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

arXiv:2607.28944v1 Announce Type: cross 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 extit{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 extit{Student Processing Units} (SPUs) that function as distributed sensing nodes. The network is explicitly designed to satisfy five objectives: it must be extbf{non-intrusive}, extbf{non-invasive}, extbf{non-stigmatizing}, extbf{real-time}, and extbf{automatic}, while ensuring rigorous protection of student data security and privacy. Each SPU supports two operational modes: (i) a extit{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 extit{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.

Source: arXiv cs.CV | 2026-08-03