CAREER: Interactive Systems for Learning Programming at Scale

计算机编程是从技术到商业到医疗保健等领域的许多现代职业的重要技能。现在有数百万人想学习这项技能，为越来越多的职业做准备。尽管一些资源充足的学校提供编程课程，但这个国家的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到可自由访问的在线环境中，以便为更多人提供教育机会。该项目的目标是开发方法，通过使任何人都能在学习编程时接受免费的一对一辅导沿着计算机生成的反馈，来扩大计算机编程教育。该项目将开发、部署和评估两种新颖的交互式系统，使大量的人能够在专家通常无法访问的在线环境中相互帮助学习编程。具体来说，研究团队将在他们广泛使用的Python Tutor在线教育平台上开发两个新系统：1）Omnitutor，它可以组织学习者相互指导，即使他们正在单独编写自己的代码，2）Rosetta，它使学习者能够用提示注释他们的代码和错误，这可能会使未来面临类似问题的学习者受益。部署和评估这些系统将推进以下方面的科学知识：1）如何协调一组学习者在各自编写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实现这一目标的交互式技术，如代码相似性分析，机器辅助代码简化和代码注释的泛化; 3）这些设计如何使新手能够为他们不认识的人提供高质量的帮助;以及4）新手对编程语言的常见误解。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.