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Labster CEO: why has adaptive learning fallen short so far?

Adaptive learning Adaptive learning
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Umberto Salvagnin

Adaptive learning was set to change the way we learn. Yet without the right algorithms in place – it becomes impossible to scale.

Given the immense advances in education technology in the past ten years, one might expect a corresponding push in outcomes for students.

However, the fact remains that seven percent of US high school students drop out and nearly half of those who start college don’t finish within six years.

Adaptive learning has long been championed as an innovation that could finally move the needle for education outcomes.

Also known as personalized learning, adaptive learning is defined by the Tyton Partners, the leading provider of investment banking and strategy consulting services to the global knowledge sector, as a pedagogical method or process that draws on observation to inform tailored student educational interventions designed to increase the likelihood of learner success.

Even more, The Gates Foundation has invested heavily in adaptive learning, such as awarding $1 million in grants for universities that adopt adaptive learning programs.

Adaptive learning software has been bandied about as a “magic pill” (in the words of Knewton founder Jose Ferreria) for improving learning outcomes and increasing the effectiveness and efficiency of school curricula.

But Michael Feldstein, a digital education consultant and co-founder of the ed blog e-Literate, told NPR, “Comments, context, facial gestures, eye contact and so on: There is a lot of bandwidth there. Knewton really has a very narrow bandwidth in terms of what they can observe about the student relative to what a human teacher can observe.”

Feldstein didn’t dismiss Knewton out of hand, but he continued by saying, “We have no reason to believe that it [Knewton] is capable of achieving anything like this magic robot in the sky that he’s selling — or, as he put it recently in Wired magazine, a ‘magic pill.’ ”

As big data and machine learning has become the norm for software giants, adaptive learning companies are beginning to flex their data muscles to collect millions of data points from students, similar to Google’s autonomous car efforts.

But similar to driverless cars, truly effective adaptive learning software may be years away.

Although some studies have shown that adaptive learning can improve test scores and improve retention, administrators have thus far seen mixed results.

However, their optimism for adaptive learning technology has not diminished. In a recent Gallup and Inside Higher Ed survey, two in three college and university presidents believed adaptive learning would make a “positive impact on higher education.” So while very few doubt the promising potential of adaptive learning technology, the vision for scalable, personalized education is still a ways off.

As Mary Cullinane, chief content officer and EVP of corporate affairs at Houghton Mifflin Harcourt, recently told THE Journal, “There has been a lot of noise in the marketplace around adaptive learning, but so many of the offerings are just binary ‘if you get this question wrong you go here; if you get it right, you go there’ kinds of things.

True adaptivity isn’t just about understanding that the kid got the question wrong, but why the kid got the question wrong.”

Cullinane later added, “But we do feel that this is technology that can actually deliver on its promise. It’s going to take a little time to make sure that we do it right.” The state of adaptive learning seems eerily similar to virtual reality ten years ago — a lot of chatter about possibilities, but showing mixed results.

With VR companies like Oculus making VR technology more accessible, major players in media, gaming, and education have been able to create engaging VR applications that anyone with a smartphone can experience. For example, it has become so mainstream that the New York Times released a VR app just the month.

The hope is adaptive learning can make the same progress as technology in general has enhanced our daily lives. With more emphasis on big data, psychometrics and creating products where the real learning outcomes for students are measured relentlessly, we may start to see some significant progress in the efficacy of adaptive learning software.

In my view, the key is to make sure that the advanced algorithms put in place actually work – and to pair the adaptive learning with cutting edge learning methods based on educational psychology and good UI/UX design.

As Adam Newman, founding partner of Education Growth Advisors, recently said, “Whatever the approach,” Newman said, “the essential premise of adaptive learning — and it is a premise, not a panacea — is that it holds the promise of a more outcomes-oriented system that is more efficient in the use of time and resources.”

There is no doubt in my mind that adaptive learning will be key in providing game-changing educational software that will greatly impact learning effectiveness in the future. However we need to recognize that truly effective online learning tools including adaptive algorithms are very challenging to make, and it will probably take years before they arrive at scale, and requires a shift in focus from learning method to student outcomes.

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