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Isn't the coefficient on 'T', the predicted 'mastery' variable, pointing the wrong way in the final logistic regression? And 'E', which is just getting 85% of
by a1k0n 15y ago
Isn't the coefficient on 'T', the predicted 'mastery' variable, pointing the wrong way in the final logistic regression? And 'E', which is just getting 85% of the exercises right, is completely dominating all the other stuff (exponential moving average, etc) they did in the model.
So while all this is really cool, and I like the idea of doing EM on missing data (I've done it myself), it doesn't seem like it actually adds much, if I'm reading that right.
- kohlmeier 15y agoThe 'E' in the regression is the inferred/predicted value of the E variable for that exercise, using no problem history from that exercise--only what's pulled in through the Bayes net. (Sorry that wasn't clear) The 'T' variable is likely just a case of multicollinearity with the 'E' variable and should go away on a full-scale data set. If not it can easily be removed from the model. The 'E' variable is dominating because is additionally captures cross-sectional affects across the various exercises in the regression.
- a1k0n 15y agoAh, ok. So is this a sort of Markov model, where you are predicting the probability of getting an exercise right after observing (some subset of) the previous exercises? And E is not 1 or 0, but the expected probability of getting it right? I'm still confused where all the different E_i's fit in. That would explain the magnitude, and I agree the negative weight on T would just be due to the direct correlation between E and T. Edit: I just realized that an exercise consists of multiple problems, so you're predicting whether or not the student will get >= 85% of the problems right on an exercise.