Mapping data literacy trajectories in K-12 education
Organizations: Raspberry Pi Foundation, United Kingdom · University of Cambridge, United Kingdom
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.
Figures & tables
| Dimension | Knowledge-based (KB) | Data-driven (DD) |
| Transparent (T) Explainable by design | KB+T : Rule-based models which are explainable by design (e.g., rule-based decision trees, manual classification) | DD+T: Data-driven models which are explainable by design (e.g., linear regression, k-nearest neighbour) |
| Opaque (O) Explainable through additional methods (e.g. post-hoc) | KB+O: Rule-based models which are explainable through additional methods | DD+O: Data-driven models (e.g., neural networks, random forest) which are explainable through additional (e.g. post-hoc) methods |
Computer Science Education
Computer science education studies how people learn computing and how curricula, teaching methods, and assessments can support that learning.
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