| |9 December 2017HIGHERReviewdents evolve within the institutions, identify those students most likely to succeed or abandon the institution, or even acquiring a detailed characterisation of those students in high school that can be influenced when choosing their next edu-cation stage. The big paradigm shift is that all these analysis have a direct connection with revenue streams.The increasing competition in the post-secondary sector is also leading to explore how to improve the student expe-rience through personalisation. Ideally, students should be exposed to the resources, engage in activities, and partici-pate in those interactions that are most adequate to maxi-mise their academic achievement. This personalisation is a trend that is well under way in other contexts such as online retail, entertainment, marketing, and so on. But in learn-ing, this level of personalisation requires comprehensive data sets about student behaviour. Interestingly, institutions are already collecting massive amounts of data about wireless us-age, physical presence in libraries and laboratories, and even engagement with a wide variety of online platforms (sessions in the learning management systems, discussion forums, en-gagement with recorded lectures and so on)The combination of these three information levels (in-stitutional, academic program, and every day interaction with electronic resources) should ideally translate into in-stitutions that identify students needs well before they join the institution, adapt their portfolio of programs to suit their needs, and provide a fully personalised experience all throughout their academic experience. However, although technically possible, this vision is far from reality and one of the obstacles preventing its fulfilment is the fragmentation of data sources.Educational institutions are complex organisational structures that require a combination of technical solutions at different level. As a consequence, the data is collected and stored across a wide variety of platforms and formats typical-ly integrated into different organisational units. The contrast between these rigid and usually siloed institutional units and the need for a holistic approach to data management causes significant friction.The design of a data-driven university requires a holistic approach. Institutional leaders must be aware of the emerg-ing data ecosystem that is multi-modal and fragmented and articulate their vision to deploy horizontal structures that are sensitive to all stakeholders. Institutional reporting must be maintained and enhanced, academic units at different levels must have data about how students traverse the existing pro-grams, and academics must have comprehensive information about how students are performing in their day-to-day activ-ities. And, this data must be available through simple, fully integrated and secure platforms. We are heading into an era where students will end up demanding this type of service as a way to increase their chances of success and make the most of their education. Educational institutions cannot afford to ignore this need.The vision is simple to describe, simple to understand, but significantly complex to implement. A cultural change is needed to fully embrace a mentality that acknowledges the potential of data-supported decision making in education-al institutions to bring improvements that will ultimately translate into more fulfilling experiences to students. Abelardo Pardo specialises in technology-enhanced learning with emphasis on learning and behavioural analytics, computer supported collaborative learning, and personalization of learning experiences. He is a member of the executive committee of the Society for Learning Analytics Research. In his (scarce) spare time, he maintains the blog T2T - Techies meet Teachers - which focuses on issues related to technology when used in learning experiences.Abelardo Pardo
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