TUES: EAGER: Scaffolding Big Data for Authentic Learning of Computing

计算的持续快速发展及其对世界的影响为大学计算学习体验的设计带来了新的挑战。所有研究领域，包括“数字人文”等新兴领域，都越来越需要深入了解计算的技能和知识。越来越多的人意识到“计算思维”是21世纪的一种能力，这就要求学习计算的基础知识成为每个大学生教育的一部分。然而，将课程、教学法和工具编织在一起，以吸引具有不同性格和对计算学习期望的学习者，是一个关键的挑战。这个探索性项目将探索一种应对这一挑战的方法，通过使用“大数据”制作真实的、引人入胜的学习经验，这些经验涉及真实的现象，在规模和复杂性上都是现实的。这种方法激发了学习的动机，并将现实世界的问题置于学习经验的核心。现实世界的焦点鼓励对计算进行更多的研究，并被认为对在计算密集型领域招聘和留住妇女特别有用。该项目将为一门计算思维课程和两门计算机科学入门课程创建课程和技术。这项工作将(1)利用“大数据”开发新颖的课程资源，包括集成图形编程、可视化和在线执行的交互式学习材料；(2)通过增加许多新的数据流，并包括可视化和其他关键服务的接口，扩展和增强用于支撑大数据流访问的框架；(3)开发、应用和分析一套广泛的评估措施，包括与学习目标的实现、学生动机和学生群体动态有关的评估。这项工作可以作为一个全国性的试验台：(1)为其他大学应对为所有学生提供计算教育的挑战提供了一个模型，(2)为开发计算机科学的辅修课程提供了一个入口，(3)创建资源，将现有的计算机科学课程融入大数据项目，(4)有助于发展关于如何最好地将学生引入计算机科学的理论讨论。

The continued rapid evolution of computation and its impact on the world creates new challenges for the design of learning experiences in computation at the university level. Deeply informed skills and knowledge about computation are increasingly needed in all fields of study, including emerging fields like the "digital humanities." Growing awareness of "computational thinking" as a 21st century competency requires that learning the basics of computation becomes a part of every university student's education. However, weaving together the curriculum, pedagogy, and tools that engage learners with different dispositions and expectations about their learning of computation is a critical challenge. This exploratory project will investigate an approach to meeting this challenge by crafting authentic and engaging learning experiences using "big data" that are about real phenomenon and are realistic in scale and complexity. This approach stimulates motivation to learn and places real world problems at the core of the learning experience. The real world focus encourages additional study of computation and is believed to be especially useful to recruit and retain women in computing-intensive fields.The project will create curriculum and technologies for use in a computational thinking course and two introductory computer science courses. The work will (1) develop novel curriculum resources leveraging "big data", including interactive learning materials integrating graphical programming, visualization, and in-line execution; (2) expand and enhance a framework used to scaffold access to big data streams by adding many new streams and including interfaces to visualization and other critical services; and (3) develop, apply, and analyze an extensive set of assessment measures including assessments related to achievement of learning objectives, student motivation, and the dynamics of student cohorts. This work can serve as a national test-bed that: (1) offers a model for other universities grappling with the challenge of providing education in computation for all students, (2) provides an on-ramp for developing minor courses of study in computer science, (3) creates resources that infuse existing computer science courses with engaging big data projects, and (4) contributes to evolving discussions of theories on how students are best introduced to the computer science.