Knowledge Spaces
A Bayesian Student Model without Hidden Nodes and its Comparison with Item Response Theory
International Journal of Artificial Intelligence in Education
Item to skills mapping: deriving a conjunctive q-matrix from data
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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Matrix factorization is a general technique that can extract latent factors from data. Recent studies applied matrix factorization to the problem of establishing which skills are required by question items, and for assessing student skills mastery from student performance data. A number of generic algorithms, such as Non-negative Matrix Factorization and Tensor factorization, are used in these studies to perform the factorization, but few have looked at optimizing these algorithms to the specific characteristics of student performance data. In this thesis, we explore how one such characteristic can lead to better factorization: the fact that items are learnt in a constrained order and allow such inferences as if a difficult item is succeeded, an easier one should also be succeeded. In particular, we want to address this question: can a partial order knowledge structure (POKS) be used to guide matrix factorization algorithms and lead to faster or better solutions to latent skills modelling?