Predicting Learning Achievement Using Ensemble Learning with Result Explanation

Abstract Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. This study introduces a predictive framework for learning achievement based on ensemble learning techniques. Specifically, six distinct machine learning models are utilized to establish a base learner, with logistic regression serving as the meta-learner to construct an ensemble model for predicting academic performance. The SHapley Additive exPlanation (SHAP) model is then employed to explain the prediction results. The model’s accuracy was validated on classification tasks. The results demonstrate that the ensemble learning-based predictive framework significantly outperforms conventional machine learning methods. Through feature importance analysis, the SHAP method enhances model interpretability and improves the reliability of the prediction results, enabling more personalized interventions to support students.