Author(s): Gergő VIDA

Title: A TANULÁSI ZAVAR, MINT ELMOSÓDOTT RENDSZER – BAYES TÉTEL A DIAGNOSZTIKÁBAN

Source: K. Kéri, K. Józsa, K. Kanczné Nagy. A. Tóth-Bakos, D. Borbélyová, T. Mészáros (eds.): 14th International Conference of J. Selye University. Pedagogical Section. Conference Proceedings

ISBN: 978-80-8122-447-8

DOI: https://doi.org/10.36007/4478.2023.135

Publisher: J. Selye University, Komárno, Slovakia

PY, pages: 2023, 135-143

Published on-line: 2023

Language: hu

Abstract: Learning disorders do not have sharp, concrete boundaries. Our hypothesis is that from the sentences and patterns in the texts of the test requests, expert opinions and all textual sources that initiate the identification of learning disabilities, a mathematical model of learning disability identification can be constructed based on fuzzy logic (Zadeh, 1965). In our research, all applications and expert opinions of a randomly selected group of 15 children delegated for learning disability diagnosis in Komárom-Esztergom County in the 2018/19 school year were analysed based on fsQCA (Sántha, 2019). If we consider the texts and results generated when identifying a learning disability as the object domain of a Bayesian network, then we can even give the learning disability, joint probability distribution function. In this way, a causal and diagnostic model of learning disorder symptoms and variables can be generated.

Keywords: In this way, diagnostics based on sharp boundaries can be replaced, which can remove the need for categorisation.

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