Personalised Learning

By David Starshaw – Mathematics Teacher – High School

Amplify is a personalised learning company based in the US. Its CEO, Larry Berger, is a seminal thinker in this field.1 He wrote a reflection in February 2018 on personalised learning, given the experiences educators have had in personalised learning to date. The following is an excerpt from that reflection (abridged):

“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.

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 [primary] mathematics; but the map doesn’t exist for reading comprehension or writing or for any area of science or social studies.

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.

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 open up.”

I can hardly add more to what Berger has already said. There are great advantages to personalised learning, but it’s not a silver bullet. In Kawakawa and Tawari maths, we are still exploring what makes effective teaching and learning of maths in a Montessori context. My reflection on our journey so far is that the relationship between student and teacher seems to be the most important factor; something that personalised learning can’t yet match.