Context, language, and technology in data literacy

Abstract

Integrating data literacy into K-12 education in an increasingly data-driven society is imperative. Data literacy is conceptualized as an interdisciplinary competence that extends beyond traditional statistical understanding, encompassing skills in accessing, analyzing, interpreting, and effectively communicating insights derived from data. The paper argues for a paradigm shift in educational approaches, advocating for incorporating contextual, inquiry-based methodologies over the traditional formalisms-first approach. This shift is essential for enhancing students' ability to apply data literacy skills in real-world contexts. The limitations of a formalisms-first pedagogical approach are discussed, highlighting its potential to restrict students' practical application of theoretical knowledge. In contrast, the article advocates for inquiry-driven educational strategies like project-based and problem-based learning to foster deeper engagement and understanding of data literacy. These strategies may be more effective in connecting theoretical concepts with students' lived experiences and real-world applications. Additionally, the paper argues that data literacy should be framed as language. Designers of data literacy learning progressions should draw on examples from mathematics and science domains and research to build students' understanding of the transformation processes from data to evidence and subsequently to models and explanations. Further, the article explores the integration of technology in data literacy education. It underscores the role of digital tools and platforms in facilitating interactive, hands-on experiences with complex data sets, enriching the learning process, and preparing students for the challenges of the digital era. In conclusion, the article calls for a comprehensive, interdisciplinary approach to data literacy education underpinned by technology-enhanced learning environments. This approach is essential for developing both the technical skills for data manipulation and a critical mindset for data evaluation and interpretation, thereby cultivating a responsible, data-literate citizenry capable of informed decision-making in a data-rich world.