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.

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

CASALINO, GABRIELLA;CASTIELLO, CIRO;DEL BUONO, Nicoletta;ESPOSITO, FLAVIA;MENCAR, CORRADO
2017-01-01

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.
2017
978-331962391-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/196938
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