UDC: 
378.14.015.62
Chudinsky Ruslan M.
Доктор педагогических наук, Dr. Sci. (Pedag.), Assoc. Prof., Head of the Department of Computer Science, Information Technology and Digital Education, Voronezh State Pedagogical University, chudinsky@mail.ru, Voronezh

The Influence of Contextual Data on the Results of the Input Assessment of the Subject Content for the Computer Science Course of Secondary General Education

Abstract: 
The article analyzes the problem of the influence of contextual data on the results of the input assessment of the subject content for the computer science course of secondary general education for 1st-year students of the Faculty of Physics and Mathematics of Voronezh State Pedagogical University in 2019–2021. The purpose of the article is to identify and analyze the influence of independent factors on the results of the input assessment of the subject content for the computer science course of secondary general education for 1st year students in 2019–2021. Methodology. The input assessment of the level of formation of the subject content of the computer science course on the basis of the Unified State Exam for the academic subject “Informatics” was carried out in the 1st semester of first-year students (september-october) in 2019–2021. A total of 174 1st year students took part in it. To conduct the study, the following contextual data were used: the biological sex of students; the location of the educational organization (general education or professional) that the 1st year students completed before entering the university; participation and results of the USE students in 2019–2021 in the academic subject “Computer Science”; preparation of students for the Unified State Exam in 2019-2021 for admission to university; features of the family in which students were brought up (full and incomplete, the presence of higher education from parents in the family, the presence of work from parents in the family). The study of the results of the input evaluation, comparison and analysis of the results obtained during the study was carried out using the Kruskal-Wallis H test, the Mann-Whitney U test, and correlation analysis. In conclusion, the conclusion is made about the different influence of contextual data on the results of the input assessment of the subject content for the computer science course of secondary general education for 1st-year students of the Faculty of Physics and Mathematics in 2019–2021.
Keywords: 
input assessment, subject content of the computer science course, 1st year students, contextual data
References: 

