A Biometric Sensor Network to Enable Real-Time Measurement of Individual Student Engagement in STEM Lecture Environments

arXiv:2607.28944 · cs.CR, cs.CV, cs.SE, eess.IV · Submitted 2026-07-31 · Read on arXiv

Ahmed Elsayed

cs.CR, cs.CV, cs.SE, eess.IV

Submitted: 2026-07-31

Comments: Thesis

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: Student engagement (SE) is a critical predictor of academic performance and retention in STEM education, yet existing measurement approaches are often intrusive, manually intensive, or unsuitable for

Terminology

Calibration — 16× in this paper · explained in 7 episodes
Biometric Sensor Network — 10× in this paper
Alignment — 9× in this paper · explained in 1 episode
Core — 8× in this paper · explained in 2 episodes
Behavioral Engagement — 7× in this paper
Student Processing Unit — 7× in this paper
Accuracy — 6× in this paper · explained in 1 episode
Cognitive Engagement — 6× in this paper
Edge Devices — 6× in this paper · explained in 2 episodes
Role-Based Access Control — 5× in this paper
Benchmark — 4× in this paper · explained in 2 episodes
Consistency — 4× in this paper · explained in 3 episodes
Verification — 4× in this paper · explained in 2 episodes
Emotional Engagement — 3× in this paper
Affective Computing — 2× in this paper · explained in 1 episode
Aggregation — 2× in this paper · explained in 1 episode
Bill of Materials — 2× in this paper
Classroom Fixed Unit — 2× in this paper
Clustering — 2× in this paper · explained in 2 episodes
Cognitive Load — 2× in this paper · explained in 2 episodes
Institutional Review Board — 2× in this paper
Robustness — 2× in this paper · explained in 5 episodes
Temporal consistency — 2× in this paper · explained in 2 episodes
Active Learning — 1× in this paper · explained in 6 episodes
Computational overhead — 1× in this paper · explained in 2 episodes
Computer Vision and Image Processing — 1× in this paper
Distillation — 1× in this paper · explained in 2 episodes
Energy Efficiency — 1× in this paper · explained in 3 episodes
Feature Extraction — 1× in this paper · explained in 2 episodes
Federated Learning — 1× in this paper · explained in 15 episodes
Generalizability — 1× in this paper · explained in 6 episodes
Generalization — 1× in this paper · explained in 13 episodes
IEEE International Conference on Image Processing — 1× in this paper
Interpretability — 1× in this paper · explained in 15 episodes
Multimodal Fusion — 1× in this paper · explained in 3 episodes
Pruning — 1× in this paper · explained in 4 episodes
Quantization — 1× in this paper · explained in 12 episodes
Transformer — 1× in this paper · explained in 2 episodes

Abstract

Student engagement (SE) is a critical predictor of academic performance and retention in STEM education, yet existing measurement approaches are often intrusive, manually intensive, or unsuitable for real-time classroom use. This thesis proposes a novel Biometric Sensor Network (BSN) designed to enable real-time measurement and continuous tracking of individual student engagement in STEM classroom environments. The system enables capturing of behavioral, emotional, and cognitive indicators through camera-based sensing while preserving ethical and privacy constraints. To measure these indicators unobtrusively and ethically, we propose a BSN composed of Student Processing Units (SPUs) that function as distributed sensing nodes. The network is explicitly designed to satisfy five objectives: it must be non-intrusive, non-invasive, non-stigmatizing, real-time, and automatic, while ensuring rigorous protection of student data security and privacy. Each SPU supports two operational modes: (i) a dataset-collection mode, in which raw student video is temporarily recorded to construct a private SE dataset for model training and validation, and (ii) an analysis mode, in which the SPU performs real-time inference on 10-second video segments without storing or transmitting raw frames. In this analysis role, each SPU enables fully on-device processing---including face detection, gaze estimation, and affective analysis---ensuring that no identifiable video data leaves the device. A secure backend infrastructure manages device authentication, session orchestration, and encrypted data ingestion. The full system integrates hardware design, computer-vision pipelines, wireless networking, security protocols, and session-level data management.

Terminology

Abstract

Related papers