To Kill a Student’s Disengagement: Personalized Engagement Detection in Facial Video

P. 329–337.

Churaev E., Andrey V. Savchenko

This work deals with engagement detection for e-learning systems based on a student’s facial video. We propose a novel, accurate technique that adapts the neural network-based binary classifier by using a short set of videos of a concrete user. The facial features are extracted with pre-trained lightweight deep networks that can be launched on a person’s device without a privacy leak. Moreover, we introduce the publicly available dataset that can be used to test the model’s efficiency. Experimentally, it is demonstrated that the approach with model adaptation can significantly (by 10–15%) improve the accuracy of predicting user engagement in the video.

Keywords: e-learning, engagement detection, facial analysis in video

Publication based on the results of: Network models, optimization and computational complexity (2024)

In book

25th International Conference, AIED 2024, Recife, Brazil, July 8–12, 2024, Proceedings, Part I. Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. CCIS, volume 2150

Vol. 1: Part I. , Springer, 2024.