Problem
Research questions and friction points this paper is trying to address.
data literacy
K-12 education
learning trajectories
data-driven systems
computer science education
Mapping data literacy trajectories in K-12 education
🤖 AI Summary
This study investigates the cognitive pathways through which K–12 students develop data literacy across disciplines and contexts, with particular attention to learning differences that emerge during the transition from knowledge-driven to data-driven paradigms. Through a systematic literature review of 84 studies, the authors propose a “Data Paradigm Framework” that characterizes learning activities along two dimensions: logical type (knowledge-driven vs. data-driven) and model interpretability (transparent vs. opaque). This framework delineates four archetypal trajectories of data literacy development, offering a visual tool to understand students’ cross-paradigm evolution. By doing so, it extends the theoretical foundations of data literacy and provides new perspectives and empirical grounding for interdisciplinary curriculum design and instructional practice.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
data literacy
learning trajectories
data-driven systems
explainability
K-12 education
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