While topic mastery is hidden, we can partially observe performance on the exercises in that topic. You can think of a topic as a collection of any N exercises. I didn’t find a great one, so I’m posting what I came up with in the hope of helping the next soul.įor our example problem, let’s say there is a hidden (unobservable) binary variable T for every Khan Academy user that represents whether they have mastered a given topic (1=mastered, 0= not mastered). When I started coding, though, I had several practical questions and really wanted a simple code example. I’m not going to duplicate them–I recommend this or that.

So I sat down to start applying these tools, and there were plenty of online resources to help with the book learnin’. What instructional interventions can an intelligent learning system make to best aid the user?.

Can we predict the best ordering of topics for a user to minimize time spent and maximize success?.

What are the underlying concepts that relate mastery of our hundreds of exercises to each other?.

Did I mention that your variables can have noisy, missing, or just plain unobservable data?Īt Khan Academy, we’ve got problems like that. Especially if you have the kind of problem that involves hundreds or thousands of interrelated variables, any one of which you might want to predict based on some subset of the others. But better than being cool, they’re useful. We computer geeks can love ‘em because we’re used to thinking of big problems modularly and using data structures. Bayesian networks (and probabilistic graphical models more generally) are cool.

Khan academy coding python