# SetEasy: A Multi-Modal Classroom Engagement Assessment and Seating Optimization Framework

arXiv: https://arxiv.org/abs/2608.07188 · HTML: https://mlsanity.com/paper/2608.07188/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Student Classroom Behavior Detection based on YOLOv7-BRA and Multi-Model Fusion](https://mlsanity.com/paper/2305.07825/) (2023)
- [CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities](https://mlsanity.com/paper/2607.08554/) (2026)
- [EEG Emotion Recognition From AI-Generated Biodigital Architecture Images](https://mlsanity.com/paper/2607.24808/) (2026)

## Abstract (arXiv)

SetEasy optimizes classroom engagement in fixed seating grids. It fuses multimodal sensing (wristband physiology, 4K video, environmental data) and trains a v-Gage model grounded in a revised ISEQ. Each week, two-week engagement forecasts are mapped to a student-seat utility matrix, and CP-SAT generates seating plans under visual-access and social-dynamics constraints. In a four-week deployment (23 students, 331 classes), v-Gage converged across affective, behavioral, cognitive, and overall dimensions, cutting RMSE from 0.75 to 0.53. Optimization raised mean engagement from 0.30 to 0.70, with over two-thirds of seats reaching high engagement and back-row low-activity patterns markedly reduced. These results show that, without hardware changes, interpretable, data-driven seating strategies can substantially enhance engagement. The multimodal "assessment + optimization" paradigm offers a transferable, sustainable path to culturally responsive, differentiated spatial design amid global homogenization.