Connecting the Data Dots on Students

academic advising
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Campuses have spent years transforming their operations from analog to digital, department by department. Financial records are all electronic. Just about every course, online or in-person, now uses learning-management systems. Keycard systems count entry into dorms, gyms, and libraries.

But until recently all that digitization stayed siloed. That is changing, as colleges work to connect the dots, by creating data warehouses that draw on activity across systems, sometimes in real-time. And institutions are putting the data into the hands of administrators charged with student success, giving professors a richer picture of their students and, in some cases, letting students get answers to their questions in new ways.

What makes this round of technology different is artificial-intelligence algorithms, which can bring raw numbers into focus in new ways and can detect patterns fast enough to make meaningful interventions.

Connecting the dots can form a rich paint-by-number of each student, a portrait colleges hope will increase retention and deliver clear ROI on the tech investment. And every student saved from failure helps colleges maintain enrollment during this time of challenging demographics.

How Predictive Analytics Helps Universities Reach Their Goals

Universities use a number of data analytics models; one of the most common is to use predictive analytics modules to drive decision-making. Predictive analytics use historical data, collected over long periods of time, to inform future choices. Universities can use predictive analytics models to help with several agendas, from improving recruitment and retention to meeting students’ needs. Combined with real-time data analytics, predictive models can use stream processing to calculate what might happen in the future, but at a faster pace.

At the University of Central Florida, for example, administrators are collaborating with Microsoft to use predictive data analytics to understand trends in student and faculty success, university research and alumni donations. Using data dashboards, UCF can use data points collected in real-time to help students who are displaying patterns that show they are struggling academically, improve professors’ curricula and more effectively collect money for new campus initiatives and scholarships.

At the University of Alabama, the use of predictive analytics found that students who asked for copies of their transcripts might be at risk of leaving the university. Now, administrators can note when a student puts in such a request and offer academic and campus resources to encourage those students to stay.

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