AI 中文总结
SetEasy融合多模态感知与v-Gage模型，通过CP-SAT优化固定座位布局，在23名学生331个班级的四周部署中，将平均参与度从0.30提升至0.70，为课堂空间设计提供了可迁移路径。
AI 中文摘要
SetEasy针对固定座位布局优化课堂参与度，融合多模态感知（腕带生理数据、4K视频、环境数据）并训练基于修订版ISEQ的v-Gage模型。每周将两周的参与度预测映射至学生-座位效用矩阵，CP-SAT在视觉访问与社会动态约束下生成座位方案。在为期四周的部署（23名学生、331个班级）中，v-Gage在情感、行为、认知及整体维度收敛，将RMSE从0.75降至0.53；优化后平均参与度从0.30升至0.70，超三分之二座位达高参与度，后排低活动模式显著减少。结果表明，无需硬件改动，可解释的数据驱动座位策略能大幅提升参与度，该多模态“评估+优化”范式为全球同质化背景下的文化响应式差异化空间设计提供了可迁移、可持续的路径。
英文摘要
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.
Comments18 pages, 4 figures, 2 tables. Published in the Proceedings of ASCAAD 2025
Journal refProceedings of the 13th International Conference of the Arab Society for Computation in Architecture, Art and Design (ASCAAD 2025), Riyadh, Saudi Arabia, 2025
