Questioning personalized learning
“Personalized learning” is among the current buzzwords in K-12 education. Many buzzwords lack clear definitions, are used to describe a wide range of practices, and are linked to unsubstantiated hype, and “personalized learning” is no exception. For one example see this NPR article in which a CEO refers to his company’s software as “a robot tutor in the sky that can semi-read your mind and figure out what your strengths and weaknesses are, down to the percentile.”
Given that a substantial part of the hype—but not nearly all—comes from companies, it’s refreshing to see a CEO provide a more realistic take on the reality of personalized learning. The comments of Larry Bergman, CEO of Amplify, as published on Rick Hess’s blog on EdWeek, are worth quoting at length:
"Until a few years ago, I was a great believer in what might be called the "engineering" model of personalized learning, which is still what most people mean by personalized learning. The model works as follows:
You start with a map of all the things that kids need to learn.
Then you measure the kids so that you can place each kid on the map in just the spot where they know everything behind them, and in front of them is what they should learn next.
Then you assemble a vast library of learning objects and ask an algorithm to sort through it to find the optimal learning object for each kid at that particular moment.
Then you make each kid use the learning object.
Then you measure the kids again. If they have learned what you wanted them to learn, you move them to the next place on the map. If they didn't learn it, you try something simpler.
If the map, the assessments, and the library were used by millions of kids, then the algorithms would get smarter and smarter, and make better, more personalized choices about which things to put in front of which kids.
I spent a decade believing in this model—the map, the measure, and the library, all powered by big data algorithms.
Here's the problem: The map doesn't exist, the measurement is impossible, and we have, collectively, built only 5% of the library.
To be more precise: The map exists for early reading and the quantitative parts of K-8 mathematics, and much promising work on personalized learning has been done in these areas; but the map doesn't exist for reading comprehension, or writing, or for the more complex areas of mathematical reasoning, or for any area of science or social studies. We aren't sure whether you should learn about proteins then genes then traits—or traits, then genes, then proteins.
We also don't have the assessments to place kids with any precision on the map. The existing measures are not high enough resolution to detect the thing that a kid should learn tomorrow. Our current precision would be like Google Maps trying to steer you home tonight using a GPS system that knows only that your location correlates highly with either Maryland or Virginia.
We also don't have the library of learning objects for the kinds of difficulties that kids often encounter. Most of the available learning objects are in books that only work if you have read the previous page. And they aren't indexed in ways that algorithms understand.
Finally, as if it were not enough of a problem that this is a system whose parts don't exist, there's a more fundamental breakdown: Just because the algorithms want a kid to learn the next thing doesn't mean that a real kid actually wants to learn that thing.
So we need to move beyond this engineering model. Once we do, we find that many more compelling and more realistic frontiers of personalized learning opening up.
Which brings me to the question… "What did your best teachers and coaches do for you—without the benefit of maps, algorithms, or data—to personalize your learning?"
There are many ways to answer to this question. Each might be a doorway to the future of personalized learning.”
I believe Bergman is 95% right, but I disagree with his implied conclusion that the solution to personalized learning does not involve technology.
If I revise his question just slightly to “"What did all of your teachers do for you—without the benefit of maps, algorithms, or data—to personalize your learning?" my answer is: most of them did very little or nothing.
If that seems harsh, let me be clear that decades ago when I was in a fairly large public high school, teachers didn’t have any easy options to personalize learning. That few teachers were personalizing learning in my school was a reflection of the process, system, and expectations.
Those processes, systems, and expectations still exist in many schools today. But contra Bergman, among the schools that are implementing innovative instructional approaches, many are using technology to help teachers personalize learning. These technologies, however, aren’t “robot tutors in the sky.”
These personalized learning practices that are showing results tend to fall into a few broad categories. One is referenced by Bergman when he says “the map exists for early reading and the quantitative parts of K-8 mathematics, and much promising work on personalized learning has been done in these areas.” This seems accurate based on our knowledge of the online instructional landscape. There is a steep drop in the availability of products, and the track record of schools using them, as one moves from math to ELA to everything else. It’s an open question as to how much of the personalized learning approach used in middle school math, for example, will extend to other grade levels and subject areas. But examples of strong implementations and promising outcomes exist, and most of these are supported by technology.
A second category of personalized learning is both more advanced and less recognized. In this category are the innovative high schools which are able to use a mix of courses that are online, blended, and dual enrollment, freeing time that allows teachers to be a combination of subject matter expert, college counselor, life coach, and myriad other roles that so many students respond to. Oasis High School, run by the Santa Cruz County Office of Education, and Innovations Early College High School in Salt Lake City, are good examples. I suspect that part of the reason these schools are overlooked as personalized learning exemplars is that they rely so heavily on excellent teachers and a wide mix of digital tools and resources that their stories are complicated and not easily explained.
We’re in a strange time relative to personalized learning, in that two apparently contradictory conditions co-exist. First, undoubtedly the hype exceeds the reality, particularly from advocates outside of the public education system. But at the same time, excellent examples exist of schools using innovative instructional strategies using technology, in ways that are improving student outcomes. We don’t need to look to the past to consider the future of school innovation and improvement, as Bergman suggests—the examples exist around us now.
Disclosure: Amplify is a past Evergreen client.