ScholarWorks@숙명여자대학교

초록

This study explores the educational value and potential of data science education in school mathematics in response to the rise of a data-driven society, with a particular focus on whether data processing skills—centered on social network analysis (SNA)—can be recognized as a form of digital literacy. While traditional statistics education has often been limited to descriptive statistics and mechanical calculations, data science education encompasses the entire process of data collection, processing, analysis, visualization, and interpretation. It emphasizes students’ problem-solving skills, digital literacy, and creative thinking. Social network analysis, as a representative unsupervised learning method, enables mathematical modeling and interpretation of relational data through graph theory and quantitative metrics such as centrality indices. This analytical approach aligns closely with the mathematical modeling cycle in education, offering students meaningful learning experiences in mathematically structuring and critically interpreting real-world problems through mathematics. The study argues that SNA can serve as an effective educational tool for fostering digital literacy and data competency.

키워드