The Science of Adaptivity
In general, “adaptive” refers to a computerized-learning interface that constantly assesses a student's thinking habits and automatically customizes material for him or her. Not surprisingly, though, competitors argue ferociously about who can claim the title of true adaptivity. Some say that a test that does nothing more than choose your next question based on whether you get the item in front of you correct—a test that steers itself according to the logic of binary branching—does not, in 2013, count as fully adaptive. In this view, adaptivity requires the creation of a psychometric profile of each user, plus the continuous adjustment of the experience based on that person's progress.
To make this happen, adaptive-software makers must first map the connections among every concept in a piece of learning material. Once that is done, every time a student watches a video, reads an explanation, solves a practice problem or takes a quiz, data on the student's performance, the effectiveness of the content, and more flow to a server. Then the algorithms take over, comparing that student with thousands or even millions of others. Patterns should emerge. It could turn out that a particular student is struggling with the same concept as students who share a specific psychometric profile. The software will know what works well for that type of student and will adjust the material accordingly. With billions of data points from millions of students and given enough processing power and experience, these algorithms should be able to do all kinds of prognostication, down to telling you that you will learn exponents best between 9:42 and 10:03 a.m.
They should also be able to predict the best way to get you to remember the material you are learning. Ulrik Juul Christensen, CEO of Area9, the developer of the data-analysis software underpinning McGraw-Hill's adaptive LearnSmart products, emphasizes his company's use of the concept of memory decay. More than two million students currently use LearnSmart's adaptive software to study dozens of topics, either on their own or as part of a course. Research has shown that those students (all of us, really) remember a new word or fact best when they learn it and then relearn it when they are just on the cusp of forgetting it. Area9's instructional software uses algorithms to predict each user's unique memory-decay curve so that it can remind a student of something learned last week at the moment it is about to slip out of his or her brain forever.
Few human instructors can claim that sort of prescience. Nevertheless, Christensen dismisses the idea that computers could ever replace teachers. “I don't think we are so stupid that we would let computers take over teaching our kids,” he says.
In March, Gerald J. Conti, a social studies teacher at Westhill High School in Syracuse, N.Y., posted a scathing retirement letter to his Facebook page that quickly became a viral sensation. “In their pursuit of Federal tax dollars,” he wrote, “our legislators have failed us by selling children out to private industries such as Pearson Education,” the educational-publishing giant, which has partnered with Knewton to develop products. “My profession is being demeaned by a pervasive atmosphere of distrust, dictating that teachers cannot be permitted to develop and administer their own quizzes and tests (now titled as generic ‘assessments’) or grade their own students' examinations.” Conti sees big data leading not to personalized learning for all but to an educational monoculture: “STEM [science, technology, engineering and mathematics] rules the day, and ‘data driven’ education seeks only conformity, standardization, testing and a zombie-like adherence to the shallow and generic Common Core.”