Data literacy across disciplines: Context, language, and technology in K-12 learning
TL;DR
It is argued that the formalisms-first pedagogical approach to data literacy education is theoretically limited, rooted in a conception of learning that strips away the social, cultural, and material conditions through which meaningful data reasoning develops, and advocated for inquiry-driven strategies like project-based and problem-based learning, which restore the contextual, collaborative conditions that make data literacy learnable.
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 communicating insights from data. Drawing on sociocultural theory, this paper argues that the formalisms-first pedagogical approach is theoretically limited, rooted in a conception of learning that strips away the social, cultural, and material conditions through which meaningful data reasoning develops. In contrast, we advocate for inquiry-driven strategies like project-based and problem-based learning, which restore the contextual, collaborative conditions that make data literacy learnable. Central to this argument is the claim that data literacy is best understood as a disciplinary language, a set of representational, argumentative, and interpretive practices acquired through socially scaffolded participation. Learning progressions should draw on contexts across STEM, humanities, and social sciences, recognizing that the language of data is relevant wherever students reason with evidence. The article also examines technology integration in data literacy education. Rather than treating digital tools as neutral delivery mechanisms, we frame them as mediating artifacts that shape how students think, represent, and argue with data. We discuss equitable access to these tools, the promise of emerging technologies such as augmented and virtual reality, and the value of unplugged approaches where access to technology is limited. In conclusion, we call for an explicitly sociocultural and interdisciplinary approach to data literacy education, essential for developing both technical skills for data work and a critical mindset for evaluating data as a social and civic practice that fosters a responsible, data-literate citizenry capable of informed participation in a data-rich world. Future empirical work is needed on the effectiveness of inquiry-driven approaches for data literacy outcomes and on what equitable technology integration looks like across diverse learning contexts.