So what do we mean by a learning space and how is an intelligent learning space different?
Though the main thrust of the Jisc Intelligent Campus project is looking at how we can extend learning analytics to include and incorporate physical data, there is also space to discuss peripheral and related issues to the work. One aspect of this is the development and design of learning spaces as well as the use of data gathered from the use of learning spaces.
Generally most learning spaces are static spaces designed to allow for particular kinds of learning. Some have an element of flexibility allowing for different kinds of learning activity.
Often the pedagogy is shoe-horned into the space that is available and even if more appropriate spaces are available on campus, often they are unavailable for that particular slot or cohort.
A smart learning space would taken into account historical usage of the room and how people felt that the space either contributed or hindered the learning taking place there. You can imagine how users of the room could add to a dataset about the activities taking place in the room and how they felt it went.
You would think that data from the timetable could allow for this automatically, but timetabling data tells us about the cohort, the course they are on and the academic leading the session, most timetabling software doesn’t have the granular activity data in it.
The course module information may have the plans of the activity data within it, but may not have the room data from the timetable, nor may it have cohort details. You could easily imagine that some cohorts may be quite happy with undertaking group activities in a lecture theatre space, but there may be other cohorts of students who would work more effectively if the space was better at facilitating the proposed learning activity.
Likewise when it comes to adding feedback about the session, where does that live? What dataset contains that data?
Then there are environmental conditions such as heat, temperature, humidity, CO2 levels, which can also impact on the learning process.
So an actual smart learning space would be able to access data about the session from multiple sources and build a picture of what kinds of learning spaces work best for different kinds of learning activities, taking into account factors such as cohort, environmental conditions, the academic leading the session and so on…
These datasets could also be used to inform future space planning and new builds, but smart learning spaces are only the beginning. Taking a smart space and making it intelligent is an obvious next step.
An intelligent learning space would take this data, and then start to make suggestions based on the data. It would identify possible issues with the learning plan and make recommendations to either change the learning activities planned, or recommend a more appropriate space. An intelligent learning space would adjust the environmental conditions to suit the activities planned for that spaces, rather than users of the space having to manually adjust the conditions when it becomes too cold, too hot, too bright, stuffy, etc….
Making the timetabling software intelligent, well dynamic, could mean that rooms are not allocated to cohorts of students for a set amount of time, but rooms are allocated based on pedagogical need and student need and done as and when needed.
One of the key issues with all this is to collect and store the data somewhere, a centralised hub would be critical and that is something Jisc have built for the analytics service and would be used for the future Intelligent Campus service.