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2017 | OriginalPaper | Buchkapitel

Q-matrix Extraction from Real Response Data Using Nonnegative Matrix Factorizations

verfasst von : Gabriella Casalino, Ciro Castiello, Nicoletta Del Buono, Flavia Esposito, Corrado Mencar

Erschienen in: Computational Science and Its Applications – ICCSA 2017

Verlag: Springer International Publishing

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Abstract

In this paper we illustrate the use of Nonnegative Matrix Factorization (NMF) to analyze real data derived from an e-learning context. NMF is a matrix decomposition method which extracts latent information from data in such a way that it can be easily interpreted by humans. Particularly, the NMF of a score matrix can automatically generate the so called Q-matrix. In an e-learning scenario, the Q-matrix describes the abilities to be acquired by students to correctly answer evaluation exams. An example on real response data illustrates the effectiveness of this factorization method as a tool for EDM.

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The actual values of W and H have been normalized to allow the heatmap representation.

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Titel: Q-matrix Extraction from Real Response Data Using Nonnegative Matrix Factorizations
verfasst von: Gabriella Casalino
Ciro Castiello
Nicoletta Del Buono
Flavia Esposito
Corrado Mencar
Verlag: Springer International Publishing
Buch: Computational Science and Its Applications – ICCSA 2017
Print ISBN: 978-3-319-62391-7

Electronic ISBN: 978-3-319-62392-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-62392-4_15

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