CAREER: Improving Undergraduate Computing Education by Scaffolding Write Code Problems with Automatically Generated, Personalized, and Adaptive Parsons Problems

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。该项目旨在通过创建和测试工具来服务于国家利益，以提高学生在入门编程课程中的成功率。编程课程通常要求学生通过编写完整的程序来练习。然而，这对于刚接触编程的学生来说可能非常困难。在编写代码时挣扎可能会增加学生不及格或离开该领域的风险。来自计算领域代表性不足的群体的学生在入门编程课程中面临的风险更大，因为他们的经验往往更少。这个项目将帮助学生成功地编写完整的程序，为他们提供一个混合代码问题的形式提示。在这些问题中，学生将混淆的代码块按正确的顺序放置。先前的研究表明，学生解决这些问题的速度比编写完整的程序更快，但仍然学到了类似的量。这个项目将从学生编写的代码中自动生成两种类型的混合代码问题：（1）基于最常见的学生编写的解决方案的问题，以及（2）基于学生的不正确解决方案的个性化问题。这个项目的目标是调查两种类型的混合代码的问题，也被称为帕森斯问题，相对于学生的看法，学习收益，效率，问题完成率，自我效能和保留的影响。方法包括有声思维学科内的研究，学科间的研究，和调查。效果将在多个教育环境中进行测试：一个本科Python课程，一个在线Python课程，帮助在计算中代表性不足的群体的中学生取得成功的计划，一个本科Java课程和一个高中水平的Java课程。这些工具和生成的问题将被集成到Runestone中，Runestone是一个开源的电子书平台，已经被数百个机构和数万名学生使用。研究成果将通过博客、会议论文和计算机教师研讨会传播。这个项目将增加新的知识，努力为那些在编写完整程序时苦苦挣扎的学生提供学习的支架。在这个项目中使用的技术，如果成功的话，可以用于其他STEM科目，以提高成功率和保留率。该职业奖由NSF的IUSE：EHR计划支持，该计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This project aims to serve the national interest by creating and testing tools to improve student success in introductory programming courses. Programming courses usually require students to practice by writing complete programs. However, this can be very difficult for students who are new to programming. Struggling while writing code can increase the risk that students will fail a course or leave the field. Students from groups that are underrepresented in computing are more at risk in introductory programming courses since they tend to have less prior experience. This project will help students succeed in writing complete programs by providing them with a hint in the form of a mixed-up code problem. In these problems, students place mixed-up blocks of code in the correct order. Prior research has shown that students solve these problems faster than writing complete programs and yet still learn a similar amount. This project will automatically generate two types of mixed-up code problems from student-written code: (1) a problem based on the most common student-written solution, and (2) a personalized problem based on a student's incorrect solution. The goals of this project are to investigate the effects of two types of mixed-up code problems, also called Parsons problems, with respect to student perceptions, learning gains, efficiency, problem completion rates, self-efficacy, and retention. Methods include think-aloud within-subjects studies, between subject studies, and surveys. Effects will be tested in multiple educational contexts: an undergraduate Python course, an online Python course, programs to help secondary students from groups underrepresented in computing succeed, an undergraduate Java course, and a high-school level Java course. The tools and generated problems will be integrated into Runestone, an open-source ebook platform that is already used by hundreds of institutions and tens of thousands of students. Research results will be disseminated via blogs, conference papers, and workshops for computing instructors. This project will add new knowledge in the effort to scaffold learning for students who are struggling while writing complete programs. Techniques used in this project, if successful, could be used in other STEM subjects to improve success and retention rates. This CAREER award is supported by NSF's IUSE:EHR Program which supports research and development projects to improve the effectiveness of STEM education for all students.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.