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

布朗大学提议为K-12课程创建轻量级数据科学课程。数据科学可以大致定义为“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。这里的“轻量级”一词并不是贬义的，而是指一种与CS for All Initiative的目标相一致的方法，该目标是将CS教育扩展到所有学校和所有学生。这个项目将数学限制为“naïve”统计，而不是依赖于连续数学。对于编程，课程将创建支持，不需要比CS原理课程更复杂，甚至更少。最后，它的设置也很简单，使用谷歌Sheets作为主要的“数据库”系统，减轻了教师和学校的管理负担，并提供了许多学生和教师已经熟悉的优雅用户界面。编程支持将嵌入到Pyret（一种教学编程语言）中，通过对表和表格操作的直接支持对其进行扩展。这将允许学生编写简洁的程序，使用旨在唤起SQL的结构。为了增加年轻学生的访问权限并限制打字，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，允许学生挑选他们感兴趣的主题，并找到他们想要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，以及教师找到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和其他人之前工作的基础上，该项目将涉及一些初步调查，即提供高水平的构式并让教师“向构式”授课是否会自然地导致更好的计划作文。

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.