## 4 Methods and Experiment
### Keyframe Extraction
The MultiFusedNet network analyzed the form of a given action based on multi-features, which are extracted and mixed from the selected keyframe. The spatial and temporal features are extracted from RGB images, including L*a*b* color images that are robust to changes in lighting, geometric transformed images that ensure data diversity, and optical flow images that contain motion vectors. We used the various spatial and temporal features in the depth network BiLSTM with an attention mechanism model. With highly complex student behavior datasets, the model can effectively analyze complex student behavior datasets, even when the data is similar across classes. By leveraging the capabilities of our framework, teachers or educators can develop novel techniques to help students learn more effectively. The model's ability to analyze student behavior can identify relationships between learning outcomes, activities, and behaviors, enabling instructors to design more personalized and targeted learning plans based on the PLC approach. In future work, we aim to expand our collection of student behavior datasets with diverse labeling schemes while adding new categories to our existing datasets. To enhance the effectiveness and accuracy of our overall framework, we plan to integrate pose estimation of the human skeleton into our existing multiple features and use a BiLSTM-AT deep sequence neural network to classify students' behaviors. The goal of this work is to comprehensively study student behavior in classrooms across diverse schools in Thailand. By partnering with researchers and using AI technology, the aim is to support teachers in analyzing and improving students' behaviors. This approach will enhance the classroom environment, address learning needs, and improve outcomes in all subjects. **Author Contributions:** Conception and design of the proposed method: [PERSON] and S.N. ; performance of the experiments: [PERSON], H.J.L. and S.-H.N. ; writing of the paper: S.N. ; paper review and editing: [PERSON] and S.-H.N. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub) and partially funded by the TRSI, Ministry of Higher Education, Science, Research and Innovation (MHESI) of Thailand (Project-ID: 63673). **Institutional Review Board Statement:** The ethical approval was waived by Sisaket Rajabhat University Research Ethics Committee for this study because this research done in education settings is not likely to have adverse impact on students learning. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions. **Acknowledgments:** In this research, we would like to express our gratitude to [PERSON] from Sisaket Rajabhat University in Thailand. He specializes in analyzing student behavior datasets, as well as creating specific category labels for datasets related to math class behavior. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

* (1) [PERSON] Lesson study and open approach development in Thailand: A longitudinal study. _Int. J. Lesson Learn. Stud._**2022**, _11_, 1-15. [CrossRef]
* (2) [PERSON] _Professional Learning Communities: Communities of Continuous Inquiry and Improvement_; Southwest Educational Development Laboratory: Austin, TX, USA, 1997. * (3) [PERSON]; [PERSON]; [PERSON] Cognitive Aspects of Students' Mathematical Reasoning Habits: A Study on Utilizing Lesson Study and Open Approach. _Pertanika J. Soc. Sci. Humanit._**2021**, _29_, 2591-2614.