Data and computational literacies empower youth to be active participants and future leaders in our increasingly data-driven society. We conducted a design-based research project in which a small group (n = 5) of high school youth from diverse backgrounds learned how to code and create data visualizations and stories with public data about climate change in a 5-day (20 h total) free virtual summer program. Using interaction analysis methods to microanalyze students’ engagements with data technologies, we developed the computational data literacy model (CDLM) to describe students’ participation in various computational data literacies that emerged from our analysis (remixing, wayfinding, interpreting variables, making hypotheses, and personalizing data) and their use of different data tools (the code, data visualization, variable of interest, and story) to support scientific inquiry and reasoning. Using the CDLM, the presented analysis investigates how students navigated across coding and storytelling cycles of activities. Within those cycles, students collaboratively problem-solved in the code and engaged in collaborative inquiry, drawing on personal experiences to make multivariate hypotheses and stories about human impacts on carbon emissions. Our findings suggest that using a socioscientific issue (SSI) context supported students’ back-and-forth movement between coding and storytelling activities, perhaps by affording greater personalization of the data, which, in turn, facilitated data-based reasoning. The findings of this study inform our understanding of the challenges and learning opportunities in this computational, data-rich intervention situated in socioscientific inquiry. We discuss future uses of our model for learning designs to support computational data activities about SSIs.

数据和计算素养使年轻人能够成为日益数据驱动的社会的积极参与者和未来领导者。我们开展了一个基于设计的研究项目，其中来自不同背景的一小组 ( n  = 5) 高中青年在 5 天（总共 20 小时）内学习如何使用有关气候变化的公共数据进行编码和创建数据可视化和故事）免费虚拟暑期课程。使用交互分析方法微观分析学生对数据技术的参与，我们开发了计算数据素养模型（CDLM）来描述学生对各种计算数据素养的参与这些都是从我们的分析（重新混合、寻路、解释变量、做出假设和个性化数据）以及他们使用不同的数据工具（代码、数据可视化、感兴趣的变量和故事）来支持科学探究和推理中产生的。使用 CDLM，所提出的分析调查了学生如何跨过编码和讲故事的活动周期。在这些周期中，学生们协作解决代码中的问题并参与协作探究，利用个人经验提出有关人类对碳排放影响的多元假设和故事。我们的研究结果表明，使用社会科学问题（SSI）背景可以支持学生在编码和讲故事活动之间来回移动，也许是通过提供更大的数据个性化，从而，促进基于数据的推理。这项研究的结果让我们了解社会科学探究中这种计算、数据丰富的干预措施所面临的挑战和学习机会。我们讨论了我们的学习设计模型的未来用途，以支持有关 SSI 的计算数据活动。