Adaptive learning, big data, and the meaning of learning

Knewton defines adaptive learning as "A teaching method premised on the idea that the curriculum should adapt to each user." In a recent blog post, Knewton's COO, David Liu, expanded on this definition. Here are some extracts:

You have to understand and have real data on content… 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? 

On the other side of the equation, you really have to understand student proficiency… 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.

The idea of putting the right thing in front of a students is very cool. That's part of what we do here at Lectica. But what does Knewton mean by learning?

Curiosity got the better of me, so I set out to do some investigating. 

What does Knewton mean by learning?

In Knewton's white paper on adaptive learning the authors do a great job describing how their technology works. 

To provide continuously adaptive learning, Knewton analyzes learning materials based on thousands of data points — including concepts, structure, difficulty level, and media format — and uses sophisticated algorithms to piece together the perfect bundle of content for each student, constantly. The system refines recommendations through network effects that harness the power of all the data collected for all students to optimize learning for each individual student.

They go on to discuss several impressive technological innovations. I have to admit, the technology is cool, but what is their learning model and how is Knewton's technology being used to improve learning and teaching?

Unfortunately, Knewton does not seem to operate with a clearly articulated learning model in mind. In any case, I couldn't find one. But based on the sample items and feedback examples shown in their white paper and on their site, what Knewton means by learning is the ability to consistently get right answers on tests and quizzes, and the way to learn (get more answers right) is to get more practice on the kind of items students are not yet consistently getting right.

In fact, Knewton appears to be a high tech application of the content-focused learning model that's dominated public education since No Child Left Behind—another example of what it looks like when we throw technology at a problem without engaging in a deep enough analysis of that problem.

We're in the middle of an education crisis, but it's not because children aren't getting enough answers right on tests and quizzes. It's because our efforts to improve education consistently fail to ask the most important questions, "Why do we educate our children?" and "What are the outcomes that would be genuine evidence of success?"

Don't get me wrong. We love technology, and we leverage it shamelessly. But we don't believe technology is the answer. The answer lies in a deep understanding of how learning works and what we need to do to support the kind of learning that produces outcomes we really care about. 


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3 thoughts on “Adaptive learning, big data, and the meaning of learning

  1. I hope that in the advert disguised as a blog post you will at least acknolwedge that learning isn't simply either fact mastery or what you (annoyingly, for me anyway) call "robust" learning.    Even though it grates, I'll call it robust too, so you'll know what I mean.

    Ribust learning depends on fact mastery. Also, there are stages of life during which the child's mind is hardwired to memorize basic facts, techers today are too frequently begnning the robust stuff way too soon.  You try to memorize 1000 spanish vocabulary words and compete against your four year old and see who wins..  To the extent that education reformers have attempted to begin the robust stuff too soon, basic fact mastery has suffered, and along with it, robust learning later on.


    To illustrate, imagine that you were asked without the use of any tool other than your own arms and legs to move a large  pile of standard sized bricks from point A to point B in the shortest possible time.  You could move one brick at a time, but that would be the slowest method, and you could do two, three, four, etc.  At some point, you would figure out  the optimum number of bricks you could carry, and with each load carry exactly that number of bricks.  But what happens if you try to add even one more brick?

    In learning, when we ask a student to make a leap (a robust one at that!) that is only one step too far, he drops all of the bricks, not just a few, and collapses into a puddle of defeat.  Basic fact mastery enables robust learning.

    Knewton's plan stinks for a lot of reasons, so please try to ignore them (when they run out of Venture Capital, they're gone with the wind) and please don't lose sight of the basic fact that adaptive tutoring can homogenize a group of students with repsect to basic fact mastery, rendering them much more likely to be able to succeed at  robust learning.  

    It may even be a fool's errand (time will tell) to try to replace human teachers with "Robust Learning Technology."

    I give you this story for your further consideration:

    (If adding only 5 more numbers to the subjects' short term memory overrides the rational brain so effectively, imagine what happens to a student without basic fact mastery who is asked to be robust.)  The little monsters are all eating cake because they don't have the basics mastered.

    All comments mine, and not those of my overlords.



    • Hi Eric,

      I think you may have missed something. I’m not arguing that memorizing isn’t important. I’m arguing that it isn’t enough. Not nearly enough.

  2. Pingback: Adaptive learning. Are we there yet? - Lectica FAQLectica FAQ

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