1. Demos, specifications, codifiers of the 2019 Unified State Exam on the academic subject “Informatics and ICT” [online]. Available at: http://www.fipi.ru/sites/default/files/document/1542988313/inf_ege_2019.zip (аccessed 21.02.2022). (In Russ.)
2. Demos, specifications, USE 2020 codifiers for the academic subject “Informatics and ICT” [online]. Available at: http://doc.fipi.ru/ege/demoversii-specifikacii-kodifikatory/2020/inf_ege... (аccessed 21.02.2022). (In Russ.)
3. Demos, specifications, codifiers of the Unified State Exam 2021 for the academic subject “Informatics and ICT” [online]. Available at: http://doc.fipi.ru/ege/demoversii-specifikacii-kodifikatory/2021/inf-ege... (аccessed 21.02.2022). (In Russ.)
4. Krylov, S. S., 2019. Methodological recommendations for teachers prepared on the basis of an analysis of typical mistakes of participants of the Unified State Exam 2019 in computer science and ICT. Pedagogical measurements, no. 4, pp. 52–66. (In Russ., abstract in Eng.)
5. Krylov, S. S., 2020. Methodological recommendations for teachers prepared on the basis of the analysis of typical mistakes of the participants of the 2020 Unified State Exam in computer science and ICT. Pedagogical measurements, no. 3, pp. 113–128. (In Russ., abstract in Eng.)
6. Krylov, S. S., 2021. Methodological recommendations for teachers prepared on the basis of the analysis of typical mistakes of participants of the Unified State Exam in 2021 in computer science and ICT. Pedagogical measurements, no. 4, pp. 29–45. (In Russ., abstract in Eng.)
7. Pentin, A. Yu., Kovaleva, G. S., Davydova, E. I., Smirnova, E. S., 2018. The state of natural science education in the Russian school according to the results of international studies TIMSS and PISA. Questions of education, no. 1, pp. 79–109. DOI: 10.17323/1814-9545-2018-1-79-109. (In Russ., abstract in Eng.)
8. Zuckerman, G. A., Kovaleva, G. S., Kuznetsova, M. I., 2011. Victory in PIRLS and defeat in PISA: the fate of reading literacy of 10-15 summer schoolchildren. Questions of Education, no. 2, pp. 123–150. DOI: 10.17323/1814-9545-2011-2-123-150. (In Russ., abstract in Eng.)
9. Chudinsky, R. M., Bykanov, A. S., 2019. The use of contextual data in the analysis of the results of procedures for assessing the quality of school education in the Voronezh region. Problems and prospects for the development of education quality assessment systems. The integrating role of information policy in ensuring the effectiveness of the regional system for assessing the quality of education. Proc. of the IV interregional Sci. and pract. conf. Chelyabinsk: RCOKIO Publ., pp. 188–194. (In Russ.)
10. Chudinsky, R. M., Bykanov, A. S., Volodin, A. A., Malev, V. V., 2020. Study of the dependence of the subject results of students in grades 5-9 on the contextual data of educational organizations of the Voronezh region. International Research Journal, no. 5-3 (95), pp. 151–160. DOI: 10.23670/IRJ.2020.95.5.112. (In Russ., abstract in Eng.)
11. Chudinsky, R. M., Bykanov, A. S., Chudinova, T. A., 2020. The results of the study of the level of formation of the subject content in the computer science course of secondary general education for 1st year students of the Faculty of Physics and Mathematics. Proceedings of the Voronezh State Pedagogical University, no. 2 (287), pp. 32–38. (In Russ., abstract in Eng.)
12. Chudinsky, R. M., Bykanov, A. S., Volodin, A. A., Malev, V. V., 2020. Investigation of the dependence of meta-subject results of students of grades 4-8 on contextual data of educational organizations of the Voronezh region. Modern problems of science and education, no. 3 [online]. Available at: https://science-education.ru/ru/article/view?id=29798 (accessed: 21.02.2022). (In Russ., abstract in Eng.)
13. Chudinsky, R. M., Malev, V. V., Mosolov, O. N., Dubov, V. M., 2021. Analysis of the results of the Unified State Exam in the Voronezh Region in 2020. The world of science. Pedagogy and psychology, no. 2 [online]. Available at: https://mir-nauki.com/PDF/15PDMN221.pdf (accessed: 03.21.2022). (In Russ., abstract in Eng.)
14. Chudinsky, R. M., Malev, V. V., Dubov, V. M., Kubryakov, E. A., Basharina, S. O., 2021. Analysis of the results of the input assessment of the subject content for the computer science course of secondary general education for 1st-year students of the Faculty of Physics and Mathematics in 2020. International Scientific Research Journal, no. 6-4 (108), pp. 191–199. DOI: 10.23670/IRJ.2021.108.6.135. (In Russ., abstract in Eng.)
15. Yastrebov, G. A., Pinskaya, M. A., Kosaretsky, S. G., 2014. The use of contextual data in the education quality assessment system: experience in the development and testing of tools. Education issues, no. 4, pp. 58–95. (In Russ., abstract in Eng.)
16. Epstein, J. L., McPartland, J. M., 1976. The Concept and Measurement of the Quality of School Life. American Educational Research Journal, vol. 13 (1), pp. 15–30. (In Eng.)
17. Ginsburg, G. S., Bronstein, P., 1993. Family Factors Related to Children’s Intrinsic. Extrinsic Motivational Orientation and Academic Performance. Child Development, vol. 64 (5), pp. 1461–1474. (In Eng.)
18. OECD (2019), PISA 2018 Results (Volume III): What School Life Means for Students’ Lives, PISA, OECD Publishing, Paris. Available at: https://www.oecd.org/publications/pisa-2018-results-volume-iii-acd78851-... (accessed: 03.02.2022). DOI: 10.1787/acd78851-en. (In Eng.)
19. Perry, L. B., 2012. What do we know about the causes and effects of school socio-economic composition? A review of the literature. Education and Society, vol. 30(1), pp. 19–35. DOI: 10.7459/es/30.1.03. (In Eng.)
20. Rivkin, S. G., Hanushek, E. A., Kain, J. F., 2005. Teachers, Schools, and Academic Achievement. Econometrica, vol. 73(2), pp. 417–458. (In Eng.)
21. Sirin, S. R., 2005. Socioeconomic Status and Academic Achievement: a Meta-Analytic Review of Research. Review of Educational Research, vol. 75 (3), pp. 417–453. (In Eng.)