A General Model for Detecting Learner Engagement: Implementation and Evaluation

TL;DR

This paper introduces a lightweight, general model for detecting learner engagement from video data, leveraging temporal features and an adaptation policy, achieving competitive accuracy and outperforming existing models.

Contribution

A novel, adaptable model for learner engagement detection that processes sequential data and incorporates affective state labels to enhance performance.

Findings

Achieved 68.57% accuracy in engagement detection.

Outperformed existing state-of-the-art models.

Utilized the DAiSEE dataset for dynamic engagement analysis.

Abstract

Considering learner engagement has a mutual benefit for both learners and instructors. Instructors can help learners increase their attention, involvement, motivation, and interest. On the other hand, instructors can improve their instructional performance by evaluating the cumulative results of all learners and upgrading their training programs. This paper proposes a general, lightweight model for selecting and processing features to detect learners' engagement levels while preserving the sequential temporal relationship over time. During training and testing, we analyzed the videos from the publicly available DAiSEE dataset to capture the dynamic essence of learner engagement. We have also proposed an adaptation policy to find new labels that utilize the affective states of this dataset related to education, thereby improving the models' judgment. The suggested model achieves an accuracy of 68.57\% in a specific implementation and outperforms the studied state-of-the-art models detecting learners' engagement levels.