The impact of AI on Japanese language education: a hybrid model for student behavior detection

The increasing complexity of classroom environments necessitates nigh-on-sophisticated methods for the detection and analysis of student behavior, especially in Japanese language education, where engagement and attention are crucial for effective learning. This study presents a new model to integrate the AlexNet deep Neural Network combined with an Extreme Learning Machine (ELM) for enhanced feature extraction and classification accuracy in the detection of student behavior under wireless network settings. Up until now, optimizer parameters of the ELM need to be adjusted to enhance the convergence of the learning model, therefore, an Advanced Electric Fish Optimization (AEFO) algorithm is introduced with frequency adjustment and multi-modal exploration capabilities for improved search ability. The model was tested on a real-life classroom dataset consisting of 282 images and videos, 1,456 test samples, and it was compared to state-of-the-art methods, namely CNN, ANN, and others optimized by metaheuristic. The experimental result shows that the proposed AlexNet/ELM/AEFO framework achieves a classification accuracy of 96.5%, precision of 94.8%, and recall of 98.2%, which surpassed existing methods in the detection of behaviors like writing, listening, raising hands, sleeping, and answering questions. The robustness of the proposed model and its high discriminating power has further been substantiated through confusion matrix and ROC/AUC analysis. This study shows promise in positioning the proposed system as a reliable and automated method for real-time monitoring of student engagement with the ability for data-driven pedagogical intervention, thus expediting the AI framework in intelligent classroom environments.