Using a Tutoring System to Teach High-Quality Coding Practices

使用辅导系统教授高质量的编码实践

• 批准号: 2142648
• 负责人: Amruth Kumar
• 金额: $ 30万
• 依托单位: Ramapo College of New Jersey
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2142648&HistoricalAwards=false
• 关键词: Using; Tutoring; System; Teach; Quality

This project aims to serve the national interest by developing, evaluating, and disseminating a tutoring system that will teach students to write high-quality programming code. Code quality has a direct impact on the cost of modern software and the productivity of professional programmers. High-quality code should be correct, understandable, maintainable, and extendable. In a typical academic setting, students write code that is correct, but is hard to understand, maintain, or extend. Current approaches to teaching students how to write high-quality code, such as tutor/peer code review, live coding, and refactoring instruction, are resource-intensive and not scalable. This tutoring system will teach students to write high-quality code by solving problems on their own time and by automatically providing guidance on how the students can improve the quality of the code. This project addresses a significant unmet need of programming education. It will contribute to research on how to effectively teach programming. Additionally, the project has potential to better prepare Computer Science graduates to enter the professional computing workforce. The tutoring system will generate refactoring problems as randomized instances of templates, with each problem illustrating one semantic anti-pattern. The system will provide a limited set of editing operations with which to solve problems and will generate feedback that will help students to incrementally solve problems and understand the anti-pattern. The system will automatically adapt to the learning needs of each student and will generate progress reports for instructors. The tutor will cover up to 100 semantic anti-patterns and include 5 – 10 problem templates per anti-pattern. The system will be available for C++, Java, and Python. The problems and feedback designed for each anti-pattern will be evaluated by observing the efficacy of the tutor in helping students learn to refactor code, and the system’s effectiveness in helping students to write high-quality code. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在通过开发、评估和传播一个指导学生编写高质量编程代码的辅导系统来服务于国家利益。代码质量直接影响到现代软件的成本和专业程序员的生产力。高质量的代码应该是正确的、可理解的、可维护的和可扩展的。在典型的学术环境中，学生编写的代码是正确的，但很难理解、维护或扩展。目前教授学生如何编写高质量代码的方法，如导师/同行代码评审、实时编码和重构指导，都是资源密集型的，而且不可扩展。这个辅导系统将通过在自己的时间里解决问题，并自动提供关于学生如何提高代码质量的指导，来教学生编写高质量的代码。这个项目解决了一个重要的未满足的编程教育需求。它将有助于研究如何有效地教授编程。此外，该项目有潜力更好地准备计算机科学毕业生进入专业计算劳动力。辅导系统将生成重构问题作为模板的随机实例，每个问题说明一个语义反模式。系统将提供一组有限的编辑操作来解决问题，并将生成反馈，帮助学生逐步解决问题并理解反模式。该系统将自动适应每个学生的学习需求，并为教师生成进度报告。本教程将涵盖多达100个语义反模式，每个反模式包括5 - 10个问题模板。该系统将支持c++、Java和Python。通过观察导师在帮助学生学习重构代码方面的有效性，以及系统在帮助学生编写高质量代码方面的有效性，将对每个反模式设计的问题和反馈进行评估。NSF IUSE: EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。