An implicit assumption in psychometrics and educational statistics is that the generative model for student scores on test questions is governed by the topics of those questions and each student's aptitude in those topics. That is, a function to generate the matrix of scores for m students on n questions should rely on each student's ability in a set of t topics, and the relevance of each question to those topics. In this paper, we use educational data mining techniques to analyze score matrices from university-level computer science courses, and demonstrate that no such structure can be extracted from this data.
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