Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students
Not on the map. Why?
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jan 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2021Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websitescited by 4
- 2024Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Reviewcited by 4
- 2022Inconsistencies in the Definition and Annotation of Student Engagement in Virtual Learning Datasets: A Critical Reviewcited by 0
- 2025OPEN: A Benchmark Dataset and Baseline for Older Adult Patient Engagement Recognition in Virtual Rehabilitation Learning Environmentscited by 0
- 2025OPEN: A Benchmark Dataset and Baseline for Older Adult Patient Engagement Recognition in Virtual Rehabilitation Learning Environments23 Jul 2025
- 2024Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review22 Aug 2024
- 2022Inconsistencies in the Definition and Annotation of Student Engagement in Virtual Learning Datasets: A Critical Review9 Aug 2022
- 2021Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites31 Jan 2021