Dealbreaker - Lan et al

Machine Learning based education problem is student-response modeling i.e., developing principled statistical models that (i) accurately predict unobserved student responses to questions and (ii) identify the latent concepts that govern correct or incorrect responses. The Rasch Model: simple yet effective for analyzing student-response data. This model characterizes the probability of a correct response as a function of two scalar parameters: the student's ability and the question's difficulty. Affine functions - characterize a student's probability of success on a question as an affine function of the student's knowledge on underlying concepts. Affine models allow weak knowledge of a concept to be erroneously covered up by strong knowledge of other potentially unrelated concepts. Affine models also fail to capture more complicated nonlinear dynamics underlying student responses.