# Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

## TL;DR

This paper introduces a multimodal, unobtrusive method for detecting students' behavioral engagement states in real classroom settings by combining appearance, context-performance, and mouse data.

## Contribution

It presents a novel multimodal fusion approach for behavioral engagement detection using unobtrusive data sources in authentic educational environments.

## Key findings

- High accuracy in engagement state classification
- Effective fusion of multiple modalities improves detection performance
- Validated on real classroom student data

## Abstract

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.