Knewton is a pioneer in adaptive learning. Back in 2008, when Knewton started building an adaptive learning platform, hardly anybody had heard of adaptive learning, just a handful of academic specialists and researchers.
Google “adaptive learning” today, and you’ll find 565,000 search results.
It’s gratifying to see more people talking about adaptive learning and more companies committing themselves to personalizing education.
At the same time, “adaptive” and “personalized” have become education buzzwords. These terms get used so often and for such a wide range of products and services that you could almost think that any learning tool with a digital component could be considered “adaptive.”
So what does adaptive learning mean?
At the 2016 ASU–GSV Summit, Knewton president and COO David Liu gave a great answer to that question. To hear it, start the embedded video at the 30-minute mark.
Or read the transcript below, which has been condensed and edited for clarity.
When we think of “adaptive,” it’s real science. It is a real practice. It takes real expertise and experience and large, large data sets.
I’m not going to get too technical, but let me just break it down in this way:
You have to understand and have real data on content. You really have to have a detailed understanding of how the content is working: Is the instructional content teaching what it was intended to teach? Is the assessment accurate in terms of what it’s supposed to assess? Can you calibrate that content at scale so you’re putting the right thing in front of a student, once you understand the state of that student?
If you don’t truly understand data at that level from that content, you’re making guesses. People will call that adaptive, because something is changing, and that’s completely irresponsible.
Adaptive learning means understanding at very granular level, if required, what each piece of content is supposed to be doing.
And doing it at scale: I’m talking about millions of pieces of content.
And doing it in real time.
On the other side of the equation, you really have to understand student proficiency. Again, not guessing because they got a question either right or wrong, which is adaptive testing — that’s been around for decades. It’s actually understanding and being able to predict how that student is going to perform, based upon what they’ve done and based upon that content that I talked about before. And if you understand how well the student is performing against that piece of content, then you can actually begin to understand what that student needs to be able to move forward.
And that’s all in the context of this teaching environment.…
It is very important for people to understand what “adaptive” really is. It is absolutely data-driven. It is absolutely data-driven at scale.
You have to understand content and proficiency of students.
And if you don’t, you can build any kind of recommendation engine you want, but you’re literally spitting out randomized answers, and that’s completely irresponsible.