A Scaffolded Data-Centric Approach to Improved Learning of Introductory Computing Concepts

一种以数据为中心的脚手架方法，用于改进入门计算概念的学习

• 批准号: 1624320
• 负责人: Dennis Kafura
• 金额: $ 59.43万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1624320&HistoricalAwards=false
• 关键词: Scaffolded; Data; Centric; Approach; Improved

The significance and importance of this project is the availability of improved science for teaching fundamental computational concepts both to students pursuing degrees in computer science and to students where computation plays a secondary but increasingly vital role in the practice of their discipline. The fundamental problem addressed by this project is the limited methods currently available to motivate and sustain student engagement in challenging parts of the learning process. Through the use of relevant and authentic data combined with an improved learning environment the project will achieve improvements in learning and, more broadly, will provide the "data literacy" increasingly critical in the economy and society. While advantageous for all students, these characteristics of realism are especially engaging for students in under-represented populations. The "big data" resources are useful not only in introductory courses but also for emerging data science courses. The project will develop a deep understanding of the impact on student motivation and learning of the project's interventions across diverse student populations. This understanding will influence curriculum design and help shape better pedagogical practices. The goal and scope of this project will be enhanced knowledge of, and extended technologies for, improving motivation and cognitive gains of students learning introductory computing concepts. The project will extend the catalog of available data sets, add authoring and curation tools enabling the efficient creation and restructuring of data sets, improve the ability of a student to search for a relevant data set, and provide scaffolding to allow early visualization of "big data". The project will extend a block-based visual programming environment, BlockPy, allowing mutual translation between Blockly and Python. The extension will add an instructor authoring tool and run-time support for immediate feedback on algorithm-based exercises that improves student learning and encourages student exploration of alternative algorithms. New visualizations will support student learning in any programming class using big data. These resources will be combined with other course elements in a robust web framework supporting many forms of distributed instructional delivery. The project will apply the Dick and Carey Instructional Design method to facilitate adoption by others and integrate detailed assessment of the curriculum.

该项目的意义和重要性在于，为攻读计算机科学学位的学生和计算在其学科实践中扮演次要但日益重要角色的学生提供了改进的科学来教授基本的计算概念。本项目解决的根本问题是目前可用的方法有限，无法激励和维持学生在学习过程中具有挑战性的部分的参与。通过使用相关和真实的数据，结合改善的学习环境，该项目将改善学习，更广泛地说，将提供在经济和社会中日益重要的“数据素养”。虽然对所有学生都有利，但现实主义的这些特点对代表性不足的学生尤其有吸引力。“大数据”资源不仅对入门课程有用，而且对新兴的数据科学课程也有用。该项目将深入了解对不同学生群体的学生动机和学习项目干预措施的影响。这种理解将影响课程设计，并有助于形成更好的教学实践。该项目的目标和范围将是增强知识和扩展技术，提高学生学习入门计算概念的动机和认知收益。该项目将扩展可用数据集的目录，增加能够有效创建和重组数据集的创作和管理工具，提高学生搜索相关数据集的能力，并提供脚手架，以便早期实现“大数据”的可视化。该项目将扩展基于块的可视化编程环境BlockPy，允许在Blockly和Python之间进行相互翻译。该扩展将添加一个教师创作工具和运行时支持对基于算法的练习的即时反馈，以提高学生的学习，并鼓励学生探索替代算法。新的可视化将支持学生在任何使用大数据的编程课上学习。这些资源将与其他课程元素结合在一个强大的网络框架中，支持多种形式的分布式教学交付。该项目将采用迪克和凯里教学设计方法，以方便其他人采用，并整合课程的详细评估。