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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Notes
- 1.
The actual values of W and H have been normalized to allow the heatmap representation.
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Acknowledgements
This work has been supported in part by the GNCS (Gruppo Nazionale per il Calcolo Scientifico) of Istituto Nazionale di Alta Matematica Francesco Severi, P.le Aldo Moro, Roma, Italy.
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Casalino, G., Castiello, C., Del Buono, N., Esposito, F., Mencar, C. (2017). Q-matrix Extraction from Real Response Data Using Nonnegative Matrix Factorizations. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_15
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