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

arXiv: https://arxiv.org/abs/2607.28944 · HTML: https://mlsanity.com/paper/2607.28944/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 103 references that are in the map.
- First posted on arXiv 31 Jul 2026.

## Builds on (most cited first, 1 of 1)

- [Stem-ming the Tide: Predicting STEM attrition using student transcript data](https://mlsanity.com/paper/1708.09344/) (2017)

## Abstract (arXiv)

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 textitBiometric 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 textitStudent 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 textitdataset-collection mode, in which raw student video is temporarily recorded to construct a private SE dataset for model training and validation, and (ii) an textitanalysis 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.