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A Confession and a Question on Personalized Learning

Wir hatten ja diese und letzte Woche unsere ersten Webinare im Rahmen unseres Moduls „KI-basierte und adaptive Lernumgebungen„. In diesem Zusammenhang finde ich die folgende Notiz von Larry Berger interessant. Larry Berger ist CEO von Amplify,  einem Anbieter von personalisierten bzw. gamifizierten E-Learning-Inhalten (u.a. Comic-Abenteuer für das Lesetraining) sowie Assessment-Services hat sehr offen und skeptisch formuliert, wo er die Entwicklung von adaptiven Lernumgebungen sieht.

Noch eine Orientierung zum Kontext: Larry Berger war von Rick Hess zu einer Tagung eingeladen worden, konnte aufgrund eines Terminkonflikts aber nicht teilnehmen und hat dann diese Nachricht an die Teilnehmenden der Tagung übermittelt:

I can’t believe I’ve sat through years of dreary Rick Hess policy discussions waiting for the day when you would talk about personalized learning. And now you’re finally talking about it and I need to be at the Amplify holiday party.

Why do we have a holiday party tonight? Because selling personalized learning is not yet a good enough business for Amplify to afford a proper December holiday party.

I have notebooks and slide decks full of things I might say about personalized learning, but Joanne was unwilling to read those notebooks aloud. So I will limit myself to one confession and one question.

First, the confession:

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 that I hope might kick off your conversation: „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.



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