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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2603.28317 |
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| _version_ | 1866908920937709568 |
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| author | Whyte, Robert Cheung, Manni Childs, Katharine Waite, Jane Sentance, Sue |
| author_facet | Whyte, Robert Cheung, Manni Childs, Katharine Waite, Jane Sentance, Sue |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28317 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mapping data literacy trajectories in K-12 education Whyte, Robert Cheung, Manni Childs, Katharine Waite, Jane Sentance, Sue Computers and Society Artificial Intelligence 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. |
| title | Mapping data literacy trajectories in K-12 education |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2603.28317 |