A Multicamera Classroom Dataset for Deep Learning-Based Student Action Recognition and Engagement Analysis

Classroom behavior analysis has become a relevant area for developing intelligent educational environments, enabling continuous, non-intrusive understanding of student engagement and interactions. In this work, we present a new video clip dataset for recognizing student behavior in the classroom, based on behavior derived from observational protocols. This new dataset also has the feature of obtaining student actions through four different cameras that are positioned frontally and laterally in the classroom. The dataset is then used to train state-of-the-art models for video behavior classification, including Multiscale Vision Transformer (MViTv2-S), Swin Transformer, SlowFast, and X3D. Our results show that MViTv2-S has better performance when accuracy and F1-score are analyzed. The results indicate an accuracy of 74.44 ± 0.45 for the frontal cameras and 70.88% ± 1.04 for the lateral cameras, demonstrating the effectiveness of both camera configurations for student action recognition. When engagement is evaluated by aggregating the classified actions, an accuracy of 83.33% and a macro F1-score of 80.42% are achieved. The results highlight the potential of deep learning-based approaches for classroom behavior analysis, offering insights that may support applications in pedagogical support, student engagement assessment, and the development of interpretable and scalable intelligent educational systems.