# Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning

arXiv: https://arxiv.org/abs/1909.12913 · HTML: https://mlsanity.com/paper/1909.12913/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [Evaluating Image-Based Face and Eye Tracking with Event Cameras](https://mlsanity.com/paper/2408.10395/) (2024)
- [Leveraging Language Models for Emotion and Behavior Analysis in Education](https://mlsanity.com/paper/2408.06874/) (2024)
- [Performance Analysis and Evaluation of Cloud Vision Emotion APIs](https://mlsanity.com/paper/2303.12974/) (2023)
- [From Learning Management System to Affective Tutoring system: a preliminary study](https://mlsanity.com/paper/2311.05513/) (2023)
- [VisioPhysioENet: Visual Physiological Engagement Detection Network](https://mlsanity.com/paper/2409.16126/) (2024)
- [Monitoring Viewer Attention During Online Ads](https://mlsanity.com/paper/2504.06237/) (2025)
- [Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context](https://mlsanity.com/paper/2507.12889/) (2025)
- [Will You Be Aware? Eye Tracking-Based Modeling of Situational Awareness in Augmented Reality](https://mlsanity.com/paper/2508.05025/) (2025)
- [Attention-Guided Dual-Stream Learning for Group Engagement Recognition: Fusing Transformer-Encoded Motion Dynamics with Scene Context via Adaptive Gating](https://mlsanity.com/paper/2604.10078/) (2026)

## Abstract (arXiv)

With the increase of distance learning, in general, and e-learning, in particular, having a system capable of determining the engagement of students is of primordial importance, and one of the biggest challenges, both for teachers, researchers and policy makers. Here, we present a system to detect the engagement level of the students. It uses only information provided by the typical built-in web-camera present in a laptop computer, and was designed to work in real time. We combine information about the movements of the eyes and head, and facial emotions to produce a concentration index with three classes of engagement: "very engaged", "nominally engaged" and "not engaged at all". The system was tested in a typical e-learning scenario, and the results show that it correctly identifies each period of time where students were "very engaged", "nominally engaged" and "not engaged at all". Additionally, the results also show that the students with best scores also have higher concentration indexes.