CAREER: Towards Intelligent Learning Environments that Support the Practice of Programming

技术在现代社会中发挥着重要作用，塑造了人们的社交，学习和工作方式。因此，编程技能在所有经济部门都有很高的需求。虽然大学计算机科学（CS）课程的入学人数正在迅速增长，但许多学生很难学习编程，并且该专业的保留率很低。学习编程需要掌握解决问题、系统调试和适应性规划等实践。然而，CS课程的入学人数不断增加，使得教师难以监控学生的实践并提供反馈。鉴于学生的编程过程不能从最终的解决方案中确定，教师很少能看到学生的编程实践。该项目利用一个独特的机会来观察和支持自动编程过程，旨在通过构建智能编程环境来评估和适应学生的动机和实践，从而促进对编程过程的科学理解。作为一个CAREER项目，它采用了综合教育和推广工作，发展所需的理论和技术基础，开发和传播智能学习环境，有效地支持programming.This CAREER项目的实践探索和解决在促进有效的编程实践中的核心挑战介绍CS课程。PI之前的工作表明，学生经常使用编程实践作为他们是否表现良好的信号。例如，许多学生认为计划和查找语法是能力低下的标志，尽管专家认为这些做法是编程的自然组成部分。此外，这些自我评估已被证明会影响自我效能，或学生对自己成功能力的信念。这些发现提出了两个核心挑战。首先，学生对编程的不准确期望可能导致他们避免有效的专家实践。其次，学生可能会发展低自我效能感，当他们从事这些做法，一个因素，可以影响课程的表现和决定在CS专业。该项目提出了一种通用的方法和一套技术来解决这些挑战，通过（1）开发行为模型，自动检测编程实践，以实现对学生动机和实践的大规模研究，以及（2）设计和评估干预措施，提供个性化的指导，以帮助学生发展动机和有效的实践。拟议的教育计划将通过以下方式扩大研究的影响：（1）针对CS教师的在线研讨会，重点关注学生的动机和实践，（2）针对计划成为CS教师的研究生的新课程，以及（3）为在计算机科学领域代表性不足的学生提供研究机会。该奖项反映了NSF的法定使命，并且通过使用基金会的评估被认为值得支持知识价值和更广泛的影响审查标准。

Technology plays an important role in modern society, shaping how people socialize, learn, and work. As a result, programming skills are in high demand across all sectors of the economy. While enrollments in university computer science (CS) courses are growing rapidly, many students struggle to learn programming and retention in the major is low. Learning to program requires mastering practices like problem-solving, systematic debugging, and adaptive planning. However, rising enrollments in CS classes make it difficult for instructors to monitor student practices and provide feedback. Given that a student’s programming process cannot be determined from the final solution, instructors rarely have visibility into students’ programming practices. This project leverages a unique opportunity to observe and support the programming process automatically and aims to advance scientific understanding of the programming process by building intelligent programming environments that assess and adapt to students’ motivations and practices. As a CAREER project, it employs integrated education and outreach efforts, to develop the theoretical and technical foundations needed to develop and disseminate intelligent learning environments that effectively support the practice of programming.This CAREER project explores and addresses core challenges in promoting effective programming practices in introductory CS courses. The PI’s prior work reveals that students often use programming practices as signals of whether they are performing well. For example, many students believe that planning and looking up syntax are signs of low ability, even though experts consider these practices a natural part of programming. Furthermore, these self-assessments have been shown to impact self-efficacy, or a student’s belief in their ability to succeed. These findings introduce two core challenges. First, students’ inaccurate expectations about programming may lead them to avoid effective expert practices. Second, students may develop low self-efficacy when they engage in these practices, a factor that can impact course performance and the decision to major in CS. This project proposes a general approach and set of techniques to address these challenges by (1) developing behavioral models that automatically detect programming practices to enable a large-scale study of student motivations and practices, and (2) designing and evaluating interventions that provide personalized guidance to help students develop motivation and effective practices. The proposed education plan will expand the impact of the research through (1) online workshops for CS instructors that focus on student motivation and practices, (2) new courses for graduate students who plan to become CS instructors, and (3) research opportunities for students who are underrepresented in CS.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.