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

被引：9

作者：

Casalino, Gabriella ^{[1
]}

Castiello, Ciro ^{[1
]}

Del Buono, Nicoletta ^{[2
]}

Esposito, Flavia ^{[2
]}

Mencar, Corrado ^{[1
]}

机构：

[1] Univ Bari Aldo Moro, Dept Informat, I-70125 Bari, Italy

[2] Univ Bari Aldo Moro, Dept Math, I-70125 Bari, Italy

来源：

COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2017, PT I | 2017年 / 10404卷

关键词：

Nonnegative Matrix Factorization; Educational Data Mining; Q-matrix; Skill interpretation; CONSTRAINED LEAST-SQUARES; DISCOVERY;

D O I：

10.1007/978-3-319-62392-4_15

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：203 / 216

页数：14

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