AI 中文总结
E3Sense通过头戴式设备整合脑电、眼动和皮电信号，实现个性化学习参与度感知，预测准确率达75%，优于基线，并验证了学习者定义对测量的提升作用。
AI 中文摘要
参与度感知的学习系统可以在学习者遇到困难时提供提示或调整节奏。先前的参与度感知工作将传感器分布在身体各处或身体外部，而非集中在单一位置，或者将参与度简化为共享情感或单一维度（来自行为、情感和认知参与度）。我们介绍了E3Sense，一个头戴式平台，它共同定位了脑电图、眼动追踪和皮肤电活动，以个性化参与度测量。在一项实验室研究中，我们收集了参与者观看教育视频时的450个五级有序量表上的参与度水平评分。对于十五名留出参与者，E3Sense达到了75.0%的预测得分（预测与实际评分相差一级以内），而总是预测最常见评分的基线为63.0%。在对同一研究中18名定义参与度的参与者的探索性分析中，以学习者的定义作为条件，将同一指标提高了6.9个百分点，从64.6%提高到71.5%。我们的工作为自适应教育界面提供了头部位置、个性化多模态参与度感知的概念验证。
英文摘要
Engagement-aware learning systems could provide hints or adjust pacing when learners struggle. Prior engagement sensing work distributes sensors across or outside the body rather than consolidating them at one site, or reduces engagement to shared affect or a single dimension (from behavioral, emotional, and cognitive engagement). We introduce E3Sense, a head-worn platform that co-locates electroencephalography, eye tracking, and electrodermal activity to personalize engagement measurement. During a lab study we collected 450 ratings of engagement levels on a five-level ordinal scale while participants watched educational videos. For fifteen held-out participants, E3Sense achieved a within-one-level prediction score of 75.0%, compared with 63.0% for always predicting the most common rating. In an exploratory analysis of 18 participants from the same study who defined engagement, conditioning on learners' definitions raised the same measure by 6.9 points, from 64.6% to 71.5%. Our work provides a proof-of-concept of a head-site, personalized multimodal sensing of engagement for adaptive educational interfaces.
