Zhang Qi, Qian Yiming, Gao Shumiao, Liu Yufei, Shen Xinyu, Jiang Qing
School of Foreign Languages, Beijing Institute of Technology, Beijing 100081, China.
China Agricultural University, Beijing 100083, China.
Behav Sci (Basel). 2025 Mar 6;15(3):326. doi: 10.3390/bs15030326.
This study proposes a novel approach for analyzing learning behaviors in Chinese language education by integrating generative attention mechanisms and generative state transition equations. This method dynamically adjusts attention weights and models real-time changes in students' emotional and behavioral states, addressing key limitations of existing approaches. A central innovation is the introduction of a generative loss function, which jointly optimizes sentiment prediction and behavior analysis, enhancing the adaptability of the model to diverse learning scenarios. This study is based on empirical experiments involving student behavior tracking, sentiment analysis, and personalized learning path modeling. Experimental results demonstrate this method's effectiveness, achieving an accuracy of 90.6%, recall of 88.4%, precision of 89.3%, and F1-score of 88.8% in behavioral prediction tasks. Furthermore, this approach attains a learning satisfaction score of 89.2 with a 94.3% positive feedback rate, significantly outperforming benchmark models such as BERT, GPT-3, and T5. These findings validate the practical applicability and robustness of the proposed method, offering a structured framework for personalized teaching optimization and dynamic behavior modeling in Chinese language education.
本研究提出了一种通过整合生成式注意力机制和生成式状态转移方程来分析汉语教育中学习行为的新方法。该方法动态调整注意力权重，并对学生的情绪和行为状态的实时变化进行建模，解决了现有方法的关键局限性。一个核心创新点是引入了生成式损失函数，它联合优化情感预测和行为分析，增强了模型对不同学习场景的适应性。本研究基于涉及学生行为跟踪、情感分析和个性化学习路径建模的实证实验。实验结果证明了该方法的有效性，在行为预测任务中达到了90.6%的准确率、88.4%的召回率、89.3%的精确率和88.8%的F1分数。此外，该方法获得了89.2的学习满意度得分和94.3%的积极反馈率，显著优于BERT、GPT-3和T5等基准模型。这些发现验证了所提方法的实际适用性和稳健性，为汉语教育中的个性化教学优化和动态行为建模提供了一个结构化框架。
