A learning behavior classification model based on classroom meta-action sequences

The individual adaptive behavioral interpretation of students' learning behaviors is a vital link for instructional process interventions. Accurately recognizing learning behaviors and conducting a complete judgment of classroom meta-action sequences are essential for the individual adaptive behavioral interpretation of students' learning behaviors. This paper proposes a learning behavior classification model based on classroom meta-action sequences (ConvTran-Fibo-CA-Enhanced). The model employs the Fibonacci sequence for location encoding to augment the positional attributes of classroom meta-action sequences. It also integrates Channel Attention and Data Augmentation techniques to improve the model's ability to comprehend these sequences, thereby increasing the accuracy of learning behavior classification and verifying the completeness of classroom meta-action sequences. Experimental results show that the proposed model outperforms baseline models on human activity recognition public datasets and learning behavior classification and meta-action sequences completeness judgment datasets in smart classroom scenarios.