Bayesian Knowledge Tracing, explained without the maths
Why a forty-year-old algorithm is still the right way to track what you actually know, and how we use it to retire concepts at 0.90 mastery probability instead of 'you saw it three times'.
If you have used a flashcard app, you know the feeling: you have seen a card thirty times, you can read it back without thinking, and the moment someone phrases it differently you blank. Repetition is not the same as understanding, and the easiest way to fool a study system is to confuse the two. Bayesian Knowledge Tracing was designed to keep them apart.
BKT keeps a single number for each concept you are studying: the probability that you have actually mastered it. Every answer you give is evidence. A correct answer pushes the probability up; an incorrect one pulls it down. Crucially, the model accounts for guessing and slipping, so a lucky correct answer does not retire a concept, and an unlucky wrong one does not bury you. The maths is unspectacular. The behaviour is not.
We retire a concept once its mastery probability clears 0.90 and bring it back into rotation only if a downstream question reveals a regression. The result is a study queue that shrinks as you actually learn, rather than one that just rolls forward in time. Spaced repetition still has a role, but it sits underneath the BKT layer, not on top of it.