Behavioral Dynamics Analysis in Language Education: Generative State Transitions and Attention Mechanisms
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 beha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Behavioral Sciences |
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| Online Access: | https://www.mdpi.com/2076-328X/15/3/326 |
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| author | Qi Zhang Yiming Qian Shumiao Gao Yufei Liu Xinyu Shen Qing Jiang |
| author_facet | Qi Zhang Yiming Qian Shumiao Gao Yufei Liu Xinyu Shen Qing Jiang |
| author_sort | Qi Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ac20ff11f72747bfa105b57534e03003 |
| institution | DOAJ |
| issn | 2076-328X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Behavioral Sciences |
| spelling | doaj-art-ac20ff11f72747bfa105b57534e030032025-08-20T02:42:35ZengMDPI AGBehavioral Sciences2076-328X2025-03-0115332610.3390/bs15030326Behavioral Dynamics Analysis in Language Education: Generative State Transitions and Attention MechanismsQi Zhang0Yiming Qian1Shumiao Gao2Yufei Liu3Xinyu Shen4Qing Jiang5School of Foreign Languages, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Foreign Languages, Beijing Institute of Technology, Beijing 100081, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaCollege of Physical Education and Sports, Beijing Normal University, Beijing 100091, ChinaFaculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, ChinaThis 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.https://www.mdpi.com/2076-328X/15/3/326behavioral dynamics modelingadaptive learning path designdynamic learning behavior predictiongenerative attention mechanismstate transition equation |
| spellingShingle | Qi Zhang Yiming Qian Shumiao Gao Yufei Liu Xinyu Shen Qing Jiang Behavioral Dynamics Analysis in Language Education: Generative State Transitions and Attention Mechanisms Behavioral Sciences behavioral dynamics modeling adaptive learning path design dynamic learning behavior prediction generative attention mechanism state transition equation |
| title | Behavioral Dynamics Analysis in Language Education: Generative State Transitions and Attention Mechanisms |
| title_full | Behavioral Dynamics Analysis in Language Education: Generative State Transitions and Attention Mechanisms |
| title_fullStr | Behavioral Dynamics Analysis in Language Education: Generative State Transitions and Attention Mechanisms |
| title_full_unstemmed | Behavioral Dynamics Analysis in Language Education: Generative State Transitions and Attention Mechanisms |
| title_short | Behavioral Dynamics Analysis in Language Education: Generative State Transitions and Attention Mechanisms |
| title_sort | behavioral dynamics analysis in language education generative state transitions and attention mechanisms |
| topic | behavioral dynamics modeling adaptive learning path design dynamic learning behavior prediction generative attention mechanism state transition equation |
| url | https://www.mdpi.com/2076-328X/15/3/326 |
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