Abstract
Student group engagement helps share knowledge and build a more complete understanding. Recognition of group engagement in the classroom helps us understand students' learning state and optimize teaching and study processes. While existing research predominantly focuses on individual engagement recognition through controlled lab-based computer interactions, this paradigm was fundamentally misaligned with authentic classroom dynamics. To address this issue, this research focuses on student group engagement recognition in classroom environments using visual cues. We propose OUC Classroom Group Engagement Dataset (OUC-CGE), the first benchmark dedicated to group engagement analysis in authentic classroom settings using pure visual signals. Several classical models are tested on OUC-CGE, and through technical-pedagogical dual validation strategy, OUC-CGE exhibits good consistency and discriminability with existing datasets. By transcending the individualistic paradigm, this work establishes group engagement as a computable pedagogical construct, offering teachers diagnostic insights into group engagement trajectories while preserving the ecological complexity of authentic classrooms. The OUC-CGE dataset and models are publicly released to catalyze research in socially-embedded educational artificial intelligence.