Intelligent Campus Data Requirements


We’re working on ways to improve the student experience by capturing and analysing the many kinds of data that can be collected across university and college campuses. This research is developing alongside our effective learning analytics project and our work to build a learning analytics service.

At the core of the learning analytics service is the learning data hub where academic and engagement data is collected, stored and processed.

We’ll extend the learning data hub to enable data to be gathered in from physical places (movement trackers, heat and CO2 sensors, for example) and from systems that record and monitor space and equipment usage, timetabling and other activities.

Our vision for the intelligent campus is that we will work with existing services rather than compete against them. We will establish a standardised data hub for the intelligent campus. This data hub will make it easier for institutions to collect data from their various software and devices and will allow vendors to build tools that analyse data from a range of software and devices to deliver tools and insights to students, teachers and institutional managers.

This is the same model we have used for learning analytics and after some initial scepticism learning analytics vendors have embraced the concept as it reduces their cost of sale to institutions and allows them to access richer data more easily. Similarly institutions see the value in the learning data hub approach since it takes the pain away from extracting data from institutional systems and helps them avoid vendor lock in as standardising the data means it is easy to switch all their existing data to a new provider. The diagram below illustrates our proposed approach.

Intelligent Campus Data Requirements

The Jisc learning data hub sits in the middle layer of the diagram and works with existing and new vendor products in the top and bottom layers. The top layer relates to the use cases we have identified in earlier blog posts.

The other significant differentiator is the link to our learning analytics data. Most existing products focus purely on the smart campus idea of making the estate more efficient and responsive to user needs. By using this data alongside our learning analytics data, we can explore how the use of the campus relates to learning progress and outcomes and use these insights to make improvements.  

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