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

周二：EAGER：为真实的计算学习搭建大数据支架

• 批准号: 1444094
• 负责人: Dennis Kafura
• 金额: $ 9.77万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1444094&HistoricalAwards=false
• 关键词: TUES; EAGER; Scaffolding; Big; Data

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.

计算的持续快速发展及其对世界的影响为大学级别的计算学习体验设计带来了新的挑战。在所有研究领域，包括“数字人文科学”等新兴领域，都需要越来越需要了解计算的技能和知识。对“计算思维”作为21世纪的能力的越来越多的认识要求学习计算的基础知识成为每个大学学生教育的一部分。但是，将课程，教学法和工具编织在一起，以使学习者具有不同的性格和对计算学习的期望，这是一个至关重要的挑战。该探索性项目将通过使用“大数据”来制定真实现象的真实和引人入胜的学习经验来调查应对这一挑战的方法，这些学习经验与真实现象有关，并且在规模和复杂性上是现实的。这种方法刺激了学习的动机，并将现实世界问题置于学习经验的核心。现实世界的重点会鼓励对计算的其他研究，被认为对于招募和保留妇女在计算密集型领域中特别有用。该项目将创建课程和技术，用于计算思维课程和两个入门计算机科学课程。这项工作将（1）开发利用“大数据”的新型课程资源，包括整合图形编程，可视化和在线执行的交互式学习材料； （2）通过添加许多新流以及包括可视化和其他关键服务的接口来扩展和增强用于脚手架访问大数据流的框架； （3）制定，应用和分析一系列广泛的评估措施，包括与学习目标，学生动机和学生队列动态有关的评估。这项工作可以作为国家测试床：（1）为其他大学提供模型，以应对所有学生为计算提供教育的挑战，（2）为开发计算机科学研究的较小研究课程提供了跨度，（3）创建资源，为现有的计算机科学课程注入现有的计算机科学课程，并介绍了为大型数据项目提供参与计算机的介绍，该课程介绍了计算机的发展。