Tong Tingting, Li Zhen
School of Information Science and Technology, Northeast Normal University, Changchun, Jilin, China.
PLoS One. 2025 Jan 2;20(1):e0312124. doi: 10.1371/journal.pone.0312124. eCollection 2025.
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 learning achievement. The SHapley Additive exPlanation (SHAP) model is then employed to explain the prediction results. Through the experiments on XuetangX dataset, the effectiveness of the proposed model is verified. The proposed model outperforms traditional machine learning and deep learning model in terms of prediction accuracy. The results demonstrate that the ensemble learning-based predictive framework significantly outperforms traditional 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.
预测学习成绩是解决高辍学率问题的关键策略。然而，现有的预测模型往往存在偏差，限制了其准确性。此外，当前机器学习方法缺乏可解释性，限制了它们在教育中的实际应用。为了克服这些挑战，本研究结合了各种机器学习算法的优势，设计了一个在多个指标上表现良好的稳健模型，并使用可解释性分析来阐明预测结果。本研究引入了一种基于集成学习技术的学习成绩预测框架。具体而言，利用六种不同的机器学习模型建立一个基础学习器，以逻辑回归作为元学习器来构建一个预测学习成绩的集成模型。然后采用SHapley加性解释（SHAP）模型来解释预测结果。通过在学堂在线数据集上的实验，验证了所提模型的有效性。所提模型在预测准确性方面优于传统机器学习和深度学习模型。结果表明，基于集成学习的预测框架明显优于传统机器学习方法。通过特征重要性分析，SHAP方法提高了模型的可解释性，提高了预测结果的可靠性，从而能够进行更个性化的干预以支持学生。
