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Big Data for Higher Education


 
  
New students are more likely to drop out of online colleges if they take full courseloads than if they enroll part time, according to findings from a research project that analyzed data on 640,000 students. The data can enable the colleges to become Match.com for Higher Ed.


Inside Higher Ed, February 1, 2012, Paul Fain

Education New students are more likely to drop out of online colleges if they take full courseloads than if they enroll part time, according to findings from a research project that is challenging conventional wisdom about student success.

But perhaps more important than that potentially game-changing nugget, researchers said, is how the project has chipped away at skepticism in higher education about the power of "big data."

Researchers have created a database that measures 33 variables for the online coursework of 640,000 students - a whopping 3 million course-level records. While the work is far from complete, the variables help track student performance and retention across a broad range of demographic factors. The data can show what works at a specific type of institution, and what doesn't.

That sort of predictive analytics has long been embraced by corporations, but not so much by the academy.

The ongoing data-mining effort, which was kicked off last year with a $1 million grant from the Bill and Melinda Gates Foundation, is being led by WCET, the WICHE Cooperative for Educational Technologies.

Match.com for Higher Ed?

The data set has the potential to give institutions sophisticated information about small subsets of students - such as which academic programs are best suited for a 25-year-old male Latino with strength in mathematics, for example. The tool could even become a sort of Match.com for students and online universities, Ice said.

That application is nowhere near to being a reality, in part because institutions are loath to share competitive information with each other, or the general public. But researchers said the project will almost certainly help other colleges follow Rio Salado's lead in using predictive analytics to help design better academic programs.

"If institutions of higher education did more of this type of analytics," Díaz said, they could tell their prospective students: "Look, these are the kinds of students who tend to have more success at our institution."

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