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Transformative interaction between digital technologies and people for a sustainable indoor climate in schools (DIGG-MIN-SKOLE)


A good indoor environment at school is important for the health and well-being of pupils and staff, and has a significant impact on pupils' learning outcomes.

Good maintenance of buildings and operation of the technical facilities is important to have good indoor climate, but it is also crucial that staff and pupils use the school buildings correctly and are involved in practical indoor environment work at school level.

This requires that staff and pupils are aware of and have knowledge of how their behavior affects the indoor climate, as well as how the individual can contribute to ensuring as good an indoor climate as possible at the school.

Data from indoor climate sensors, combined with information about how employees and pupils experience indoor climate and related health problems, can provide new opportunities to both identify indoor climate problems, find the cause and identify the right measures, and to create new tools that engage and involve the users of the school buildings.

Today's schools are largely equipped with sensor systems for indoor climate, but there are no tools to collect data on user experiences. Data from integrated sensors is to a small extent available to the school. Information on connections between sensor data and experiences is currently lacking.

DIGG-MIN-SKOLE vill combine data from sensors that are an integral part of the school's technical facilities and/or individual indoor climate sensors with self-acquired data related to user experience.

This data will be used to develop a machine learning model that can estimate the probability that the users will experience reduced well-being/health problems, which factors in the indoor climate are most likely to be the cause of the health problems (temperature, light conditions, noise, CO2 etc.) and identify targeted mitigating measures at school /classroom level.

Unit managers, staff and students must contribute to the design of (part) tools so that the results from the machine learning model are suitable for use in the school's everyday life. The end result will be a technical specification and demonstration of a user-oriented management system (BOF) in several schools.