What works in spelling instruction? New study on how to teach it effectively and the pre-testing effect:
- Copying spelling words might be one of the least effective things we ask pupils to do.
- Generating answers before learning can improve spelling, even when pupils are wrong.
- The benefits of testing grow over time, not immediately.
- What matters is not how many times pupils see a word, but how often they retrieve it.
One of the rapidly developing areas of research I've been watching closely is "pretrieval" practice and what happens when you test students on material before they learn it.
My theory on pre-testing has been that there is some kind of priming effect by quizzing students on to-be-learned material.
The pretesting effect now has a well‑controlled demonstration for spelling, in both Chinese and English.
What matters is not how many times pupils see a word, but how often they retrieve it.
Three groups saw the same words, but only the ones who had to think about them learned them best.
A few things though; the "errorful generation" framing is partly misleading in this dataset: the conditional analysis shows that pretesting items initially guessed correctly were retained at ceiling (M ≈ 0.92–0.95), whereas items guessed incorrectly recovered only modestly (M ≈ 0.59–0.65). So it seems we could say that pretesting works in school spelling not only by inducing error but also by surfacing what pupils already half‑know.
Secondly, the much smaller English effects (d ≈ 0.26–0.35 versus 0.61–0.74) sit alongside much lower absolute retention (~40% versus ~80%). The L2 condition may simply be operating closer to a difficulty floor, which has implications for how foreign‑language teachers should think about scheduling testing relative to instruction.
So in English L2 spelling for novice learners, it does not matter whether you test before or after instruction, it only matters that you test.
Lastly, more strokes per character predicted better retention. The authors flag this as counterintuitive and tentatively read it as a desirable‑difficulty effect at the item level; equally plausible is that high‑stroke characters are more visually distinctive and therefore more memorable.
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Three different kinds of AI user are emerging right now which is presenting a novel crisis for education. We could soon see rapid cognitive disparities of a kind we have no historical precedent for. 🧵⤵️
My sense is that the emergence of frontier AI has created 3 types of users right now which is effectively running a real-time social experiment in cognition:
1.) Group 1 (bootstrappers) - those who are using it to bootstrap their thinking and to learn as much as they can
2.) Group 2 (offloaders) - those who use it to avoid all thinking and yet are producing high quality outputs but learn absolutely nothing
3.) Group 3 is (business as usual) - those who more or less ignore it, or use it like a slightly better Google.
In the short run the 2nd group outperforms the otehrs on almost every measure school can see, BUT they do not experience themselves as having lost anything because the thing they lost is precisely the thing that would have noticed the loss (hard-earned, systematised knowledge).
In other words, not only do they not know what they dont know, but everyone else doesn't know what they dont know.
Are natural learning environments really the best way learn? Thread on Herbert Simon and why effective instructional design needs to be artificial. 🧵
In 'Sciences of the Artificial', Herbert Simon described an ant's complex, winding path across a beach.
The complexity isn't in the ant; it's in the environment (pebbles, dunes). Simon argued humans are the same: our complex behavior largely reflects the complexity of the environment we are navigating.
Simon’s claim is brutal and clarifying: Human behaviour looks complex largely because environments are complex. Change the environment and behaviour changes automatically. Leave it untouched and no amount of exhortation will help.
Natural learning is brutal. Evolution's method: those who fail to learn, perish. The knowledge we transmit in a single sentence ("don't eat that berry") took generations of fatal errors to acquire. Schools exist precisely because natural learning is slow, cruel, and inefficient.
The artificial classroom isn’t a falling away from a natural ideal; it’s an improvement on natural indifference.
Working on instructional invariants today and the idea that evaluability is far more important than feedback. In fact, feedback is not an invariant at all.🧵
An instructional invariant is a non-negotiable design condition that must hold for learning to occur reliably.
If violated, it causes learning to fail. Even if everything else appears to be working.
Instructional invariants are constraints on learning environments that prevent predictable failure modes.
They are not a theory of learning. They are a diagnostic tool for design.
Feedback is something the system does. Evaluability is something the learner does. There's a massive difference between the two.
Reading 'A Pattern Language' by Christopher Alexander and it’s just blowing my mind. His ideas have so much to offer instructional design 🧵
A “pattern” isn’t a recipe. It’s a constraint that, if violated, makes the design fail no matter how pretty the surface is. A room can be any style, but violate “Light on Two Sides” and it will feel gloomy anyway.
This is a big lesson for instructional design: you can have the slickest UX, the funniest characters, the best “personalisation" etc but if you violate learning constraints, the product won’t teach. It will just entertain.
Not everything is worth retrieving. Retrieval practice is powerful, but only when it targets the right knowledge. 🧵
As a general rule, knowledge that's central to the discipline should be retrieved.
Threshold concepts:
- Opportunity cost in economics
- Evolution by natural selection in biology
- The concept of a limit in calculus
- Irony in literature.
Hinge points:
A moment in instruction where everything that follows depends on students having grasped what came before. It's the juncture where the lesson either consolidates or collapses. If students haven't understood the concept at this point, proceeding is futile.
1. Retrieve knowledge that future learning depends on.
Example: In maths, fluent retrieval of place value and number bonds underpins everything from fractions to algebra. If students cannot retrieve these instantly, problem solving is a struggle.
Vygotsky's 'Zone of Proximal Development' is perhaps the most misunderstood idea in education. It was never a teaching method but a metaphor for how teaching can pull thinking upward, from the everyday to the scientific. ⬇️ 🧵
There are, broadly speaking, two Vygotskys.
The Anglo-American Vygotsky is social, collaborative, constructivist. Born in Mind in Society (1978), he became the patron saint of progressive education and appears alongside Bruner, Piaget, Rogoff, and Wertsch in teacher education courses.
His classroom privileges dialogue, peer tutoring, and scaffolding. He advocates discovery learning, group work, and authentic tasks. The teacher steps back.