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Predictive Analytics: Higher Ed’s Moneyball?

Yes, I admit it. I listen to NPR most evenings. I suppose I prefer my news read to me by people speaking softly very close into the microphone.

Tonight on NPR’s All Things Considered I listened to the latest installment in their Ideas series which explores innovation in education.

Neal Phillips, English professor at Valencia College in Orlando, FL spoke with NPR’s Eric Westervelt about his experience using predictive analytics to help identify students that need additional support early in their academic career. This section from the article, Higher Ed’s Moneyball?, is what made me decide to post tonight.

National and Valencia figures show that if students withdraw from or fail even one of their first five course attempts, their chance of graduating is cut in half. Fail or withdraw from two classes, and those chances are cut in half again.

But fast, early interventions might save them. “We want to help them get there as much as possible,” Philips says, “because, yeah, the data is not good if they don’t do well on the first try.

The story goes on to describe the technology solution Professor Phillips uses, but I couldn’t help think of how Oracle’s Big Data Discovery could help with this use case. Universities today have a wealth of information about their students. With sophisticated learning management systems such as Blackboard collecting a wealth of knowledge about each student’s performance the power of an analytical discovery tool like BDD can facilitate the correlative attributes that identify students in need of remediation…or just a motivational boost. With online students, who are sometimes at a greater risk of struggling, the learning management system tends to have even more data to work with as it is the primary communication tool between the professor and the student.

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Patrick Abram

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