The science of learning
Bloom's two-sigma problem
In 1984 Benjamin Bloom put a number on something good teachers had always sensed — give a learner a decent tutor and their results don't just nudge upward, they transform.
Bloom ran a straightforward experiment. Take the same lesson and teach it three ways: a normal class of thirty, a class using mastery learning, and one-to-one tutoring. Then measure what people could actually do afterwards.
The tutored group didn’t edge ahead. They moved about two standard deviations — two sigma — beyond the ordinary class. The average tutored student ended up ahead of 98% of the students taught the usual way. Mastery learning, where nobody moves on until they’ve got it, landed about one sigma up: still large, still mostly ignored.
Why it has haunted the field ever since
A two-sigma effect is one of the biggest in all of education. If a drug did that, we would reorganise medicine around it. And we have known about it for forty years.
The reason it changed so little is money, not doubt. You cannot give every learner a personal tutor. So the result sat there — real, repeatable, and useless at scale.
The result was never in question. The delivery was.
What a tutor actually does
It helps to be precise about why tutoring works, because that is the part we can try to copy. A good tutor notices exactly where you are, pitches the next step just beyond it, asks the question that makes you think rather than nod, and comes back at the right moment to check it stuck. Very little of that is about delivering information. It is about responding to one specific person.
That is the thread running through everything else on this site — aiming higher up Bloom’s taxonomy, spacing out retrieval, making the learner do the work. They are all attempts to get some of the tutor’s effect without the tutor’s cost.
Whether technology can finally close that gap is the open question I spend most of my time on. For now, the two-sigma problem is the clearest target we have: we already know what great looks like. We just can’t yet afford to give it to everyone.