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A Video Dataset for Classroom Group Engagement Recognition.

Weigang Lu1, Yang Yang1, Runfei Song1

  • 1Department of Education, Ocean University of China, Qingdao, 266100, China.

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|April 16, 2025
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Summary

This study introduces a new dataset for recognizing student group engagement in real classrooms using visual cues. This advances educational AI by analyzing group dynamics, not just individual actions.

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Area of Science:

  • Educational Technology
  • Computer Vision
  • Artificial Intelligence in Education

Background:

  • Student group engagement is crucial for knowledge sharing and deeper understanding.
  • Current methods for recognizing engagement often rely on lab-based individual interactions, which do not reflect authentic classroom dynamics.
  • There is a need for methods that can analyze group engagement within natural learning environments.

Purpose of the Study:

  • To develop and validate a method for recognizing student group engagement in authentic classroom settings using only visual cues.
  • To introduce the OUC Classroom Group Engagement Dataset (OUC-CGE), the first benchmark for visual-based group engagement analysis in classrooms.
  • To establish group engagement as a computable pedagogical construct for diagnostic insights.

Main Methods:

  • Development of the OUC Classroom Group Engagement Dataset (OUC-CGE) using visual data from real classrooms.
  • Testing of classical machine learning models on the OUC-CGE dataset.
  • Application of a technical-pedagogical dual validation strategy to assess the dataset's effectiveness.

Main Results:

  • The OUC-CGE dataset is the first benchmark for analyzing group engagement in authentic classrooms using visual signals.
  • Validation demonstrated that OUC-CGE exhibits good consistency and discriminability with existing datasets.
  • The proposed approach successfully analyzes group engagement while preserving the ecological complexity of classroom settings.

Conclusions:

  • This research shifts focus from individual to group engagement recognition in educational settings.
  • The OUC-CGE dataset and associated models provide valuable tools for researching socially-embedded educational AI.
  • The findings offer teachers diagnostic insights into group engagement patterns, aiding in the optimization of teaching and learning processes.