European Journal of Higher Education IT 2017-1

Better insight into the teaching process and targeted feedback to students, ultimately resulting in improved education: that is the idea behind learning analytics. Learning analytics makes it possible to follow students’ digital footsteps. These footsteps can be recorded and analysed, leading to the creation of comprehensive data collections. With this data, it is possible to make predictions about, for example, the quality of the teaching materials used, how teachers and students interact with the material, how the digital learning and working environment is used, etc. Learning analytics offers a great many possibilities, but how can an institution use it successfully? This is the reason why SURFnet started the Learning Analytics Experiment for Dutch institutes for higher education to gain experience with learning analytics. With this experiment, SURFnet demonstrates the possibilities of learning analytics in education. In this paper we present the set-up of the Learning Analytics experiment, the learning analytics architecture and infrastructure used for this experiment as well as the preliminary results and further work.

Many institutions strive to provide education that matches the learning needs of each individual student. This places high demands on the digital learning environment (DLE) of an institution. One system that meets all the needs and requirements of every student and lecturer, does not exist, which is why a modular approach seems the obvious choice.

In 2016, SURFnet translated this modular approach into a functional model for the DLE. This resulted in thirteen components for the DLE, that support several educational processes. Splitting the DLE into components is supportive for institutions in two ways: 1. It helps institutions to decide to what degree of control and management the components have to be placed in the architecture of the digital learning environment; 2. It helps institutions to determine in which way the components are connected and able to interact.

It is a simple idea, really.

Learning is all about feedback. Runners, for example, use apps like the RunKeeper. Research shows that apps like that enhance engagement and results. And people think it is fun. The essence being that the behavior of the runner is tracked and communicated back to the runner in a dashboard.

We, at Fontys University, wondered if you can reach the same positive effect if you had a dashboard for study behavior. For students. And what should you measure, track and communicate? We wondered if we could translate the Quantified Self Movement into a Quantified Student.

It seems that every few years a new development (usually featuring technology), be it online learning or, more recently, MOOCs, heralds the ‘death of the university campus’. The physical evidence however always appears to contradict these predictions. Campus education continues to be relevant because learning is a social process but the nature of the facilities we need to provide is changing significantly.

This paper looks at trends in the use of digital technologies on-campus and the organisational and management issues they pose. We concentrate primarily on the use of such technologies in spaces used for formal and informal learning and teaching. The paper makes the case that successful integration of digital technology is key to delivering excellent learning experiences on campus and we make recommendations as to how the EUNIS community can take steps to help develop smarter campuses in their own institutions.

The authors are professionals who have supported a range of different universities in various aspects of learning space development. In the Congress presentation that accompanies this paper we illustrate the points with worldwide examples.