CSforAll: EAGER: Integrating Lightweight Data Science and Computing for K-12

布朗大学提议为K-12创建轻量级数据科学课程。数据科学可以粗略地定义为“编程+统计”，因此有两个控制点：需要多少编程，以及使用多少统计数据。这里的“轻量级”一词并非贬义，而是指一种与CS for All倡议的目标相一致的方法，即将CS教育扩展到所有学校和所有学生。这个项目将数学限制在“朴素”的统计学上，而不是依赖于连续的数学。对于编程，该课程将创建支持，不需要比CS原则课程要求的更复杂，甚至可能更少。最后，它也将是轻量级的设置，使用Google Sheets作为主要的“数据库”系统，减轻教师和学校的巨大行政负担，并实现许多学生和教师已经熟悉的优雅用户界面。编程支持将嵌入Pyret，一种教学编程语言，通过直接支持表格和表格操作对其进行了扩展。这将使学生能够使用旨在唤起SQL的结构编写简洁的程序。为了增加对年轻学生的访问并限制打字，PI将在Pyret中加入双模式块和文本界面。为了吸引更多的学生，这项工作将包括对课程个性化的支持，允许学生挑选出他们感兴趣的主题，并找到他们想问的关于这些数据的相应问题。PI将提供精心策划的数据集，以及教师查找其他类似数据的方法，以帮助需要建议的学生开始学习。最后，在PI和其他人以前的工作的基础上，项目将涉及一些初步的调查，是否提供高层次的结构，让教师教“的结构”，自然会导致更好的计划组成。

Brown University proposes to create lightweight data science curricula for K-12. Data science can be roughly defined as "programming + statistics" and thus has two points of control: how much programming is needed, and how much statistics is used. The term "lightweight" here is not used pejoratively but to connote an approach that is aligned with the CS for All Initiative's goal of scaling CS education to all schools and all students. This project will limit mathematics to "naïve" statistics rather than depending on continuous mathematics. For programming, the curriculum will create support that requires no more sophistication than required by CS Principles curricula, and perhaps even less. Finally, it will also be light on setup, using Google Sheets as the primary "database" system, lifting a big administrative burden on teachers and schools, and enabling elegant user interfaces that are already familiar to many students and teachers.The programming support will be embedded into Pyret, a pedagogic programming language, extending it with direct support for tables and tabular operations. This will allow students to write concise programs using constructs designed to be evocative of SQL. To increase access to younger students and to limit typing, the PIs will incorporate a dual-mode block-and-text interface into Pyret. In order to engage more students, the work will include support for curricular personalization, allowing students to pick out the topics that they are interested in, and find the corresponding questions they want to ask about these data. The PIs will provide curated datasets, as well as means for teachers to find other similar data, to help students who need suggestions to get started. Finally, building on previous work of the PI and others, project will involve some preliminary investigation on whether providing high-level constructs and letting teachers teach "to the constructs" will naturally lead to better plan composition.