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

这个项目的意义和重要性在于，无论是对攻读计算机科学学位的学生，还是对计算在其学科实践中扮演次要但日益重要的角色的学生，都可以利用改进的科学来教授基本的计算概念。这个项目解决的根本问题是，目前可用来激励和维持学生参与学习过程中具有挑战性的部分的方法有限。通过使用相关和真实的数据，并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”。虽然现实主义的这些特点对所有学生都有利，但对代表不足的人群中的学生来说尤其吸引人。“大数据”资源不仅在入门课程中很有用，对新兴的数据科学课程也很有用。该项目将深入了解该项目的干预措施对不同学生群体的学生动机和学习的影响。这一理解将影响课程设计，并有助于形成更好的教学实践。这个项目的目标和范围将是增强学生学习入门计算概念的动机和认知收益的知识和扩展技术。该项目将扩展现有数据集的目录，增加创作和管理工具，使数据集能够有效地创建和重组，提高学生搜索相关数据集的能力，并提供脚手架，以便能够及早实现“大数据”的可视化。该项目将扩展一个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。该扩展将增加教师创作工具和运行时支持，以获得基于算法的练习的即时反馈，从而改善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堂上学习。这些资源将与其他课程要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。该项目将应用迪克和凯里的教学设计方法，以便于其他人采用，并整合课程的详细评估。

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.