The emerging field of Data Science has had a large impact on science and society. This has led to over a decade of calls to establish a corresponding field of Data Science Education. There is still a need, however, to more deeply conceptualize what a field of Data Science Education might entail in terms of scope, responsibility, and execution. This special issue explores how one distinguishing feature of Data Science—its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded—suggests serious implications for learning and education. The learning sciences is uniquely positioned to investigate how such contextual embeddings impact learners’ engagement with data including conceptual, experiential, communal, racialized, spatial, and political dimensions. This special issue demonstrates the richly layered relationships learners build with data and reveals them to be not merely utilitarian mechanisms for learning about data, but a critical part of navigating data as social text and understanding Data Science as a discipline. Together, the contributions offer a vision of how the learning sciences can contribute to a more expansive, agentive and socially aware Data Science Education.

新兴的数据科学领域对科学和社会产生了重大影响。这导致了十多年来呼吁建立数据科学教育的相应领域。但是，仍然需要更深入地概念化数据科学教育领域在范围，责任和执行方面可能需要的内容。本期专刊探讨了数据科学的一项显着特征-侧重于从学习者经常发现自己已深深嵌入其中的社会和环境环境中收集的数据-对学习和教育的严重影响。学习科学在研究此类上下文嵌入如何影响学习者对包括概念，经验，社区，种族，空间和政治维度等数据的参与方面具有独特的地位。本期专刊展示了学习者与数据建立的深层次关系，并揭示了它们不仅是学习数据的功利主义机制，而且还是将数据作为社交文本进行导航以及将数据科学理解为一门学科的关键部分。这些贡献共同提供了一种愿景，即学习科学如何为更广阔，更具代理性和社会意识的数据科学教育做出贡献。