###### Abstract  
This research proposes a deep learning method for classifying student behavior in classrooms that follow the professional learning community teaching approach. We collected data on five student activities: hand-raising, interacting, sitting, turning around, and writing. We used the sum of absolute differences (SAD) in the LUV color space to detect scene changes. The K-means algorithm was then applied to select keyframes using the computed SAD. Next, we extracted features using multiple pretrained deep learning models from the convolutional neural network family. The pre-trained models considered were InceptionV3, ResNet50V2, VGG16, and EfficientNetB7. We leveraged feature fusion, incorporating optical flow features and data augmentation techniques, to increase the necessary spatial features of selected keyframes. Finally, we classified the students' behavior using a deep sequence model based on the bidirectional long short-term memory network with an attention mechanism (BiLSTM-AT). The proposed method with the BiLSTM-AT model can recognize behaviors from our dataset with high accuracy, precision, recall, and F1-scores of 0.97, 0.97, and 0.97, respectively. The overall accuracy was 96.67%. This high efficiency demonstrates the potential of the proposed method for classifying student behavior in classrooms. lesson study; professional learning community; student behaviors classification; video classification; keyframe selection; multi-feature fusion; hybrid model; deep recurrent neural networks +
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deployed machine learning and deep learning models [5] to help resolve difficulties such as the difficulties in understanding student behavior, student engagement in the classroom, and students' emotional facial expressions [6; 7; 8; 9; 10; 11]. AI technology in the computer vision domain can help teachers analyze student behavior better. Furthermore, this technology can be used to develop an improved teaching approach and learning model that will help students enhance their enthusiasm for studying. In addition, this approach and model improve the capacity of students to solve issues and form stronger relationships among themselves. While AI can potentially analyze student behavior, limitations persist in this analysis due to data complexity, dynamic classroom environments, and behavior variability. The multifaceted nature of student behavior poses challenges for models to capture behaviors precisely. AI model accuracy is also influenced by classroom size, as larger classes can overwhelm the model's ability to track individual behaviors effectively. Additionally, varying classroom contexts, such as in terms of teaching approaches, classroom layouts, lighting conditions, and classroom learning culture, can significantly impact student behavior. To overcome these limitations, we propose AI technology [12], based on a novel hybrid approach involving a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) [13; 14], for the classification of student behavior. Our framework utilizes keyframe selection in video preprocessing and multiple-feature fusion from various pretrained CNN families to extract features from each keyframe and fused features. We then leverage a deep sequence neural network with an attention mechanism to classify student behavior in the classroom. We collected the dataset from mathematics classrooms in a primary school at Sisaket Rajabhat University Demonstration School, Thailand. The student's behavior dataset consists of five categories: hand raising, interacting, sitting, turning around, and writing. Our work uses a student behavior classification approach that is different from that of other researchers, who typically use behavior detection to alert teachers about inappropriate learning behaviors. Student behavior classification, a critical component of student analysis, is essential for designing learning units in each subject.