Science for Education Today, 2020, vol. 10, no. 4, pp. 139–155
© Urban M. A., Smoleusova T. V., 2020
UDC: 
371

Characteristics and sub-skills of mathematical modeling in the mathematics curriculum for primary schools

Urban M. A. 1 (Minsk, Republic of Belarus), Smoleusova T. V. 2 (Novosibirsk, Russian Federation)
1 Belarusian State Pedagogical University
2 Novosibirsk Institute for Advanced Studies and Retraining of Educators
Full text (Open Access) — Downloaded 1187
Abstract: 

Introduction. The article addresses the issue of targeted use of modeling in education. The purpose of the research is to clarify the characteristics and components (elementary sub-skills) of a complex skill of modeling in the contemporary mathematics primary education.
Materials and Methods. The study used the following research methods: theoretical analysis of contemporary international and Russian scholarly literature on using modeling in educational process; educational action research in order to test the approach to assessment of modeling skills; methods of mathematical statistics for processing the experimental data.
Results. The article presents the authors’ approach to component assessment of the complex skill of modeling (with the main focus on teaching mathematics in the primary school). Moreover, it reviews contemporary psychological and educational literature on the problem of targeted use of educational modeling, which is considered both as a means of learning and its outcome. The authors proposed a definition of modeling and identified and described its elementary sub-skills. The authors developed, described and experimentally verified the approach to component evaluation of the complex skill of modeling.
Conclusions. The approach to evaluation of the complex modeling skill allows to assess the level of its development in primary schoolchildren.

Keywords: 

Modeling in education; Visual modeling; Complex modeling skills; Evaluation of modeling skills; Teaching mathematics in the primary school.

URL WoS/RSCI: https://www.webofscience.com/wos/rsci/full-record/RSCI:43933909

Prominence Percentile SciVal: 99.370 Pedagogical Support | Educational Process | Professional Competence

http://apps.webofknowledge.com/full_record.do?product=RSCI&search_mode=G...

https://www.scopus.com/record/display.uri?eid=2-s2.0-85092323893&origin=...

Characteristics and sub-skills of mathematical modeling in the mathematics curriculum for primary schools

For citation:
Urban M. A., Smoleusova T. V. Characteristics and sub-skills of mathematical modeling in the mathematics curriculum for primary schools. Science for Education Today, 2020, vol. 10, no. 4, pp. 139–155. DOI: http://dx.doi.org/10.15293/2658-6762.2004.09
References: 
  1. Chou T.-L., Wu J.-J., Tsai C.-C. Research trends and features of critical thinking studies in e-learning environments. Journal of Educational Computing Research, 2019, vol. 57 (4), pp. 1038–1077. DOI: https://doi.org/10.1177/0735633118774350
  2. Pushkarev Y. V., Pushkareva E. A. Communication foundation for intellectual culture: Tendencies of contemporary development. XLinguae, 2019, vol. 12 (4), pp. 212–218. DOI: https://doi.org/10.18355/XL.2019.12.04.18
  3. Ivlev V. Y., Ivleva M. L., Inozemtsev V. A. Cognitive revolution as a factor in the formation of a new epistemological paradigm and methodology of the study of knowledge in modern science. Izvestiya Moskovskogo Gosudarstvennogo Tehnicheskogo Universiteta MAMI, 2013, vol. 6 (1), pp. 91–99. (In Russian) URL: https://elibrary.ru/item.asp?id=20799316
  4. Trofimov V. M. What is the exact knowledge and how it is prodused in the cognitive processes. Novosibirsk State Pedagogical University Bulletin, 2018, vol. 8 (4), pp. 141–157. (In Russian) DOI: http://dx.doi.org/10.15293/2226-3365.1804.09 URL: https://elibrary.ru/item.asp?id=35605575
  5. Pushkarev Y. V., Pushkareva E. A. The phenomenon of social information in education: Modern research practices (a critical review). Science for Education Today, 2019, vol. 9 (6), pp. 52–71. (In Russian) DOI: http://dx.doi.org/10.15293/2658-6762.1906.04 URL: https://www.elibrary.ru/item.asp?id=41586633
  6. Pushkarev Yu. V., Pushkareva E. A. Philosophical interpretation of knowledge and information: Knowledge value and information diversity in modern communication. XLinguae, 2018, vol.  11  (3), pp. 176–184. DOI: https://doi.org/10.18355/XL.2018.11.03.17
  7. Chou T.-L., Wu J.-J., Tsai C.-C. Research trends and features of critical thinking studies in e-learning environments. Journal of Educational Computing Research, 2019, vol. 57 (4), pp. 1038–1077. DOI: https://doi.org/10.1177/0735633118774350
  8. Hamada M., Hassan M. An interactive learning environment for information and communication theory. Eurasia Journal of Mathematics Science and Technology Education, 2017, vol. 13 (1), pp.  35–59. DOI: http://doi.org/10.12973/eurasia.2017.00603a
  9. Nancy W., Parimala A., Merlin L. M. Livingston advanced teaching pedagogy as innovative approach in modern education system. Procedia Computer Science, 2020, vol. 172, pp. 382–388. DOI: https://doi.org/10.1016/j.procs.2020.05.059

