用多模态数据优化教室座位，提升学生参与度。
SetEasy: A Multi-Modal Classroom Engagement Assessment and Seating Optimization Framework
- 融合生理、视频与环境数据，用v-Gage模型评估参与度。
- 座位优化使平均参与度从0.30升至0.70，超三分之二座位达高参与。
- 无需改硬件，适合教育机构做可解释的个性化空间设计。
SetEasy通过多模态传感（腕带生理信号、4K视频、环境数据）与基于改进ISEQ的v-Gage模型，对固定座位制课堂进行参与度评估与座位优化。每周生成两周参与度预测，构建学生-座位效用矩阵，并在视觉可达性与社交动态约束下，由CP-SAT生成优化座位方案。四周期部署（23名学生，331节课）中，v-Gage在情感、行为、认知及总体维度均收敛，均方根误差（RMSE）从0.75降至0.53。优化后平均参与度从0.30升至0.70，超三分之二座位进入高参与状态，后排低活跃现象显著减少。结果表明，无需硬件改造，可解释的数据驱动策略可大幅提升课堂参与度。该多模态“评估+优化”范式为应对全球空间同质化，提供了一条可迁移、可持续的文化响应型空间设计路径。
原文摘要 · Abstract (English)
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.
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