Geodesic
Blended learning technology in the study of descriptive characteristics of a statistical sample
Problemy fiziki, matematiki i tehniki, no. 3 (2023), pp. 88-94.

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A substantive description of blended learning is presented, the main elements of the blended learning model and methodology are noted: rotation, flipped class, individual plan, flexible model. The analysis of information and communication technologies and Internet resources with data sets for solving the problem of statistical data processing is performed. On an example of solving the problem of determining the descriptive characteristics of the statistical sample there is used programming language R with a set of libraries: WDI, xtable, openxlsx, psych, sm, ggplot2, tidyverse, dunn.test, rstatix, ggpubr. The implementation of parsing data from the World Bank's open data web resource (data.worldbank.org) is shown. The script in the programming language R for finding descriptive characteristics of the statistical sample is developed. The technology of using information and communication technology for the educational process in the study of statistical methods of data processing is proposed.
Keywords: blended learning, information and communication technology, parsing, R programming language, descriptive data sampling characteristics. 
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O. M. Demidenko; A. I. Yakimov; E. A. Yakimov; K. G. Tishchenko. Blended learning technology in the study of descriptive characteristics of a statistical sample. Problemy fiziki, matematiki i tehniki, no. 3 (2023), pp. 88-94. http://geodesic.mathdoc.fr/item/PFMT_2023_3_a15/

[1] V.V. Mendel, O.A. Trinadtsatko, “Aspekty ispolzovaniya informatsionno-kompyuternykh tekhnologii v obrazovatelnom protsesse”, Sovremennye problemy nauki i obrazovaniya, 2020, no. 2 (Data dostupa: 02.03.2023) https://science-education.ru/ru/article/view?id=29755

[2] A.S. Fomina, “Smeshannoe obuchenie v vuze: institutsionalnyi, organizatsionno-tekhnologicheskii i pedagogicheskii aspekty”, Teoriya i praktika obschestvennogo razvitiya, 2014, no. 21, 272–279

[3] I.A. Nagaeva, “Smeshannoe obuchenie v sovremennom obrazovatelnom protsesse: neobkhodimost i vozmozhnosti”, Otechestvennaya i zarubezhnaya pedagogika, 2016, no. 6, 56–67

[4] C. Hands, M. Limniou, How Does Student Access to a Virtual Learning Environment (VLE) Change During Periods of Disruption?, Journal of Higher Education. Theory and Practice, 23:2 (2023), 18–34

[5] A. Alammary, J. Shepard, A. Carbone, “Blended learning in higher education: Three different design approaches”, Australasian Journal of Educational Technology, 30:4 (2014), 440–454 | DOI

[6] J. Broadbent, “Comparing online and blended learner's self-regulated learning strategies and academic performance”, The Internet and Higher Education, 33 (2017), 24–32 | DOI

[7] W.N. Venables, D.M. Smith, R Core Team, An introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics, CRAN, 2020, 105 pp.

[8] Dzh.D. Long, Pol Titor, Kniga retseptov: proverennye retsepty dlya statistiki, analiza i vizualizatsii dannykh, per. s angl. D.A. Belikova, DMK Press, M., 2020, 510 pp.

[9] H.C. Thode, Testing For Normality, Taylor Francis Limited, New York, 2019, 368 pp.