Exploring Patterns of Information Literacy Development in Schools: Application of Multilevel Latent Class Analysis to School Students Survey Data

P. 253–266.

Dvoretskaya I., Alexey Semenov, Uvarov A.

While the literature on digital transformation in education has searched for evidence based practices to improve ICT uptake in school settings, we know little about how schools differ in their approaches. This study aims to overcome the absence of standardized tools that could help to assess the stages and progress of ICT integration in educational settings. By using the example of information literacy development tasks assignment in classroom, we applied a latent class analysis to the survey data obtained from the monitoring the digital transformation of schools in the 2020–21 academic year. Based on the survey data from monitoring the digital transformation of schools, four types of students’ patterns were identified, depending on the information skills tasks assigned to them by their teachers at school. Based on the distribution of students’ patterns of working with information, three typical patterns of schools were identified with the use of multilevel latent class analysis. This study provides evidence for how the development of information literacy differs across schools contexts. As with advent of digital technologies education becomes data intensive domain, new approaches to the big data analysis are encouraged and it can help educators and education policy makers to improve decision-making.

Keywords: цифровая трансформация образования, digital transformation

In book

26th International Conference, DAMDID/RCDL 2024, Nizhny Novgorod, Russia, October 23–25, 2024, Revised Selected Papers. Data Analytics and Management in Data Intensive Domains. (CCIS, volume 2641)

Springer, 2026.