School of Computing Technologies, RMIT University, Melbourne, 3000, Australia.
School of Architecture and Urban Design, RMIT University, Melbourne, 3000, Australia.
Sci Data. 2022 Jun 2;9(1):261. doi: 10.1038/s41597-022-01347-w.
We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-conditioners; these were collated into individual datasets for each classroom at a 5-minute logging frequency, including additional data on occupant presence. The dataset was used to derive predictive models of how occupants operate room air-conditioning units. Second, we tracked 23 students and 6 teachers in a 4-week cross-sectional study En-Gage, using wearable sensors to log physiological data, as well as daily surveys to query the occupants' thermal comfort, learning engagement, emotions and seating behaviours. Overall, the combined dataset could be used to analyse the relationships between indoor/outdoor climates and students' behaviours/mental states on campus, which provide opportunities for the future design of intelligent feedback systems to benefit both students and staff.
我们在澳大利亚墨尔本郊区的一所 K-12 私立学校进行了一项实地研究。数据采集包含两个要素：首先，使用两个户外气象站以及 17 间教室内的气象站和通风口温度传感器，进行为期 5 个月的纵向现场 In-Gauge 研究，以 5 分钟的记录频率整理为每个教室的单独数据集，包括关于居住者在场的其他数据。该数据集用于推导居住者操作房间空调设备的预测模型。其次，我们在一项为期 4 周的横向 En-Gage 研究中跟踪了 23 名学生和 6 名教师，使用可穿戴传感器记录生理数据，以及每日调查来查询居住者的热舒适度、学习参与度、情绪和座位行为。总的来说，综合数据集可用于分析室内/室外气候与学生在校园内的行为/心理状态之间的关系，这为未来设计智能反馈系统提供了机会，使学生和教职员工都受益。
