CRII: CHS: Automatically Praising Learning Process to Promote the Growth Mindset in Computer Science

我们迫切需要培养大量的计算机科学家来满足我国经济的需求，但是许多学生在编程入门课程上遇到了困难。最近的研究表明，这些课程通常会促进固定心态，或者认为编程能力是一种天生的特质。心理学研究表明，拥有固定心态的学生将错误视为能力低下的表现，在面对挑战时表现不佳。相比之下，拥有成长型思维的学生认为编程能力是可塑的，在受到挑战时可以表现出色。该项目旨在设计、构建和评估编程工具，通过自动检测和表扬学生在编写代码时的良好学习行为，帮助学生培养成长心态。这项研究将为计算机科学领域的成长心态提供科学知识，并为学习编程的过程提供见解。该项目团队将为他们所在大学的数百名学生部署这些工具，并在网上免费发布，供任何学生或教师使用。如果成功，这种干预有可能改善成功完成编程课程的学生的经验、技能和多样性，并继续参与STEM领域的就业和研究。该项目旨在开发一种新的成长心态干预，通过使用人工智能技术来利用编程环境，实时自动检测和赞扬良好的学习过程。编程环境为跟踪和理解学生的学习行为提供了独特的机会，并为自动表扬好的实践提供了可伸缩的环境。通过揭露和赞扬学习过程，这种干预将教会学生将他们的成功和失败归因于可塑的学习过程，而不是天生的计算机科学天赋。本研究将分两个阶段进行。首先，项目团队将开发启发式方法，使用行为日志数据检测良好的学习过程，利用计算机科学教育文献，研究固定思维和成长型思维学生的行为，以确定良好的学习过程。其次，团队将迭代地设计和构建一个编程环境扩展，该扩展使用经过验证的启发式来自动检测和赞扬良好的学习过程，并通过对大学生进行为期十周的受控研究来评估这种干预。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

There is a pressing need to train large numbers of computer scientists to meet the demands of our nation's economy, but many students struggle in introductory programming courses. Recent studies show that these courses often promote the fixed mindset, or the belief that programming aptitude is an inborn trait. Psychology research shows that students with the fixed mindset view mistakes as indications of low ability and perform poorly in the face of challenge. In contrast, students with the growth mindset believe that programming aptitude is malleable and excel when challenged. This project aims to design, build, and evaluate programming tools that help students develop the growth mindset by automatically detecting and praising good learning behaviors as students write code. This research will contribute scientific knowledge about the growth mindset in the domain of computer science and provide insights about the process of learning to program. The project team will deploy the tools to hundreds of students at their own university and release them for free online for any student or teacher to use. If successful, this intervention has the potential to improve the experiences, skills, and diversity of students who successfully complete programming courses and go on to participate in employment and research in STEM fields.This project aims to develop a new growth mindset intervention that leverages the programming environment by using artificial intelligence techniques to automatically detect and praise good learning processes in real time. Programming environments provide a unique opportunity to track and understand student learning behaviors, and offer a scalable environment for praising good practices automatically. By exposing and praising the learning process, this intervention will teach students to attribute their successes and failures to malleable learning processes, rather than an innate aptitude for computer science. This research will be conducted in two phases. First, the project team will develop heuristics that detect good learning processes using behavioral log data, leveraging the computer science education literature and studying the behavior of fixed and growth mindset students to identify good processes. Second, the team will iteratively design and build a programming environment extension that uses the validated heuristics to automatically detect and praise good learning process, and evaluate this intervention through a controlled ten-week study with university 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.