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服务于国家利益，以自动奖励学生在编写计算机程序时遵循良好过程的分数。这个项目旨在解决一个常见的问题，即学生在计算机编程课程中表现不佳，因为他们没有遵循一个良好的过程。“过程”指的是学生在开发计算机程序时所采取的方法。虽然教师可能会建议学生遵循一个好的过程，但学生往往不接受这样的建议，也许他们认为自己可以走捷径，找到一个可行的程序。给学生加分是积极影响学生行为最直接、最常见的方式。然而，手动奖励遵循良好流程的分数是一项劳动密集型的评分任务。该项目旨在创建自动化方法来奖励良好的编程过程，同时更深入地了解良好的编程过程如何提高学生的元认知和成功。 该项目团队计划利用新的编程学习系统，可以捕获过程相关的数据，自动分析学生的记录行为和奖励积分。 自动获得积分的过程行为将包括四个阶段：1）早期开始，2）花费足够的时间，3）一点一点地编写程序，4）彻底测试自己的程序。这种自动化直到最近才成为可能，因为基于云的编程学习环境的出现记录了学生的大部分编程行为。该项目旨在回答几个研究问题：1）可以使用今天广泛使用的编程学习系统自动授予所需的过程点？2)过程点能激励好的过程，同时避免负面后果，如强调学生“被监视”？3)通过简单的可视化和基本的解释，过程点是否可以变得透明和易于理解？4)过程点最终会影响学生的元认知、行为和成功吗？这些技术将被编入Python脚本，并提供给全国各地的编程教师。NSF IUSE：EDU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。通过其学生学习轨道，该计划支持创建，探索和实施有前途的实践和工具。该项目还得到了NSF IUSE：HSI项目的支持，该项目的目标是提高本科STEM教育的质量，并提高攻读STEM副学士或学士学位学生的录取率、保留率和毕业率。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。