# Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

arXiv: https://arxiv.org/abs/1901.05835 · HTML: https://mlsanity.com/paper/1901.05835/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites](https://mlsanity.com/paper/2102.00423/) (2021)
- [Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review](https://mlsanity.com/paper/2408.14491/) (2024)
- [Inconsistencies in the Definition and Annotation of Student Engagement in Virtual Learning Datasets: A Critical Review](https://mlsanity.com/paper/2208.04548/) (2022)
- [OPEN: A Benchmark Dataset and Baseline for Older Adult Patient Engagement Recognition in Virtual Rehabilitation Learning Environments](https://mlsanity.com/paper/2507.17959/) (2025)

## Abstract (arXiv)

We propose a multimodal approach for detection of students' behavioral engagement states (i.e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse. Final behavioral engagement states are achieved by fusing modality-specific classifiers at the decision level. Various experiments were conducted on a student dataset collected in an authentic classroom.