A Neuroevolutionary Method for Knowledge Space Construction

Milan Segedinac1, Nemanja Milićević2, Milan Čeliković1 and Goran Savić1

  1. Faculty of Technical Sciences
    Trg D. Obradovića 6, 21000 Novi Sad, Serbia
    {milansegedinac, milancel, savicg}@uns.ac.rs
  2. SmartCat
    Danila Kiša 3V/14,21000 Novi Sad, Serbia
    nemanja.milicevic@smartcat.io

Abstract

In this paper we propose a novel method for the construction of knowledge spaces based on neuroevolution. The main advantage of the proposed approach is that it is more suitable for constructing large knowledge spaces than other traditional data-driven methods. The core idea of the method is that if knowledge states are considered as neurons in a neural network, the optimal topology of such a neural network is also the optimal knowledge space. To apply the neuroevolutionary method, a set of analogies between knowledge spaces and neural networks was established and described in this paper. This approach is evaluated in comparison with the minimized and corrected inductive item tree analysis, de facto standard algorithm for the data-driven knowledge space construction, and the comparison confirms the assumptions.

Key words

Genetic algorithms, Knowledge Space Theory, Neural networks, Educational technology

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS210820004S

Publication information

Volume 19, Issue 2 (June 2022)
Year of Publication: 2022
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Segedinac, M., Milićević, N., Čeliković, M., Savić, G.: A Neuroevolutionary Method for Knowledge Space Construction. Computer Science and Information Systems, Vol. 19, No. 2, 763–781. (2022), https://doi.org/10.2298/CSIS210820004S