10. Chekour M., Laafou M., Janati-Idrissi R. What are the adequate pedagogical approaches for teaching scientific disciplines? Physics as a case study. Journal of Educational and Social Research, 2018, vol. 8 (2), pp. 141–148. DOI: https://doi.org/10.2478/jesr-2018-0025 

11. Tsukerman G. A., Klesch N. A. Understanding conceptual texts and mastering concepts. Psychological Science And Education, 2017, vol. 22 (3), pp. 19–27. (In Russian) DOI: http://dx.doi.org/10.17759/pse.2017220302 URL: https://www.elibrary.ru/item.asp?id=29898528

12. Smoleusova T. V. Visual reference guide “Visual tables in mathematics” for the formation of universal educational actions. Herzen Readings. Primary Education, 2016, vol. 7 (1), pp. 118–121. (In Russian) URL: https://www.elibrary.ru/item.asp?id=25606060

13. Urban M., Murauyova H., Gadzaova S. Didactic principles of visualization of mathematical concepts in primary education. Pedagogika, 2017, vol. 127 (3), pp. 70–86. DOI: http://dx.doi.org/10.15823/p.2017.40 URL: https://www.elibrary.ru/item.asp?id=35703141

14. Smoleusova T. V. Variability of modeling in education as a condition for implementing the requirements of the federal state budget. Sibirskiy Uchitel, 2015, no. 6, pp. 55–57. (In Russian) URL: https://www.elibrary.ru/item.asp?id=25625645

15. DeBellis V. A., Goldin G. A. Affect and meta-affect in mathematical problem solving: A representational perspective. Educational Studies in Mathematics, 2006, vol. 63, pp. 131–147. DOI: https://doi.org/10.1007/s10649-006-9026-4

16. Rinaldi L. J., Smees R., Alvarez J., Simner J. Do the colors of educational number tools improve children’s mathematics and numerosity?  Child Development, 2020, vol. 91 (4), pp. e799-e813. DOI: http://dx.doi.org/10.1111/cdev.13314

17. Wieman C., Perkins K. Transforming physics education. Physics Today, 2005, vol. 58 (11), pp. 36–41. DOI: http://dx.doi.org/10.1063/1.2155756

18. Bernacki M. L., Greene J. A., Crompton H. Mobile technology, learning, and achievement: Advances in understanding and measuring the role of mobile technology in education. Contemporary Educational Psychology, 2020, vol. 60, pp. 101827. DOI: https://doi.org/10.1016/j.cedpsych.2019.101827

19. Walkington C., Bernacki M. L. Appraising research on personalized learning: Definitions, theoretical alignment, advancements, and future directions. Journal of Research on Technology in Education, 2020, vol. 52. DOI: https://doi.org/10.1080/15391523.2020.1747757

20. De Lange J. Mathematical literacy for living from OECD-PISA perspective. Tsukuba Journal of Educational Study in Mathematics, 2006, vol. 25, pp. 13–37. URL: http://www.human.tsukuba.ac.jp/~mathedu/2503.pdf

21. Doğan M., Gürbüz R., Çavuş Erdem Z., Şahi̇n S. Using mathematical modeling for integrating STEM disciplines: A theoretical framework. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2019, vol. 10 (3), pp. 628–653. DOI: https://doi.org/10.16949/turkbilmat.502007 .

22. Kamenkova N. G., Nekrasova S. A. Formation of modeling skills in the process of computing activity of primary school children. Herzen Readings. Primary Education, 2020, vol. 11 (1), pp. 109–119. (In Russian) URL: https://www.elibrary.ru/item.asp?id=42553906

23. Algharaibeh S. A. S. Metacognitive skills as predictors of cognitive failure. American Journal of Applied Psychology, 2017, vol. 6 (3), pp. 31–37. DOI: https://doi.org/10.11648/j.ajap.20170603.11

24. Šimleša M., Guegan J., Blanchard E., Tarpin-Bernard F., Buisine S. The flow engine framework: A cognitive model of optimal human experience. Europe's Journal of Psychology, 2018, vol. 14 (1), pp. 232–253. DOI: https://doi.org/10.5964/ejop.v14i1.1370  

Date of the publication 31.08.2020