CLASSIFICATION OF THE EDUCATIONAL TEXTS STYLES WITH THE METHODS OF ARTIFICIAL INTELLIGENCE

Tereza Horáková; Milan Houška; Ludmila Dömeová

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CLASSIFICATION OF THE EDUCATIONAL TEXTS STYLES WITH THE METHODS OF ARTIFICIAL INTELLIGENCE

Title	CLASSIFICATION OF THE EDUCATIONAL TEXTS STYLES WITH THE METHODS OF ARTIFICIAL INTELLIGENCE
Publication Type	Journal Article
Year of Publication	2017
Authors	Horáková, T, Houška, M, Dömeová, L
Journal	Journal of Baltic Science Education
Volume	16
Issue	3
Start Page	324-336
Pagination	Continuous
Date Published	June/2017
Type of Article	Original article
ISSN	1648-3898
ISBN Number	E-ISSN 2538-7138
Keywords	Artificial Intelligence, classification and regression trees, educational texts, knowledge representation, knowledge unit, production rules, stylometric analysis
Abstract	Modern educational methods emphasize the necessity to transfer knowledge instead of data or information within the educational process. Thus it is important to the educational texts supporting the educational process contain knowledge in a particular textual representation. But it is not trivial to decide whether the particular piece of text contain knowledge or not. The solution is to measure the similarity between the particular text structure and a typical structure of a knowledge-designed text. This research aims at analysing the classification ability of three commonly-used classification techniques: artificial neural networks (ANNs), classification and regression trees (CARTs) and decision trees (bigMLs) to separate texts or text fragments into two groups. The texts in the first group contain mainly data and information (common texts), the texts in the other group contain knowledge in one of the particular knowledge representations (knowledge texts). The sample of 120 text fragments was used for the analysis. The results show that the ANN techniques are significantly more able to make the right classification of the text than the CART or bigML ones, and evidence good classification abilities. Thus the ANN approach could broaden the set of methods used for evaluation of difficulty of educational texts or textbooks.
URL	http://oaji.net/articles/2017/987-1497963889.pdf
DOI	10.33225/jbse/17.16.324
Refereed Designation	Refereed
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