Automated positive reinforcement of good programming processes

自动积极强化良好的编程过程

• 批准号: 2313793
• 负责人: Mariam Salloum
• 金额: $ 40万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313793&HistoricalAwards=false
• 关键词: Automated; positive; reinforcement; good; programming

This project aims to serve the national interest by developing techniques to automatically award points to students for following a good process when students write computer programs. This project intends to address a common problem that students perform poorly in computer programming courses because they do not follow a good process. The "process" refers to an approach students take as they develop their computer programs. While instructors may advise students to follow a good process, students often do not take such advice, perhaps believing they can take a shortcut to a working program. Giving points to students is the most direct and common way to positively influence student behaviors. However, manually awarding points for following a good process is a labor-intensive grading task. This project intends to create automated methods to reward a good programming process, meanwhile developing a deeper understanding of how a good programming process can improve student metacognition and success. This project team plans to take advantage of new programming-learning systems that can capture process-related data to automatically analyze students' recorded behaviors and award points. Process behaviors to be auto-awarded with points will include four phases: 1) starting early, 2) spending sufficient time, 3) writing programs little by little, and 4) testing one's own programs thoroughly. Such an automation has only recently become a possibility, via the advent of cloud-based programming-learning environments that record much of a student's programming behavior. The project intends to answer several research questions: 1) Can desired process points be auto-awarded using today's widely-used programming-learning systems? 2) Can process points incentivize good process while avoiding negative consequences like stressing students over "being watched"? 3) Can process points be made transparent and understandable through simple visualizations and basic explanations? 4) Will process points ultimately impact students' metacognition, behavior, and success? The techniques will be codified in Python scripts that will be made available to programming instructors across the country. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This project is also supported by the NSF IUSE:HSI program, which has the goals of enhancing the quality of undergraduate STEM education, and increasing the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees in STEM.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技术来自动向学生授予积分，以遵循学生编写计算机程序时的良好过程，以为国家利益提供服务。该项目旨在解决学生在计算机编程课程中表现较差的常见问题，因为他们没有遵循一个良好的过程。 “过程”是指学生在开发计算机程序时采用的方法。尽管讲师可能建议学生遵循一个良好的过程，但学生通常不会接受这样的建议，也许认为他们可以为工作计划带来捷径。给学生积极的是积极影响学生行为的最直接和常见方法。但是，遵循良好过程的手动授予积分是一项劳动密集型的分级任务。该项目旨在创建自动化方法来奖励良好的编程过程，这意味着在对良好的编程过程如何改善学生元认知和成功的更深入了解。该项目团队计划利用新的编程学习系统，这些系统可以捕获与过程相关的数据，以自动分析学生的记录行为和奖励点。要自动授予点的过程行为将包括四个阶段：1）早点开始，2）花费足够的时间，3）稍作编写程序，4）彻底测试自己的程序。这种自动化直到最近才通过基于云的编程学习环境来记录学生的大部分编程行为，才有可能。该项目打算回答几个研究问题：1）使用当今广泛使用的编程学习系统可以自动授予所需的过程点吗？ 2）流程点可以激励良好的过程，同时避免强调学生“被监视”等负面后果？ 3）可以通过简单的可视化和基本解释使过程点透明且可以理解吗？ 4）过程点最终会影响学生的元认知，行为和成功吗？这些技术将在Python脚本中进行编纂，这些脚本将提供给全国各地的编程讲师。 NSF IUSE：EDU计划支持研发项目，以提高所有学生STEM教育的有效性。通过其参与的学生学习轨道，该计划支持了承诺实践和工具的创建，探索和实施。 NSF IUSE：HSI计划也支持该项目，该计划的目标是提高本科STEM教育的质量，并提高招聘，保留和毕业率的学生在STEM中追求合伙人或学士学位的学生的毕业率。该奖项奖励NSF的法定任务并通过评估范围来审查律师的范围。