n-Gage：在野外预测课堂情绪，行为和认知参与
n-Gage: Predicting in-class Emotional, Behavioural and Cognitive Engagement in the Wild
摘要：
学生参与度的研究吸引了越来越多的兴趣，以解决诸如学习成绩低，不满，辍学率高等问题。衡量学生参与度的现有方法通常依赖于基于调查的工具。这些办法虽然有效，但费时费力。同时，调查的答复率和质量通常都很差。作为一种替代方案，在本文中，我们研究了我们是否可以推断和预测在多个维度的参与，只是使用传感器。我们假设多维的学生参与可以转化为课堂上的生理反应和活动变化，也受到环境变化的影响。因此，我们的目标是探索以下问题：我们可以测量高中生的学习参与的多个维度，包括情感，行为和认知参与感测数据在野外？我们能否推导出影响学生参与度不同维度的活动、生理和环境因素？如果是，哪些传感器在区分参与的每个维度方面最有用？然后，我们在一所高中进行了为期4周的11门课程的144个班级的23名学生和6名教师的现场研究。我们提出了n-Gage，一个学生参与感测系统，使用可穿戴设备和环境中的传感器组合来自动检测学生在课堂上的多维学习参与。实验结果表明，n-Gage可以准确地预测多维学生参与在现实世界中的场景与平均MAE为0.788和RMSE为0.975使用所有的传感器。我们还展示了一组有趣的发现，不同的因素（例如，传感器组合、学校科目、CO2水平）影响学生学习参与度的每个维度。
英文摘要：
The study of student engagement has attracted growing interests to address problems such as low academic performance, disaffection, and high dropout rates. Existing approaches to measuring student engagement typically rely on survey-based instruments. While effective, those approaches are time-consuming and labour-intensive. Meanwhile, both the response rate and quality of the survey are usually poor. As an alternative, in this paper, we investigate whether we can infer and predict engagement at multiple dimensions, just using sensors. We hypothesize that multidimensional student engagement can be translated into physiological responses and activity changes during the class, and also be affected by the environmental changes. Therefore, we aim to explore the following questions: Can we measure the multiple dimensions of high school student's learning engagement including emotional, behavioural and cognitive engagement with sensing data in the wild? Can we derive the activity, physiological, and environmental factors contributing to the different dimensions of student engagement? If yes, which sensors are the most useful in differentiating each dimension of the engagement? Then, we conduct an in-situ study in a high school from 23 students and 6 teachers in 144 classes over 11 courses for 4 weeks. We present the n-Gage, a student engagement sensing system using a combination of sensors from wearables and environments to automatically detect student in-class multidimensional learning engagement. Experiment results show that n-Gage can accurately predict multidimensional student engagement in real-world scenarios with an average MAE of 0.788 and RMSE of 0.975 using all the sensors. We also show a set of interesting findings of how different factors (e.g., combinations of sensors, school subjects, CO2 level) affect each dimension of the student learning engagement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
