A Calculation Scheme for Measuring the Efficiency of Knowledge Texts for Vocational Education

Rauchová, Tereza; Houška, Milan

doi:10.1016/j.sbspro.2013.12.003

Cited by 4 publications

(2 citation statements)

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“…Knowledge texts have already been tested in the education process as a tool for transferring the knowledge as compared to common educational texts (Rauchová & Houška, 2013a, Horáková & Houška, 2014a, Horáková et. al., 2014), their efficiency of creation has been measured (Rauchová & Houška, 2013b), and the quantitative profile of knowledge text and its difference from text that is common from the linguistic point of view has been determined statistically, too (Rauchová et al 2014). The research follows the results of Horáková & Houška (2014b) where, among others, the hypothesis of the difference of the number of characteristic concepts of knowledge texts as compared to common texts was statistically proven; knowledge text has shown significantly higher rate of these concepts (terms) which, as stated by Horáková & Houška (2014b), is determined by the structure of the knowledge unit (see the following chapter for further details).…”

Section: Data Information and Knowledgementioning

confidence: 99%

Classification of the Educational Texts Styles With the Methods of Artificial Intelligence

Horáková

Houška

Dömeová

2017

JBSE

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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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Section: Data Information and Knowledgementioning

confidence: 99%

Classification of the Educational Texts Styles With the Methods of Artificial Intelligence

Horáková

Houška

Dömeová

2017

JBSE

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“…The question is what their effectiveness and efficiency is, i.e. whether people who solve tasks according to instructions, directives and other documents are working with lower error rate and whether this advantage brings more savings than the costs of creating knowledge-structured texts as compared to the costs of classical works directives (Rauchová and Houška, 2013b). The system approach is also suitable for creating metric-based evaluation of the efficiency and effectiveness of knowledge transfer (Rosales et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Creating the Knowledge-structured Texts in Agriculture Companies: A Cost Modeling Approach

Horáková

Rydval

Houška

2017

AOL

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Agriculture is one of the most regulated sectors. Council regulations, national legal acts, subsidies, nature protection, market regulations and many other directives induce the necessity to work with a lot of text documents and to manage the knowledge in them. Thus, it is worth considering the creation of specifically designed internal documents to represent knowledge explicitly in so-called "knowledge-structured texts". However, it is a costly process to create the knowledge texts. The objective of this paper is to create a model that is capable of showing what time point the successive costs of the two types of text are equal at. Linking the methods of knowledge engineering and management is carried out with the help of an integrating element, i.e. general systems theory, through system dynamics. Despite an initial investment in the knowledge texts, the results show their potential for lowering the costs in the middle-and long-term horizons.

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