An Online Tool to Support Productive Struggles and Improve the Self-Efficacy of Undergraduate Students in Introductory Computer Science Courses.

该项目旨在通过开发一个在线平台来服务于国家利益，该平台将有助于提高计算机入门课程的保留率。美国劳工统计局预测，未来几年对计算机工作的需求将继续增长。然而，计算机科学毕业生的预计数量并不能满足预期的需求。其中一个原因是计算机专业的高辍学率。计算机科学学位与发展技术技能有关，这些技能可能是在学生解决涉及挑战性任务的问题时教授的。解决这些具有挑战性的任务有助于学生学习，但往往导致与材料的斗争。虽然这样的斗争通常与自我效能的降低有关，但这些斗争可以成为促进学习和自我效能的工具，从而成为富有成效的斗争。将课堂挑战转化为富有成效的努力，作为学习的一种手段，可能有助于提高计算机科学入门课程的记忆力。这个在线平台名为PULSE (Productive strUggle for deveLoping Self Efficacy)，旨在促进生产性斗争，提高自我效能，增强技能发展。PULSE将利用课堂外的长篇计算机编程作业。该平台将使用机器学习驱动的方法来识别学生在解决编程作业时面临的障碍。这将使教师能够在学生遇到困难时进行干预，让学生从困难中学习，并在提交截止日期之前改进他们的工作。该项目应该通过产生关于学生在课堂外解决编码问题的斗争和编程活动模式的新知识来推进计算机科学教育。所提出的研究的见解可能提供减少大学计算机入门课程学生辍学率的方法。PULSE将作为开源软件发布，允许其他机构在其编程课程中部署它。NSF IUSE: EHR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。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。该项目还得到了NSF IUSE:HSI计划的支持，该计划的目标是提高STEM本科教育的质量，提高攻读STEM副学士学位或学士学位的学生的招聘、保留和毕业率。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。

This project aims to serve the national interest by developing an online platform that will help to improve retention in introductory computing courses. The US Bureau of Labor Statistics predicts that the demand for computing jobs will continue to grow in the coming years. However, the projected number of computer science graduates does not meet the predicted demand. One of the reasons for this is the high dropout rates of computing majors. A computer science degree is tied to developing technical skills, which may be taught while students solve problems involving challenging tasks. Solving such challenging tasks helps students to learn but often results in struggles with the material. While such struggles are usually linked with diminishing self-efficacy, these struggles can become instruments to promote learning and self-efficacy, thus becoming productive struggles. Converting classroom challenges to productive struggles as a means for learning may help to improve retention in introductory computer science classes. The online platform, called PULSE (Productive strUggle for deveLoping Self Efficacy), is intended to foster productive struggles, improve self-efficacy, and enhance skill development. PULSE will make use of long-form computer programming assignments outside the classroom. The platform will use machine learning-driven approaches to identify the obstacles students face while solving a programming assignment. This will enable instructors to intervene when students struggle, allowing students to learn from the struggles and improve their work before the submission deadline. The project should advance computer science education by generating new knowledge about student struggles and programming activity patterns in solving coding problems outside the classroom. The insights of the proposed research may provide ways to reduce student dropout rates in introductory computing classes in college. PULSE will be released as open-source software allowing other institutions to deploy it in their programming courses. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the 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.