提出数据素养学习路径框架，帮助理解中小学生学数据的四种不同方式。
Mapping data literacy trajectories in K-12 education
- 按逻辑与可解释性将数据学习分为四类范式
- 基于84项研究提炼出学生在不同情境下的数据素养发展路径
- 适合关注中小学数据教育的设计者和研究者参考
数据素养是计算机科学教育的基础。然而，理解数据驱动系统的工作机制，相较于传统规则编程，代表了一种范式转变。我们对84项研究进行了系统性文献综述，探讨了中小学生在跨学科和不同情境下与数据的互动。提出了数据范式框架，从两个维度对学习活动进行分类：(i) 逻辑（基于知识或数据驱动系统），(ii) 可解释性（透明或不透明模型）。进一步引入学习轨迹概念，可视化学习者在这些不同范式间的演进路径。本文详细描述了四种典型轨迹，旨在引发研究者与教育工作者反思数据素养在不同学习情境中的差异。建议这些轨迹可为计算机科学教育内外的数据素养学习环境设计提供参考。
原文摘要 · Abstract (English)
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.
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