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.
翻译:我们在澳大利亚墨尔本郊区的一所K-12私立学校进行了实地研究,数据采集包括两个要素:第一,5个月的纵向实地研究;利用两个室外气象站以及17个教室室内气象站和17个客房控制室通风口空调的温度传感器,利用2个室外气象站和17个室内气象站和温度传感器对每个教室进行了现场研究;这些数据以5分钟的伐木频率整理成每个教室的单个数据集,包括关于占用空间的更多数据;该数据集用来得出住户如何操作室内空调装置的预测模型;第二,我们跟踪了23名学生和6名教师进行为期4周的跨部门En-Gage研究,利用可磨损传感器记录生理数据,以及每日调查,以询问住户的热舒适、学习、情绪和座椅行为;总的来说,综合数据集可用于分析室内/室外气候与校园学生行为/心理状态之间的关系,为今后设计智能反馈系统提供机会,使学生和工作人员都受益。