How we learn has been a hot topic for as long as man has been trying to gain knowledge. Methods come and go, but we never seem to reach a definitive answer as to the best way for educators to teach students. Technology and teaching have collided in the latest methodology to gain attention—adaptive learning. Could this replace educators and be the future of education?
What is adaptive learning?
So what exactly is adaptive learning? Paul Fain wrote “Intel on Adaptive Learning” April 4, 2013, for insidehighered.com and said the approach could be “loosely defined as data-driven tools that can help professors mold coursework around individual students’ abilities.”
Sound great, right? Well colleges are struggling to figure out which companies offering adaptive learning strategies are right for them and how to incorporate them. The Bill & Melinda Gates Foundation conducted a study to help, which analyzed the various vendors and what their various companies were capable of. Included in the study was “a chart for marking off the scale, scope and sophistication of adaptivity by each company,” Fain writes. The chart shows, “for example, if the technology can be used as a study aid, supplemental instruction for a course, or as an entire course.”
Best way of teaching?
Perhaps the most well-known of the adaptive learning companies, for the moment, is Knewton. What began as an online test prep service, according to Jill Barshay’s July 7, 2013 article for wired.com, “Q&A With Knewton’s David Kuntz, Maker of Algorithms That Replace Some Teacher Work,” has become a “big data machine [that] is the hidden engine inside the online courses provided by Pearson or Houghton Mifflin Harcourt or directly by a school, such as Arizona State University and University of Alabama.”
Kuntz shares with Barshay that “The question our machine is trying to answer is, of all of the content that’s available to me in the system, what’s the best thing to teach you next that maximizes the probability of you understanding the big things that you need to know?” He adds, “It’s not just what you should learn next, but how you should learn it.”
Knewton has mathematical models that look at how a student learns, what they are engaged by, what bores them, what they are proficient at and what frustrates them. This information is filtered to create a recommendation for what will help the student learn the subject at hand. The method seems to be particularly effective in the areas of math and science.
Results at colleges
Adaptive learning is not meant to replace teachers, per se, merely to help students learn at their own pace and allow teachers to spend time helping individuals or small groups as needed. Arizona State University adopted the technology via Knewton in the summer of 2011. According to an article in the July 7, 2013 issue of Newsweek, “What If You Could Learn Everything?” by Anya Kamenetz, the results have been very positive for ASU. “After two semesters of use, course withdrawal rates dropped by 56 percent and pass rates went from 64 percent to 75 percent.”
Kamenetz shared a comment Irene Bloom, a math lecturer at ASU, made on an education blog about the effectiveness of the program. Bloom said, “I have so much more information about what my students do (or don’t do) outside of class. I can see where they are stuck, how fast they are progressing, and how much time and effort they are putting into learning mathematics.”
Adaptive learning’s future
Researchers are already thinking of other ways they can create adaptive learning software that, if it doesn’t replace educators, will definitely aid teachers. Ki Mae Heussner blogged for Gigaom.com about efforts to design software that monitors students’ facial expressions. The July 28, 2013 post, “Frustrated? Confused? Learning software could watch your face for signals and match content to your emotions,” shared how researchers at North Carolina State University have built a software package that can predict how effective an online tutoring session was “based on what the students’ facial expressions indicated about their emotions.”
The idea is that this kind of emotion-sensing technology would work well with adaptive learning and be just one more way to help determine how well students are learning. Joseph Grafsgaard, a Ph.D. student at NCSU and lead author of the paper, doesn’t see the automated facial expression recognition program replacing teachers however. Rather he feels that the point of adaptive learning is to complement the efforts of educators, not replace them.
What do you think? Is adaptive learning the future? Will it help teachers and students or hurt the learning process? Let us know your thoughts in the comments.