This article is from the In-Depth Report Learning in the Digital Age

How Big Data Is Taking Teachers Out of the Lecturing Business

Schools and universities are embracing technology that tailors content to students' abilities and takes teachers out of the lecturing business. But is it an improvement?

Arizona State is one of the earliest, most aggressive adopters of data-driven, personalized learning. Yet educational institutions at all levels are pursuing similar options as a way to cope with rising enrollments, falling budgets and more stringent requirements for student achievement. Public primary and secondary schools in 45 states and the District of Columbia are rushing to implement new, higher standards in English-language arts and mathematics known as the Common Core state standards, and those schools need new instructional materials and tests to make that happen. Around half of those tests will be online and adaptive, meaning that a computer will tailor questions to each student's ability and calculate each student's score [see “Why We Need High-Speed Schools,” on page 69]. School systems are experimenting with a range of other adaptive programs, from math and reading lessons for elementary school students to “quizzing engines” that help high school students prepare for Advanced Placement exams. The technology is also catching on overseas. The 2015 edition of the Organization for Economic Co-operation and Development's Program for International Student Assessment (PISA) test, which is given to 15-year-olds (in more than 70 nations and economies so far) every three years, will include adaptive components to evaluate hard-to-measure skills such as collaborative problem solving.

Proponents of adaptive learning say that technology has finally made it possible to deliver individualized instruction to every student at an affordable cost—to discard the factory model that has dominated Western education for the past two centuries. Critics say it is data-driven learning, not traditional learning, that threatens to turn schools into factories. They see this increasing digitization as yet another unnecessary sellout to for-profit companies that push their products on teachers and students in the name of “reform.” The supposedly advanced tasks that computers can now barely pull off—diagnosing a student's strengths and weaknesses and adjusting materials and approaches to suit individual learners—are things human teachers have been doing well for hundreds of years. Instead of delegating these tasks to computers, opponents say, we should be spending more on training, hiring and retaining good teachers.

And while adaptive-learning companies claim to have nothing but the future of America's children in mind, there is no denying the potential for profit. Dozens of them are rushing to get in on the burgeoning market for instructional technology, which is now a multibillion-dollar industry [see box at left]. As much as 20 percent of instructional content in K–12 schools is already delivered digitally, says Adam Newman, a founding partner of the market-analysis firm Education Growth Advisors. Although adaptive-learning software makes up only a small slice of the digital-instruction pie—around $50 million for the K–12 market—it could grow quickly. Newman says the concept of adaptivity is already very much in the water in K–12 schools. “In K–12, the focus has been on differentiating instruction for years,” he says. “Differentiating instruction, even without technology, is really a form of adaptation.”

Higher-education administrators are warming up to adaptivity, too. In a recent Inside Higher Ed/Gallup poll, 66 percent of college presidents said they found adaptive-learning and testing technologies promising. The Bill & Melinda Gates Foundation has launched the Adaptive Learning Market Acceleration Program, which will issue 10 $100,000 grants to U.S. colleges and universities to develop adaptive courses that enroll at least 500 students over three semesters. “In the long term—20 years out—I would expect virtually every course to have an adaptive component of some kind,” says Peter Stokes, an expert on digital education at Northeastern University. That will be a good thing, he says—an opportunity to apply empirical study and cognitive science to education in a way that has never been done. In higher education in particular, “very, very, very few instructors have a formal education in how to teach,” he says. “We do things, and we think they work. But when you start doing scientific measurement, you realize that some of our ways of doing things have no empirical basis.”

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This article was originally published with the title "Machine Learning."

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