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给大学一级的计算学习经验的设计带来了新的挑战。在所有研究领域，包括“数字人文科学”这样的新兴领域，都越来越需要关于计算的渊博技能和知识。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、教学方法和工具编织在一起，让学习者对他们的计算学习有不同的倾向和期望，这是一个关键的挑战。这个探索性项目将探索一种应对这一挑战的方法，通过使用与真实现象有关的、在规模和复杂性上现实的“大数据”来制作真实和引人入胜的学习体验。这种方法激发了学习的动力，并将现实世界的问题置于学习体验的核心。关注现实世界鼓励更多的计算学习，被认为对在计算密集型领域招聘和留住女性特别有用。该项目将创建课程和技术，用于一门计算思维课程和两门计算机科学入门课程。这项工作将(1)利用“大数据”开发新的课程资源，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)通过添加许多新的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访问大数据流的框架；以及(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的动态有关的评估。这项工作可以作为一个国家试验台：(1)为其他大学提供一个模型，以应对为所有学生提供计算教育的挑战；(2)为开发计算机科学的辅修课程提供一个入口；(3)创建资源，将现有的计算机科学课程与参与大数据项目相结合；以及(4)促进关于如何最好地向学生介绍计算机科学的理论的不断发展的讨论。

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.