Learner Attentiveness and Engagement Analysis in Online Education Using Computer Vision

TL;DR

This paper introduces a computer vision-based system that automatically analyzes and quantifies learner attentiveness and engagement in online education, providing real-time feedback to instructors to enhance teaching effectiveness.

Contribution

It develops a novel multiclass multioutput CNN classification model and an end-to-end pipeline for real-time attentiveness analysis in online learning environments.

Findings

Outperforms previous methods in attentiveness detection accuracy

Demonstrates real-time processing capability

Provides comprehensive attentiveness analytics for online education

Abstract

In recent times, online education and the usage of video-conferencing platforms have experienced massive growth. Due to the limited scope of a virtual classroom, it may become difficult for instructors to analyze learners' attention and comprehension in real time while teaching. In the digital mode of education, it would be beneficial for instructors to have an automated feedback mechanism to be informed regarding learners' attentiveness at any given time. This research presents a novel computer vision-based approach to analyze and quantify learners' attentiveness, engagement, and other affective states within online learning scenarios. This work presents the development of a multiclass multioutput classification method using convolutional neural networks on a publicly available dataset - DAiSEE. A machine learning-based algorithm is developed on top of the classification model that outputs a comprehensive attentiveness index of the learners. Furthermore, an end-to-end pipeline is proposed through which learners' live video feed is processed, providing detailed attentiveness analytics of the learners to the instructors. By comparing the experimental outcomes of the proposed method against those of previous methods, it is demonstrated that the proposed method exhibits better attentiveness detection than state-of-the-art methods. The proposed system is a comprehensive, practical, and real-time solution that is deployable and easy to use. The experimental results also demonstrate the system's efficiency in gauging learners' attentiveness.