Artificial Intelligence Could Help Colleges Better Plan What Courses They Should Offer

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Big data could help community colleges better predict how industries are changing so they can tailor their IT courses and other programs. After all, if Amazon can forecast what consumers will buy and prestock items in their warehouses to meet the expected demand, why can’t colleges do the same thing when planning their curricula, using predictive analytics to make sure new degree or certificates programs are started just in time for expanding job opportunities?

That’s the argument made by Gordon Freedman, president of the nonprofit National Laboratory for Education Transformation. He’s part of a new center that will do just that, by building a data warehouse that brings together up-to-date information on what skills employers need and what colleges currently offer—and then applying artificial intelligence to attempt to predict when sectors or certain employment needs might be expanding.

He calls the approach “opportunity engineering,” and the center boasts some heavy-hitting players to assist in the efforts, including the University of Chicago, the San Diego Supercomputing Center and Argonne National Laboratory. It’s called the National Center for Opportunity Engineering & Analysis.

Ian Roark, vice president of workforce development at Pima Community College in Arizona, is among those eager for this kind of “opportunity engineering” to emerge.

He explains when colleges want to start new programs, they face a long haul—it takes time to develop a new curriculum, put it through an internal review, and then send it through an accreditor.

“Sometimes by the time we get all that done, the market has shifted or the employer has up and moved away,” Roark says. “What’s happened over time is that colleges have become very risk-averse to starting new programs because of the time it takes to get it going—and then there’s not really a great certainty that that investment is going to pay off.”

Other players are already trying to translate the job market into a giant data set to spot trends. LinkedIn sits on one of the biggest troves of data, with hundreds of millions of job profiles, and ambitions to create what it calls the “economic graph” of the economy. But not everyone is on LinkedIn, which attracts mainly those in white-collar jobs. And companies such as Burning Glass Technologies have scanned hundreds of thousands of job listings and attempt to provide real-time intelligence on what employers say they’re looking for. Those still don’t paint the full picture, Freedman argues, such as what jobs are forming at companies.

“We need better information from the employer, better information from the job seeker and better information from the college, and that’s what we’re going after,” Freedman says.

Land of Opportunity?

The first step is to better integrate the datasets that do exist, says Ioana Marinescu, assistant professor of economics at the University of Chicago’s Harris School of Public Policy. “There is a lack of coordination between the different actors,” she argues. “Right now we don't have a tool that fully connects the labor market with the skill and educational institutions all in a one-stop shop. This is what we have the ambition to do.”

She says that the center’s data could also be used to build tools for students, so they could better understand what courses and programs best lead to jobs.

And the data could be a boon to scholars like Marinescu who are analyzing labor markets. “There is a question, is this country still a land of opportunity?” she says. “That’s what economists have been asking lately and trying to understand how opportunities are created, especially for disadvantaged students and communities.”

Details of the center, which was announced today, are still being worked out, and the grand ambition will face plenty of challenges. First of all it has to convince those that hold the data to share it. One idea is to ask community colleges to upload detailed information about all of their relevant course offerings to the center’s data warehouse, and Freedman says they are talking to groups like the League for Innovation in the Community College to try to help get buy-in from colleges.

Finding a sustainable way to pay for the effort will also be a challenge. So far the work is funded largely by grants, some of which were already underway but will now be coordinated through the new center.

And then there’s the challenge of making accurate predictions of where something as complicated as the job market is heading.

“Just like predicting an election you can’t say that this is actually going to happen,” Freedman admits. “But if you look at the patterns, you can get a better idea.”

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