# Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review

arXiv: https://arxiv.org/abs/2509.07742 · HTML: https://mlsanity.com/paper/2509.07742/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 138 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms](https://mlsanity.com/paper/2211.09210/) (2022)
- [edBB: Biometrics and Behavior for Assessing Remote Education](https://mlsanity.com/paper/1912.04786/) (2019)
- [M2LADS: A System for Generating MultiModal Learning Analytics Dashboards in Open Education](https://mlsanity.com/paper/2305.12561/) (2023)
- [VAAD: Visual Attention Analysis Dashboard applied to e-Learning](https://mlsanity.com/paper/2405.20091/) (2024)
- [A multimodal dataset for understanding the impact of mobile phones on remote online virtual education](https://mlsanity.com/paper/2412.14195/) (2024)
- [Biometrics and Behavior Analysis for Detecting Distractions in e-Learning](https://mlsanity.com/paper/2405.15434/) (2024)
- [Improving prediction of students' performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources](https://mlsanity.com/paper/2403.07194/) (2024)
- [AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning](https://mlsanity.com/paper/2506.17364/) (2025)
- [From Complexity to Parsimony: Integrating Latent Class Analysis to Uncover Multimodal Learning Patterns in Collaborative Learning](https://mlsanity.com/paper/2411.15590/) (2024)

## Built on by (most cited first, 3 of 3)

- [A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data](https://mlsanity.com/paper/2601.07576/) (2026)
- [AISSA: Implementation and Deployment of an AI-based Student Slides Analysis tool for Academic Presentations](https://mlsanity.com/paper/2605.04729/) (2026)
- [AICoFe: Implementation and Deployment of an AI-Based Collaborative Feedback System for Higher Education](https://mlsanity.com/paper/2605.04740/) (2026)

## Abstract (arXiv)

In modern online learning, understanding and predicting student behavior is crucial for enhancing engagement and optimizing educational outcomes. This systematic review explores the integration of biosensors and Multimodal Learning Analytics (MmLA) to analyze and predict student behavior during computer-based learning sessions. We examine key challenges, including emotion and attention detection, behavioral analysis, experimental design, and demographic considerations in data collection. Our study highlights the growing role of physiological signals, such as heart rate, brain activity, and eye-tracking, combined with traditional interaction data and self-reports to gain deeper insights into cognitive states and engagement levels. We synthesize findings from 54 key studies, analyzing commonly used methodologies such as advanced machine learning algorithms and multimodal data pre-processing techniques. The review identifies current research trends, limitations, and emerging directions in the field, emphasizing the transformative potential of biosensor-driven adaptive learning systems. Our findings suggest that integrating multimodal data can facilitate personalized learning experiences, real-time feedback, and intelligent educational interventions, ultimately advancing toward a more customized and adaptive online learning experience.