CLASSIFICATION OF THE EDUCATIONAL TEXTS STYLES WITH THE METHODS OF ARTIFICIAL INTELLIGENCE
Open accessAbstract
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. Keywords: artificial intelligence, classification and regression trees, educational texts, knowledge representation, knowledge unit, production rules, stylometric analysis.
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Classification demarcates knowledge content, which contributes to the accuracy of text analysis.
“Classification demarcates knowledge content, which contributes to the accuracy of text analysis [3].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [3]
The Conclusions say the classifiers distinguish knowledge-style from common-style educational texts and that the findings are useful for knowledge-content analysis. The paper measures classification correctness, but it does not show that classification improves the accuracy of text analysis; that further claim is unsupported. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPrevious research has found three basic models of AI in knowledge processing: knowledge representation, knowledge obtaining, and knowledge derivation.
“previous research has found three basic models of AI in knowledge processing: knowledge representation, knowledge obtaining, and knowledge derivation [3].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [3]
Horakova et al. found that artificial neural networks were significantly more effective than regression trees and decision trees to separate educational texts or text fragments.
“e results show that artificial neural networks (ANNs) were significantly more effective than regression trees and decision trees to separate educational texts or text fragments.”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [3]
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- Authors
- Horáková, T., Houška, M., & Dömeová, L.
- Affiliations
- Czech University of Life Sciences Prague (CZ)
- Venue
- Journal of Baltic Science Education
- Publisher
- Scientia Socialis Ltd
- Published
- 2017-06-25
- Volume
- 16
- Issue
- 3
- Pages
- 324-336
- ISSN
- 1648-3898 (ISSN-L)
- 2538-7138
- OpenAlex
- W2746677022
- Cited by
- 33
- References
- 44
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 8 · 2023: 8 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 1
- FWCI
- 0.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Journal of Baltic Science Education (publishedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text Readability and Simplification
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- educational texts, knowledge representation, artificial neural networks, classification and regression trees, decision tree, stylometry, production rules, knowledge units
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