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<p>This paper discusses the informatization teaching mode of college students’ psychoeducation courses under the cognitive-behavioral theory, highlighting the importance of informatization in education and the changes in psychoeducation courses in the past 30 years. Effective identification of college students’ psychological characteristics, especially in the early identification and intervention of psychological crisis, was realized through graph neural networks and attention mechanisms. The study involved extensive data analysis, including the psychological identification of 189,134 images of students’ study screens and the analysis of the psychological characteristics of study screens from multiple dimensions. In addition, the study designed a psychoeducational management system based on graph convolutional neural networks, which can recognize students’ psychological states and provide real-time feedback. The analysis of college students’ psychological emotions in the COVID-19 epidemic revealed the association between students’ stress reactions and the development of the epidemic and the virus mutation rate, which emphasized the necessity of the innovative “Internet+” psychoeducational model. Overall, this study provides theoretical and practical support for improving the effectiveness of psychoeducation, and demonstrates the potential application of information technology in psychoeducation.</p>
</abstract>

This paper discusses the informatization teaching mode of college students’ psychoeducation courses under the cognitive-behavioral theory, highlighting the importance of informatization in education and the changes in psychoeducation courses in the past 30 years. Effective identification of college students’ psychological characteristics, especially in the early identification and intervention of psychological crisis, was realized through graph neural networks and attention mechanisms. The study involved extensive data analysis, including the psychological identification of 189,134 images of students’ study screens and the analysis of the psychological characteristics of study screens from multiple dimensions. In addition, the study designed a psychoeducational management system based on graph convolutional neural networks, which can recognize students’ psychological states and provide real-time feedback. The analysis of college students’ psychological emotions in the COVID-19 epidemic revealed the association between students’ stress reactions and the development of the epidemic and the virus mutation rate, which emphasized the necessity of the innovative “Internet+” psychoeducational model. Overall, this study provides theoretical and practical support for improving the effectiveness of psychoeducation, and demonstrates the potential application of information technology in psychoeducation.

Exploration of Informatization Teaching Mode of College Students’ Psychological Education Courses under Cognitive Behavioral Theory

Architecture Labor University of Shaanxi Province

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper discusses the informatization teaching mode of college students’ psychoeducation courses under the cognitive-behavioral theory, highlighting the...

Exploration of Informatization Teaching Mode of College Students’ Psychological Education Courses under Cognitive Behavioral Theory

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 19. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0482

Schlüsselwörter
Graph convolutional neural network, Attention mechanism, Feature matrix, Similarity, Psychological education

© 2024 Juanwei Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploration of Informatization Teaching Mode of College Students’ Psychological Education Courses under Cognitive Behavioral Theory

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 19. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0482

SchlüsselwörterGraph convolutional neural network, Attention mechanism, Feature matrix, Similarity, Psychological education

© 2024 Juanwei Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Graph convolutional neural network, Attention mechanism, Feature matrix, Similarity, Psychological education