Mapping data literacy trajectories in K-12 education

TL;DR

This paper reviews 84 studies to map how K-12 students develop data literacy skills, introducing a framework and trajectories to guide education design in data-driven contexts.

Contribution

It proposes the data paradigms framework and visualizes learning trajectories, offering new insights for designing data literacy education in K-12 settings.

Findings

Identified four distinct data literacy trajectories in K-12 education.

Developed the data paradigms framework categorizing learning activities.

Provided a visual tool for understanding learners' pathways across data paradigms.

Abstract

Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from traditional rule-based programming. We conducted a systematic literature review of 84 studies to understand K-12 learners' engagement with data across disciplines and contexts. We propose the data paradigms framework that categorises learning activities along two dimensions: (i) logic (knowledge-based or data-driven systems), and (ii) explainability (transparent or opaque models). We further apply the notion of learning trajectories to visualize the pathways learners follow across these distinct paradigms. We detail four distinct trajectories as a provocation for researchers and educators to reflect on how the notion of data literacy varies depending on the learning context. We suggest these trajectories could be useful to those concerned with the design of data literacy learning environments within and beyond CS education.