Abstract

In the context of fundamental changes in the economy and the labor market, the introduction of educational programs in the field of data analysis and machine learning at all levels of education with the priority of integrating mathematical, natural science and socio-humanitarian knowledge becomes important. An overview and analysis of foreign experience and main discussion topics in the development of educational modules for data science and machine learning for adolescents and adolescents is presented.

Abstract

The study examines the practices of teaching data literacy for adolescents. The intersections of data literacy with statistical literacy, quantitative thinking, computational skills, and Data Science are explored. A competence model for data literacy is introduced with key competency clusters – technical, scientific, and social – aligned with the components of a scientific practice and data mining research cycle. The conclusions underscore the importance of students engaging in the exploration and modeling of socio-humanitarian data as part of their learning process. This content, firstly, aligns most closely with the sociocognitive specificity of the age, secondly, it lays the foundations for students’ interdisciplinary understanding of contemporary phenomena, which is in demand across a wide range of professions. Future directions encompass refining the data literacy model, creating methodological resources for developing data literacy within education systems, and providing pedagogical guidance and materials for educators adopting data literacy techniques.