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.

翻译：数据素养技能是计算机科学教育的基础。然而，理解数据驱动系统的工作原理代表了从传统基于规则的编程范式的转变。我们对84项研究进行了系统性文献综述，以了解K-12学习者在不同学科和背景下与数据的互动方式。我们提出了数据范式框架，该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的系统），以及（ii）可解释性（透明或不透明模型）。我们进一步应用学习轨迹的概念，可视化学习者在这些不同范式间所遵循的路径。我们详细描述了四种不同的轨迹，以此激发研究者和教育者反思数据素养概念如何因学习情境而异。我们建议这些轨迹可能对设计计算机科学教育内外的数据素养学习环境的人员有所助益。