Developing inclusive, interdisciplinary undergraduate data science curricula in computing and social science

数据科学是一个快速发展的领域，对我们的生活、工作和互动方式产生了重大影响。该项目的目标是在计算机科学与行为科学和社会科学的交叉点上创建一个跨领域的数据科学课程。本课程将提供核心计算和编程概念以及核心定量行为和社会科学方法的培训。课程将被设计成多学科、文化相关和严谨的。核心课程开发活动旨在通过一种涉及来自不同学科、不同机构和不同背景的教师的课程创造的协作方法来改变计算机科学教育。部落学院、HBCUs和加州社区学院将被邀请参加课程专题讨论会，并支持采用这些材料。课堂材料将被设计成教学视频、计算机实验室、指导讨论、项目和概念评估的脚手架集合，可以部分或全部被全国各地的机构采用。开源课程将包括以现代社会技术系统和数据为基础的独立课程模块。调查人员将设计和测试一种混合教学交付机制，以确保全国教师的广泛可及性。持续的评估和研究将使调查人员了解项目活动将如何促进传统上代表性不足的社区学生在跨学科计算机教育方面的进一步研究。该项目将确定课程的各个方面，以建立学习者对计算思维和数据科学的信心，并将为包容性计算机科学和数据科学教育贡献最佳实践。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Data science is a rapidly growing field that is having a significant impact on how we live, work, and interact. The goal of this project is to create a cross-cutting data science curriculum at the intersection of computer science and behavioral and social science. This curriculum will provide training in both core computing and programming concepts as well as core quantitative behavioral and social science methods. The curriculum will be designed to be multidisciplinary, culturally relevant, and rigorous. The core proposed course development activity aims to transform computer science education through a collaborative approach to curricular creation that involves faculty from diverse disciplines, diverse institutions, and diverse backgrounds. Tribal colleges, HBCUs, and California Community Colleges, will be invited to curriculum symposia events and supported in adopting these materials.Classroom materials will be designed as a scaffolded collection of instructional lecture videos, computing labs, guided discussions, projects, and concept assessments that can be adopted partly or wholly at institutions across the nation. The open-source curriculum will consist of standalone course modules grounded in modern socio-technical systems and data. Investigators will design and test a hybrid instructional delivery mechanism to ensure broad accessibility to instructors nationwide. Continuous assessment and research will inform investigators on how the project activities will promote further study in interdisciplinary computing education amongst students from traditionally underrepresented communities. The project will identify aspects of the curriculum that build learner confidence in computational thinking and data science and will contribute best practices for inclusive computer science and data science education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.