Adaptive Learning Technology's Lasting Impact on Instruction and Ed-Tech Innovation
Published January 20, 2015
How adaptive learning technology is changing dynamics in classrooms and ed-tech innovation centers
By Ulrik Christensen | Contains excerpts of Ulrik's guest segment on the Innovation Navigation broadcast by the Wharton School's Business Radio in November of 2014
What is the real goal of adaptive learning technologies?
So in the educational systems, we face this challenge that all the students are different. In the classic educational system where you had a teacher who had some time to address the individual students, but not enough, and you then had homework systems and books that were one size fits all. About 10 years ago we started doing a lot of research into how we could individualize that support of the teachers, so that each student could get a much more personalized learning experience and found that what was required was that the computer technologies needed to be able to adapt to the needs of the individual. All students learn differently, we're all wired differently in our heads, we all learn at different paces, and we all forget different things, that’s truly what adaptive is about - making sure that each student gets what he or she needs, and doesn't waste time on what they don't need.
From the student's perspective the biggest challenge they have is that they have way too much rehearsal and practice right before the finals so they cram. If we can help the students do that, we free up resources so that the teachers gradually can start spending more intelligent time with the students during the semester, and helping students get more out of it. So, instead of making a revolution in education, we're accelerating the evolution of education by doing it this way. We have products that are completely flipping the classroom model as well - if teachers are interested in doing that.
How are the products being designed by McGraw-Hill truly different?
We have designed products thinking that we can predict how students will learn, but we cannot. We've been doing research in this, and we've been humbled by how differently students learn. The biggest learning we have had is to not make a classic induction problem - because we can see part of the solution, we think we have all of it - it's much more a question of designing a technology that is so flexible and so accommodating that it can actually handle the students at the fringes of education, who are learning in very different ways. And there is an enormous reward at the end of that journey – or going through that journey - which is you see some of the students that otherwise get lost in the educational system; the students with learning disabilities, with ADHD, students who need to be accelerated fast. If you're able to build systems where you are humble in terms of not thinking that people are machines but rather biological organisms, you are actually able to accommodate some of these kids. It's incredible what the power is of assuming a biological approach and understanding that humans are humans, not machines, instead of an engineering approach that's really interesting.
How is the innovation process accelerated by McGraw-Hill's collaboration with educators?
Most of the innovations we make are driven by patterns we see with students who are learning in different ways and where we see needs for teachers who are describing a need or have headache of some kind. When we start seeing the pattern - we say; ‘wow, we may be able to solve this’. Then we go back in the lab and try to see how we can use hardcore computer science in collaboration with teachers to come up with a solution. We've made very few breakthroughs by ‘tech only’, most of it has been in a close collaboration with empowering teachers to explain to a computer how they would like to teach. So among other things, we're building programming languages which is a hard core computer science discipline, so that the teachers can tell the computer what a domain is like. The way we then test it is, we use an aggressive - a very aggressive - agile approach, where we prototype very, very early. We have programmers who are able to influence production environments on a very short cycle when we learn things. And we have 3000 to 4000 authors who are working on these systems and are able to change the way it's being taught when they see students learn something differently. We also protect ourselves against failures by having automated systems that if things do not work well. So it's very much a question of having the interaction between computer science and technology on one side, and the great teachers on the other side.