People seem to have a lot of concerns about personalized learning, and not enough opportunities to voice them. I say that because, every time I think we've exhausted this thread, someone else chimes in with something new. Today, I share a note from Steve Peha on the value of recognizing the limits of technology and digital curriculum mapping. Steve founded Teaching That Makes Sense, a consulting firm that specializes in literacy and technology; is author of Be a Better Writer; and has written about ed tech in places like the Washington Post and Psychology Today. Here's what he had to say:
You recently wrote, "Every time I run one of these notes on personalized learning, I figure that we're done." While you may never be done, I think the notes have sparked an important conversation.
I was struck by the insight from Amplify CEO Larry Berger regarding the "engineering" model of personalized learning: "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."
Larry's right, the map doesn't exist. Why? Because digital representations of curriculum paths and skill mappings within traditional school subjects can't account for the varied ways kids learn.
Take reading, for example. Many K-2 readers just get by with decoding. En route to 3 grade, where fluent chapter-book reading is expected, they stumble. They're not stumbling on fluent chapter-book reading per se; they're stumbling on an idiosyncratic mix of minimally learned precursor skills. Sometimes, middle schoolers suddenly, it seems, can't read. But what they actually can't do is handle the amount and type of reading required—something a digital curriculum map can't easily account for.
Kids get stuck often. Not because they're in the wrong place on a map, but because the map has no place for them to be. We've become so focused on adaptive curriculum delivery that we've paid little attention to the curriculum kids need and how they learn it.
After decades of magical thinking, technology's promise in education remains dramatically unfulfilled. This calls for a dramatic shift in our thinking from what we want technology to do to what it actually does.
Of course, technology does many things. But four functions matter most:
Whether we're talking about washing machines, weed whackers, or word processors, all we can expect from technology is the opportunity to leverage these four functions. The same is true with personalized learning. The question isn't, "How can a system teach kids to read?" It's, "What can a system enable, automate, accelerate, and amplify that helps kids learn to read?"
This is where technology is limited in its ability to personalize learning. Students often grasp the most challenging things in "Aha!" moments not easily connected to mastery of prior material because they often occur after extended periods of failure. "Aha!" moments can't easily be enabled, automated, accelerated, or amplified by technology.
Recall our struggling readers. Phrasing is one technique that helps kids make great progress. However, even though our language uses phrase-structure grammar, we don't teach phrasing explicitly. This compromises expression, the most reliable indicator of during-reading comprehension—the point at which instruction is most valuable.
Technology isn't good at detecting phrasing issues or helping kids master this skill across texts. Yet teaching phrasing is almost the simplest thing a human being can do. But it can't be mapped because it's needed all the time to different degrees that correspond to text type and text level, so it's never explicitly taught in automated systems. I've heard from many kids learning to read that, when they learn to phrase, a kind of switch flips for them. This is one of those "Aha!" moments that all kids—especially young readers—benefit from.
But making sure kids phrase effectively involves more than digitizing curriculum we think they need, developing activities we think they might learn from, and constructing maps we think represent learning progressions.
We put a lot of thinking into personalized learning, but we don't bring to it a correct understanding of what technology does and doesn't do relative to what kids need to learn and how they learn best.
This means focusing technology on aspects of the teaching-learning process that are easily enabled, automated, accelerated, and amplified. It also means we need to stop over-selling personalized learning. Well-trained, well-resourced, and well-paid human beings do a pretty good job of facilitating both personalization and learning. Politically, we've abandoned the idea of training, rewarding, and resourcing teachers effectively, but that doesn't mean it isn't the right thing to do.
Solving the riddle of personalized learning therefore requires a thorough rethinking of technology-assisted education. But a thorough rethinking is what we must undertake, if for no reason other than that it might spare you, Rick, from having to write more of these notes on personalized learning.