CAREER: Interactive Systems for Learning Programming at Scale

职业：大规模学习编程的交互式系统

• 批准号: 1845900
• 负责人: Philip Guo
• 金额: $ 47.99万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845900&HistoricalAwards=false
• 关键词: CAREER; Interactive; Systems; Learning; Programming

Computer programming is an important skill for many modern professions in fields ranging from technology to business to healthcare. Millions of people now want to learn this skill to prepare for a growing variety of careers. Although some well-resourced schools offer programming courses, the vast majority of people in this country do not have access to high-quality classroom learning environments. Thus, there is a critical need to bring the best aspects of these in-person environments to freely-accessible online settings in order to provide more people with educational opportunities. The goal of this project is to develop ways to scale up computer programming education by enabling anyone to receive free one-on-one tutoring along with computer-generated feedback as they learn programming. Its outcomes will result in a more diverse and globally-competitive American technological workforce.This project will develop, deploy, and evaluate two novel interactive systems that enable large groups of people to help one another learn programming in online environments where experts are often not available. Specifically, the research team will develop two new systems atop their widely-used Python Tutor online education platform: 1) Omnitutor, which organizes learners to tutor one another even while they are individually working on their own code, 2) Rosetta, which enables learners to annotate their code and errors with hints that may benefit future learners who face similar issues. Deploying and evaluating these systems will advance the state of scientific knowledge regarding: 1) how to coordinate a group of learners to provide both synchronous and asynchronous help while they are each working on their own code; 2) how to design interactive technologies that achieve this goal via new algorithms such as code similarity analysis, machine-assisted code simplification, and generalization of code annotations; 3) how these designs enable novices to provide high-quality assistance to people they do not personally know; and 4) common novice misconceptions about programming languages.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.

计算机编程是许多现代专业的重要技能，从技术到业务再到医疗保健。现在，数以百万计的人想学习这项技能，为不断增长的职业做准备。尽管一些资源良好的学校提供​​编程课程，但该国绝大多数人都无法获得高质量的课堂学习环境。因此，迫切需要将这些面对面环境的最佳方面带入可自由接近的在线环境，以便为更多的人提供教育机会。该项目的目的是通过使任何人在学习编程时获得免费的一对一辅导以及计算机生成的反馈来开发扩大计算机编程教育的方法。它的结果将导致更多样化和全球竞争性的美国技术劳动力。该项目将开发，部署和评估两个新颖的交互式系统，使大量人能够在通常无法使用专家的在线环境中帮助彼此学习编程。具体而言，研究团队将在其广泛使用的Python导师在线教育平台上开发两个新系统：1）Omnitutor，该计划即使他们单独使用自己的代码，2）Rosetta，该学习者即使他们单独使用自己的代码，这使学习者能够注释他们的代码和错误，从而使他们可能会受益于未来的学习者。部署和评估这些系统将提高科学知识的状态：1）如何协调一组学习者在每个人都在使用自己的代码时提供同步和异步帮助； 2）如何设计通过新算法来实现此目标的交互技术，例如代码相似性分析，机器辅助代码简化和代码注释的概括； 3）这些设计如何使新手为他们不认识的人提供高质量的援助； 4）关于编程语言的常见新手误解。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评论标准来评估值得支持的。

项目成果

Ten Million Users and Ten Years Later: Python Tutor’s Design Guidelines for Building Scalable and Sustainable Research Software in Academia

• DOI: 10.1145/3472749.3474819
• 发表时间: 2021-10
• 期刊: The 34th Annual ACM Symposium on User Interface Software and Technology
• 作者: Philip J. Guo

Learnersourcing at Scale to Overcome Expert Blind Spots for Introductory Programming: A Three-Year Deployment Study on the Python Tutor Website

大规模学习者资源以克服入门编程的专家盲点：Python 导师网站上为期三年的部署研究

• DOI: 10.1145/3386527.3406733
• 发表时间: 2020
• 期刊: Learning at Scale
• 作者: Guo, Philip J.;Markel, Julia M.;Zhang, Xiong
• 通讯作者: Zhang, Xiong

Inside the Mind of a CS Undergraduate TA: A Firsthand Account of Undergraduate Peer Tutoring in Computer Labs

计算机科学本科生助教的内心世界：计算机实验室本科生同伴辅导的第一手资料

• 发表时间: 2021
• 期刊: ACM Technical Symposium on Computer Science Education
• 作者: Markel, Julia M.;Guo, Philip J.
• 通讯作者: Guo, Philip J.

From "Ban It Till We Understand It" to "Resistance is Futile": How University Programming Instructors Plan to Adapt as More Students Use AI Code Generation and Explanation Tools such as ChatGPT and GitHub Copilot

• DOI: 10.1145/3568813.3600138
• 发表时间: 2023-08
• 期刊: Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 1
• 作者: Sam Lau;Philip J. Guo

Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia

定位、框架、桥梁、魔法和咨询：数据科学家如何驾驭工业界和学术界客户合作的外环

• DOI: 10.1145/3476052
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Kross, Sean;Guo, Philip
• 通讯作者: Guo, Philip