Published on 2020/11/12

Fixing Higher Education’s False Binaries

We have long experienced a false choice in academia: deciding between “career-ready” majors or those in the arts, humanities and social sciences. The presentation of this choice, however, fails to account for students’ individual experiences and skill sets, which recent developments in data collection could change. 

A decade ago, cloud computing hit the mainstream as a business technology. Being a leader in the field, I watched as companies were told they had to place everything in the cloud regardless of what the application was, prompting enterprise businesses to scramble to move from legacy on-premise solutions to the cloud. The mantra became: “Adopt or die!”

But we also learned—and have the scars to prove it—about the dangers of false binaries. For example, a 100-year-old reinsurance company like TransAmerica had no rational business reason to move their reinsurance application to the cloud, as it was already efficiently running within their mainframe. In hindsight, before even factoring the massive business risk moving presented, this was an overly simplistic and reductionist debate. 

For years, we’ve seen a similar debate play out in the world of higher education. Students are presented with a false choice between career-oriented majors, like STEM and business, that lead to more lucrative first jobs, or majors in the arts, humanities, and social sciences.

Forward-thinking institutions increasingly realize that this shouldn’t be an either-or proposition. In fact, there’s plenty of evidence that the most marketable—and enduring—educations are those that combine technical expertise with the uniquely human skills developed by the arts and humanities.

Yet, students continue to be presented with this false choice. This is driven by the fact that colleges themselves often know precious little—beyond major and GPA—about how specific education investments correlate to long-term success in an industry. 

That may be starting to change with the emergence of institutions, from large public flagships like the University of Washington to tiny liberal arts colleges like William Jewel, that are not only rejecting this binary choice but doing something to change it. They are working to better understand the utility of specific educational investments in the labor market, rather than accepting that the broad brushstrokes painted by a major and GPA determine a student’s destiny.

For higher education to do this more broadly, it must first leverage the power of data and partnerships to identify granular skills and competencies that lead to predictable success in the workplace.

Almost every state now has a Statewide Longitudinal Data System that connects K12, higher education, and workforce data in order to better understand education and career outcomes. But while this is critical infrastructure, those systems simply don’t provide enough granular information about program-level outcomes, much less about individual courses and competencies, to be useful for consumers.

The U.S. Chamber of Commerce Foundation’s T3 Innovation Network, and technology solutions that bring education providers and employers together to connect more detailed and usable data on the value of discrete knowledge, skills and competencies. Artificial intelligence is able to analyze millions of data points to predict the labor market value of particular educational experiences, but it’s only as good as the information we feed it. 

Increasingly, institutions are searching for ways to go beyond publicly available data and connect directly with employers. For example, forward-thinking institutions like American University and the University of Kansas are reimagining career services and curriculum alike, using data to predict labor market demand and measure the ROI of specific courses and competencies, not just those of a generic program or degree. 

While the work hasn’t been easy, slowly but surely colleges and universities are tilting the playing field for students. In doing so, they are taking the guesswork out of the specific job titles, career arcs, and earnings potential that a student can expect from specific degrees, credentials, and courses.

To do this well, however, we must be willing to move past labels and do the hard work of really understanding what students learn and how that translates to the labor market. Data from an individual’s experiences captured over time must be fully considered alongside the skills they learned and the competencies they attained to more accurately predict their probability for success in a role. 

Leveraging employer data as part of the translation process can help employers verify that the skills and competencies signaled are ones that hiring managers can trust, allowing them to move past false proxies, like degrees and majors. This will naturally erase the false choice of hiring career-ready majors or those from the humanities and arts. It will allow us to see what has always been true: there are no career-ready majors but rather career-ready graduates.

Business journalist and author George Anders summed up this challenge perfectly in his book, You Can Do Anything: The Power of a “Useless” Liberal Arts Education: “What’s so poignant about this mismatch is that a winning campus-to-career alliance is within reach — if only the combatants could talk about their values, needs, and achievements in a shared language that makes sense to one another. Instead, scholars, students, and employers are at odds because of an agonizing translation problem.”

In an increasingly dynamic workplace that requires a mix of technical and human skills, it’s become more difficult to predict how an individual’s unique skill set will actually transfer to a given job function. The good news is this move would not only be good for students but also for those employers and institutions. Presenting individuals and organizations with false choices has never been the best way — it’s just been the way we’ve always done it.

 

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