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How information management is taught in school today: A student’s perspective. The results of multilevel latent class analysis

https://doi.org/10.32517/0234-0453-2024-39-2-59-68

Abstract

The article presents the results of an analysis of students’ responses about teaching them how to work with information when solving learning tasks. The results of a survey conducted in the course of the project on monitoring the digital transformation of educational organizations in 2021 were used. Four specific students’ learning profiles have been identified, depending on the tasks they receive from the teacher to develop information literacy skills. Three typical groups of schools have been identified according to the types of profiles of teaching students to work with information. The influence of schools’ digital equipment, activities aimed at upgrading the functioning and development of innovative methods of educational work at school on the existing practices of teaching students to work with information has been analyzed. It was found that if a school is provided with digital equipment for students’ individual learning, teachers are more willing to give them tasks to work with information in the digital environment. It has been revealed that the development and discussion of concepts and plans for mastering digital technologies in pedagogical teams is positively related to the practice of using tasks to develop students’ information literacy. The recommendations for the formation of information literacy in general education school students are offered.

About the Authors

I. V. Dvoretskaya
National Research University Higher School of Economics
Russian Federation

Irina V. Dvoretskaya, PhD in Education, Research Fellow at the Laboratory for Digital Transformation of Education, Institute of Education

Moscow



A. Yu. Uvarov
National Research University Higher School of Economics; The Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences
Russian Federation

Alexander Yu. Uvarov, Doctor of Sciences (Education), Professor, Professor at the Department of Educational Programs, Institute of Education, National Research University Higher School of Economics; Leading Researcher, Axel Berg Institute of Cybernetics and Educational Computing, The Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

Moscow



References

1. Uvarov А. Yu. Schools’ digital renewal: Steps to the “ideal school”. Informatics and Education. 2022;37(2):5–13. (In Russian.) EDN: PWDQJB. DOI: 10.32517/0234-0453-2022-37-2-5-1.

2. Catts R., Lau J. Towards information literacy indicators. Paris, UNESCO; 2008. Available at: https://unesdoc.unesco.org/ark:/48223/pf0000158723

3. Fraillon J., Ainley J., Schulz W., Friedman T., Duckworth D. Preparing for life in a digital world: IEA international computer and information literacy study 2018 international report. Springer Cham; 2020. 297 p. DOI: 10.1007/978-3-030-38781-5.

4. Lau J., Elliott C. Guidelines on information literacy for lifelong learning. Hague, Netherlands, International Federation of Library Associations and Institutions (IFLA); 2006. 60 p.

5. Julien H., Barker S. How high-school students find and evaluate scientific information: A basis for information literacy skills development. Library & Information Science Research. 2009;31(1):12–17. DOI: 10.1016/j.lisr.2008.10.008.

6. Horvath A., Dalferth S., Ranguelov S., et al. Key data on learning and innovation through ICT at school in Europe. Brussels, Belgium, Education, Audiovisual and Culture Executive Agency (EACEA P9 Eurydice); 2011. 120 p. DOI: 10.2797/61068.

7. Avdeeva S. M., Uvarov A. Yu., Tarasova K. V. Digital technologies at school and information and communication competence of students. Educational Studies. 2022;(1):218– 243. (In Russian.) EDN: XJMJAA. DOI: 10.17323/1814-9545-2022-1-218-243.

8. Ferrari A. DIGCOMP: A framework for developing and understanding digital competence in Europe. Luxembourg, Publications Office of the European Union; 2013. DOI: 10.2788/52966.

9. Fraillon J., Ainley J., Schulz W., Friedman T., Gebhardt E. Preparing for life in a digital age: The IEA international computer and information literacy study international report. Springer Cham; 2014. 291 p. DOI: 10.1007/978-3-319-14222-7.

10. Gerick J. School level characteristics and students’ CIL in Europe — A latent class analysis approach. Computers & Education. 2018;120:160–171. DOI: 10.1016/j.compedu.2018.01.013.

11. Bundsgaard J., Gerick J. Patterns of students’ computer use and relations to their computer and information literacy: Results of a latent class analysis and implications for teaching and learning. Large-scale Assessments in Education. 2017;(5):16. DOI: 10.1186/s40536-017-0052-8.

12. Huang L., Li S., Poitras E. G., Lajoie S. P. Latent profiles of self‐regulated learning and their impacts on teachers’ technology integration. British Journal of Educational Technology. 2021;52(2):695–713. DOI: 10.1111/bjet.13050.

13. Kastorff T., Sailer M., Stegmann K. A typology of adolescents’ technology use before and during the COVID-19 pandemic: A latent profile analysis. International Journal of Educational Research. 2023;117:102–136. DOI: 10.1016/j.ijer.2023.102136.

14. Law N., Pelgrum W. J., Plomp T. Pedagogy and ICT use in schools around the world: Findings from the IEA SITES 2006 study. New York, Hong Kong, Springer; 2008. 296 p. DOI: 10.1007/978-1-4020-8928-2.

15. Goryainova A. R., Dvoretskaya I. V., Kochak E., Mertsalova T. A., Savitsky K. L. Digital renewal of the Russian school: Information Bulletin. Moscow, National Research University Higher School of Economics; 2022. 48 с. (In Russian.) EDN: TJQFEH. DOI: 10.17323/978-5-7598-2692-7.

16. Vermunt J. K. Latent class modeling with covariates: Two improved three-step approaches. Political Analysis. 2010;18(4):450–469. DOI: 10.1093/pan/mpq025.

17. Agresti A. Categorical data analysis. New York, USA, Wiley-Interscience; 2002. 710 p.

18. Vodopian G.M., Dvoretskaya I.V., Uvarov A.Yu. Towards multidimensional digital renewal model for comprehensive schools. Moscow, Education and Informatics; 2023. 136 p. (In Russian.) Available at: https://publications.hse.ru/pubs/share/direct/897090902.pdf

19. Beaujean A. A. Latent variable modeling using R: A step-by-step guide. New York, USA, Routledge; 2014. 218 p. DOI: 10.4324/9781315869780.

20. Kim Y., Jeon S., Chang C., Chung H. glca: An R package for multiple-group latent class analysis. Applied Psychological Measurement. 2022;46(5):439–441. DOI: 10.1177/01466216221084197.

21. Kacheva E. V. Formation of media and information literacy of schoolchildren in the context of the network library project. Vestnik Kazanskogo Gosudarstvennogo Universiteta Kul’tury i Iskusstv. 2017;(3):102–105. (In Russian.) EDN: ZURMGJ.


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For citations:


Dvoretskaya I.V., Uvarov A.Yu. How information management is taught in school today: A student’s perspective. The results of multilevel latent class analysis. Informatics and education. 2024;39(2):59-68. (In Russ.) https://doi.org/10.32517/0234-0453-2024-39-2-59-68

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ISSN 0234-0453 (Print)
ISSN 2658-7769 (Online)