Problem
Research questions and friction points this paper is trying to address.
student engagement
real-time measurement
biometric sensing
STEM education
privacy-preserving
A Biometric Sensor Network to Enable Real-Time Measurement of Individual Student Engagement in STEM Lecture Environments
🤖 AI Summary
This study addresses the limitations of existing student engagement measurement methods in STEM classrooms, which often suffer from high intrusiveness, reliance on manual annotation, or inability to provide real-time monitoring—making it difficult to balance privacy and practicality. To overcome these challenges, this work proposes a non-intrusive monitoring system based on a biometric sensor network (BSN), employing distributed Student Processing Units (SPUs) to locally capture behavioral, emotional, and cognitive indicators at the edge in real time, ensuring raw video data never leaves the device. The system integrates camera-based sensing, face detection, gaze estimation, affective computing, and edge processing, coupled with wireless networking and end-to-end encryption to fulfill design goals of non-intrusiveness, non-stigmatization, automation, and strong privacy preservation. Experimental results demonstrate that the system enables continuous, accurate, and real-time assessment of individual engagement without storing or transmitting raw data.
📝 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 $\textit{Biometric Sensor Network}$ (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 $\textit{Student Processing Units}$ (SPUs) that function as distributed sensing nodes. The network is explicitly designed to satisfy five objectives: it must be $\textbf{non-intrusive}, \textbf{non-invasive}, \textbf{non-stigmatizing}, \textbf{real-time}$, and $\textbf{automatic}$, while ensuring rigorous protection of student data security and privacy.
Each SPU supports two operational modes: (i) a $\textit{dataset-collection mode}$, in which raw student video is temporarily recorded to construct a private SE dataset for model training and validation, and (ii) an $\textit{analysis mode}$, 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Biometric Sensor Network
real-time engagement measurement
on-device inference
privacy-preserving sensing
student engagement
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