# Biometrics and Behavior Analysis for Detecting Distractions in e-Learning

arXiv: https://arxiv.org/abs/2405.15434 · HTML: https://mlsanity.com/paper/2405.15434/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 17 of 17)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Fine-Grained Head Pose Estimation Without Keypoints](https://mlsanity.com/paper/1710.00925/) (2017)
- [BlazePose: On-device Real-time Body Pose tracking](https://mlsanity.com/paper/2006.10204/) (2020)
- [Detecting Attended Visual Targets in Video](https://mlsanity.com/paper/2003.02501/) (2020)
- [BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs](https://mlsanity.com/paper/1907.05047/) (2019)
- [WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose](https://mlsanity.com/paper/2005.10353/) (2020)
- [mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation](https://mlsanity.com/paper/2006.05327/) (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)
- [ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning](https://mlsanity.com/paper/2112.09165/) (2021)
- [Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB](https://mlsanity.com/paper/2006.00825/) (2020)
- [RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild](https://mlsanity.com/paper/2011.01890/) (2020)
- [MATT: Multimodal Attention Level Estimation for e-learning Platforms](https://mlsanity.com/paper/2301.09174/) (2023)

## Built on by (most cited first, 10 of 10)

- [A multimodal dataset for understanding the impact of mobile phones on remote online virtual education](https://mlsanity.com/paper/2412.14195/) (2024)
- [DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learning](https://mlsanity.com/paper/2408.05523/) (2024)
- [SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality](https://mlsanity.com/paper/2501.10977/) (2025)
- [AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning](https://mlsanity.com/paper/2506.17364/) (2025)
- [EduEVAL-DB: A Role-Based Dataset for Pedagogical Risk Evaluation in Educational Explanations](https://mlsanity.com/paper/2602.15531/) (2026)
- [Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond](https://mlsanity.com/paper/2607.02197/) (2026)
- [M2LADS Demo: A System for Generating Multimodal Learning Analytics Dashboards](https://mlsanity.com/paper/2502.15363/) (2025)
- [Is It Really You? Exploring Biometric Verification Scenarios in Photorealistic Talking-Head Avatar Videos](https://mlsanity.com/paper/2508.00748/) (2025)
- [Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review](https://mlsanity.com/paper/2509.07742/) (2025)
- [AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations](https://mlsanity.com/paper/2607.01934/) (2026)

## Abstract (arXiv)

In this article, we explore computer vision approaches to detect abnormal head pose during e-learning sessions and we introduce a study on the effects of mobile phone usage during these sessions. We utilize behavioral data collected from 120 learners monitored while participating in a MOOC learning sessions. Our study focuses on the influence of phone-usage events on behavior and physiological responses, specifically attention, heart rate, and meditation, before, during, and after phone usage. Additionally, we propose an approach for estimating head pose events using images taken by the webcam during the MOOC learning sessions to detect phone-usage events. Our hypothesis suggests that head posture undergoes significant changes when learners interact with a mobile phone, contrasting with the typical behavior seen when learners face a computer during e-learning sessions. We propose an approach designed to detect deviations in head posture from the average observed during a learner's session, operating as a semi-supervised method. This system flags events indicating alterations in head posture for subsequent human review and selection of mobile phone usage occurrences with a sensitivity over 90%.