development programmes.