Big data and learning analytics
By now, big data and learning analytics are familiar buzz words for anyone working in higher education. Thanks to student information systems (SIS) like Banner and Blackboard, universities collect an ever increasing volume of data about campus life and student performance. But knowing what data to collect and leveraging it for better outcomes is not always easy.
“SIS gives universities new tools, but the real question is how we use these tools,” says Anthony Bichel, president of Leading Edge Learning, a consultancy that helps universities implement learning analytics. “There’s an expectation that universities make data-driven decisions, but a lot of the time, universities are not maximizing the potential of their SIS.”At UBTech 2014, Bichel’s Special Interest Group Session on big data and learning analytics will look at how universities can maximize and expand their data-driven decision making.
It is clear that data collection and analytics can vastly improve practices in higher education. “Having real-time student data allows you to make real-time interventions to help struggling students,” says Bichel. “Universities can get quick answers to specific questions like, ‘What percentage of students are succeeding with one professor versus another?’”
But the universities that Bichel works with often spend a chunk of money on an expensive SIS without developing a plan to assess and leverage the new influx of data. “Most universities that purchase enterprise-level applications don’t make much use of them,” says Bichel. “They construct limited dashboards that only a few senior leaders have access to—and they mostly focus their efforts on retention.”
In order to best utilize collected data, Bichel recommends that universities think about who will have access to it, and what sort of policies will be derived from that data, before installing a SIS. “Make a plan before you get an expensive SIS online—you have to think beyond about the technical implementation,” he says. By developing a data-driven strategy beforehand, universities can avoid being caught off guard. In Bichel’s experience, many universities realize that they lack the staffing resources to analyze data and implement interventions only after purchasing an expensive SIS. “I often recommend a take-it-slow approach,” he says.
Bichel acknowledges that here are real security concerns when it comes to collecting student information and recommends that universities have frank conversation about data-access. But overall, he advocates spreading the data around. “I’m pro-information. I think you have to put the data in the hands of people who can make a difference,” he says.
Click to learn more about the Special Interest Group Session, big data and learning analytics, at UBTech 2014.
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