Glazov, Izhevsk, Russian Federation
UDC 372.853
UDC 37
The article is devoted to the problem of identifying the semantic proximity between key ideas and concepts in educational texts on physics of the microcosm, and identifying the features of their semantic space. The texts of university textbooks and Wikipedia articles collected in one file are analyzed. Quantitative linguistics methods are used, including calculating the frequency of use of terms and marker words related to the most significant ideas, as well as comparing the distribution of concepts in physics textbooks. Special computer programs written in ABCPascal are used to remove punctuation and stop words from the text and determine the number of occurrences of various terms. The cosine similarity measure and semantic distanc-es between concepts are also calculated. As a result: 1) the key physical concepts of-ten used in the study of microcosm physics are identified; 2) the most important ideas of microcosm physics are listed: the idea of physical characteristic quantization of microobjects, the idea of wave-particle dualism, the Heisenberg uncertainty principle; 3) the cloud of concepts and pie chart are constructed showing the proportion of each of them in the text; 4) the matrix of proximity of concepts and matrix of semantic distances between them are obtained; 5) graphs are constructed that model an essential part of the semantic space of educational texts on microcosm physics and show the closest connections between key concepts and ideas.
didactics, microcosm, concept, semantic space, computer methods, textbook, physics
1. Andrievskaya, N.K. Gibridnaya intellektual`naya mera ocenki semanti-cheskoj blizosti // Problemy` iskusstvennogo intellekta. – 2021. № 1.– pp. 4-17. EDN: https://elibrary.ru/ZDZKGK
2. Anisimov, A.V. Metod vy`chisleniya semanticheskoj blizosti-svyaznosti mezhdu slovami estestvennogo yazy`ka / A.V. Anisimov, A.A. Marchenko, V.K. Kisenko // Kibernetika i sistemny`j analiz. – 2011. № 4. – pp. 18-27.
3. Borodashhenko, A.Yu. Analiz tekstov na semanticheskoe sxodstvo na os-nove apparata teorii grafov // Izvestiya OrelGTU. Seriya: Informacionny`e sistemy` i texnologii. – 2008. № 1-2. – pp. 46-52.
4. Vanyushkin, A.S. Metody` i algoritmy` izvlecheniya klyuchevy`x slov / A.S. Vanyushkin, L.A. Grashhenko // Novy`e informacionny`e texnologii v avtomatizirovanny`x sistemax. – 2016. № 19. – pp. 85-93. EDN: https://elibrary.ru/VTZOBJ
5. Kryukov, K.V., Pankova, L.A., Pronina, V.A. Mery` semanticheskoj blizosti v ontologii // Problemy` upravleniya. – 2010. – № 5. – pp. 2-14. EDN: https://elibrary.ru/MUVNSP
6. Kuzneczov, S.I. Kurs fiziki s primerami resheniya zadach: uchebnoe posobie. Chast` III. Geometricheskaya i volnovaya optika. E`lementy` atomnoj i yadernoj fiziki. Osnovy` fiziki e`lementarny`x chasticz. – Tomsk: Izd-vo Tomskogo politexnicheskogo universiteta, 2015. – 302 p. EDN: https://elibrary.ru/TXPKGV
7. Mayer, R.V. Slozhnost` uchebny`x ponyatij i tekstov: monografiya. – Gla-zov: GIPU, 2024. – 132 p. EDN: https://elibrary.ru/XWDTOK
8. Monaxov, S.I. Analiz terminologii v sovremenny`x shkol`ny`x uchebnikax metodami komp`yuternoj lingvistiki // S.I. Monaxov, V.V. Turchanenko, E.A. Fedyukova, D.N. Cherdakov / Informatizaciya obrazovaniya i metodika e`lektronnogo obucheniya. – pp. 209 – 215.
9. Morozova, Yu.I. Postroenie semanticheskix vektorny`x prostranstv raz-lichny`x predmetny`x oblastej // Informatika i ee primeneniya. − 2013. T. 7. Vy`p. 1. − pp. 90-93. EDN: https://elibrary.ru/QCJCPZ
10. Trofimova, T.I. Kurs fiziki: Ucheb. posobie dlya vuzov. — M.: Vy`ssh. shk., 2001. — 542 p. EDN: https://elibrary.ru/SNPHVF
11. Sheremet`eva, S.O. Metody` i modeli avtomaticheskogo izvlecheniya klyuchevy`x slov / S.O. Sheremet`eva, P.G. Osminin // Vestnik YuUrGU. Seriya: Lingvistika. – 2015. T. 12. № 1. – pp. 76-81. EDN: https://elibrary.ru/TKORYP
12. Manning, C.D. An Introduction to Information Retrieval / C.D. Manning, P. Raghavan, H. Schütze. – Cambridge University Press. 2008. DOI: https://doi.org/10.1017/CBO9780511809071
13. Turney, P.D. From frequency to meaning: Vector space models of seman-tics / P.D. Turney, P. Pantel // J. Artificial Intelligence Research. Menlo Park, Cali-fornia: AAAI Press, 2010. No. 37. – pp. 141–188.
14. White, M.D. Content analysis: A flexible methodology / M.D. White, E.E. Marsh // Library trends. – 2006. Vol. 55. № 1. – pp. 22 – 45. DOI: https://doi.org/10.1353/lib.2006.0053
15. Vikipediya: https://ru.wikipedia.org
