ORIGINAL RESEARCH article
Front. Psychol.
Sec. Educational Psychology
HTM-MDICE: A Transformer-Based Model for Predicting Student Engagement and Ideological Understanding in Ethical Education
Provisionally accepted
- Shandong Sport University, Jinan, China
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Tailoring individualized learning experiences depends on predicting student involvement and ideological awareness in ethical education, which is still difficult given educational data sets' complexity and class imbalance. HTM-MDICE, a new Transformer-based model meant to solve these issues by using hierarchical temporal modeling on a multi-modal ethical dataset of 68,200 scenarios, 1,000,000 numerical data points, and 500,000 behavioral logs, is presented in this paper. HTM-MDICE, utilizing a thorough evaluation framework, obtained a validation accuracy of 97.5%, an F1-score of 0.96, and an MAE of 0.12 with an early stopping patience of 5, therefore greatly outperforming four previous techniques—BSA-ANN, Decision Tree, BPNN, Petri Nets 10.5% in accuracy (p-values < 0.05). While preprocessing, early stopping, and the Transformer design were shown to be major factors in HTM-MDICE's performance, statistical analysis using paired t-tests verified the strength of its enhancements. Though it has improved, ethical issues around misclassification and data privacy call for prudent use. With future goals comprising improved interpretability, varied data integration, and longitudinal effect studies to further promote individualized education, this study adds a state-of-the-art model and assessment approach to educational predictive modeling.
Keywords: Individualized learning, ethical education, Transformer model, Predictive Modeling, student
Received: 07 Jun 2025; Accepted: 30 Oct 2025.
Copyright: © 2025 Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chang Qin, qinchang@sdpei.edu.cn
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