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A scalable multimodal framework for learning engagement recognition using three-dimensional convolutional neural

Kuan-Cheng Lin1, Chiung-Chen Tseng1, Junyi Wu1

  • 1Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan.

Frontiers in Artificial Intelligence
|May 25, 2026
PubMed
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This study developed an AI system using 3D CNNs to recognize student learning engagement from behavior and emotions. The system achieved high accuracy, offering a scalable solution for educational analytics.

Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Computer Vision

Background:

  • Accurate assessment of student learning engagement is crucial for effective pedagogy.
  • Traditional methods for engagement recognition are often subjective and labor-intensive.
  • Automated systems can provide scalable and objective insights into student engagement.

Purpose of the Study:

  • To develop and validate an automated system for recognizing learning engagement using behavioral and emotional cues.
  • To integrate a three-dimensional convolutional neural network (3D CNN) with a semi-automatic annotation mechanism.
  • To enhance the accuracy and scalability of learning engagement recognition systems.

Main Methods:

  • Utilized 570 instructional video recordings, segmented into 44,059 clips.
Keywords:
3D CNNbehavior recognitionconsistency analysisfacial emotion recognitionlearning engagement recognitionmultimodal learning analyticssemi-automatic annotation

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  • Employed three experts for semi-automatic annotation of emotional and behavioral engagement.
  • Trained two separate 3D CNN models for emotional and behavioral feature recognition.
  • Evaluated model performance using video-level validation and Fleiss' kappa for inter-annotator agreement.

    • Main Results:

    • The emotional engagement model achieved an accuracy of 0.92.
    • The behavioral engagement model achieved an accuracy of 0.80.
    • Model predictions showed "almost perfect agreement" with human annotations (Fleiss' kappa).

    Conclusions:

    • Integrating 3D CNNs with standardized annotation rules significantly improves automated learning engagement recognition.
    • The developed system demonstrates human-level reliability, accuracy, and scalability.
    • The framework supports scalable AI-driven learning analytics and video-based behavior recognition.