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. This is the first publicly available dataset studying the daily behaviours and engagement of high school students using heterogeneous methods. The combined data could be used to analyse the relationships between indoor climates and mental states of school students.
Please cite the following papers if the dataset is used in a publication:
 Gao, N., Marschall, M., Burry, J., Watkins, S., & Salim, F. D. (2021) Understanding Occupants’ Behaviour, Engagement, Emotion, and Comfort Indoors with Heterogeneous Sensors and Wearables. arxiv.org, https://arxiv.org/abs/2105.06637v1.
 Gao, N., Shao, W., Rahaman, M. S., & Salim, F. D. (2020). n-Gage: Predicting in-class Emotional, Behavioural and Cognitive Engagement in the Wild. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(3), 1-26.