Researching “student expectations” and their accommodation experiences, I found myself down a rabbit hole. I came across a post regarding a student who had just arrived at university. After a week, their anxiety had grown because depression was preventing engagement with fellow students.
The most helpful suggestion, from a university tutor, was for the student to inform their personal tutor of the situation, who could then advise on the support available. They explained that they had seen stress build up in a student who didn’t do this, and the situation had quickly snow-balled.
In most cases, Universities have good procedures in place, but they can only deal with situations that they know about. If help isn’t sought, there is little they can do to force action on adult students.
In the early stages many people will simply ‘grin and bear it’ and carry on in what they consider to be a ‘normal’ manner. But for people with which they interact, there will be ‘tells’ which could trigger concern. Absence from lectures, irritability and low self-esteem are some of the signs, as is a lack of sociability or staying in one’s room.
A building energy management system, such as Irus, seems an unlikely tool for helping with student welfare. But, when the data it absorbs is interpreted from a welfare viewpoint, it can provide useful insights for those concerned.
As well as managing energy use, the system logs data that can help paint a picture of room occupancy, without having to knock on the door.
For example, when the controller on the wall is activated to boost heat within the room, each button press is logged and indicates the time that heat was called for, this displays presence and occupancy patterns. A lux sensor will monitor light levels, and to a trained eye indicates if curtains or blinds have been drawn regularly. Humidity and CO2 sensors will detect occupier presence, while the sound-pressure monitor shows decibel levels from within the room and the surrounding area. This can also build evidence, if there are complaints, of anti-social noise from neighbouring rooms.
Automatic alerts can be set to indicate unusual levels, but routinely, unless there are concerns, the data is not automatically analysed and therefore isn’t deemed intrusive.
A system such as this is designed to keep students comfortable, with the bonus of helping to keep them safe.
