Using Program Dependence Graphs to Propagate Feedback to Students on Programming Assignments and Promote Responsive Teaching

在NSF改善本科STEM教育计划：教育和人力资源（IUSE：EHR）的支持下，该项目旨在通过使教师提高评估学生对计算机科学课程的理解并对学生作业提供反馈的能力来服务于国家利益。学生对计算机科学课程的兴趣在全国范围内迅速增加，给各部门和教师提供优质教育带来了压力。向学生提供有效的个性化反馈是学习过程的关键部分，但合格教师数量有限，学生人数众多，使得在学生与教师比例高的情况下提供此类反馈成为一项挑战。这个“学生学习跟踪探索和设计”层级项目将开发一个新的教学平台，通过自动向大量学生传播反馈来帮助计算机科学课程的教师。此外，新的教学平台旨在帮助教师根据学生提交的作业，了解学生在课程中的集体优势和劣势。该项目旨在影响罗切斯特理工学院每年1,000多名本科生。该项目开发的教学平台将分析学生提交的程序，以创建程序依赖图，该图结合了Java和Python程序的联合收割机控制和数据流。这些图将用于使用图对齐来聚类类似的学生提交，并使用子图挖掘来检测语义预期的代码模式。该平台的目标是通过提供分析，帮助教师了解单个学生和整个班级的表现，从而提高教学效率。它也被设计为与班级进一步讨论的建议途径。该项目的技术评估将研究该平台在综合和真实的任务中识别聚类和模式的能力。来自罗切斯特理工学院、几所邻近大学和当地高中的教师将参加培训研讨会，该平台将用于罗切斯特理工学院的入门课程。该项目的另一个目标是开发知识库，以便能够在新的作业中使用教学平台，并评估该平台对教员评分和教学风格的影响。该教学平台针对计算机科学课程的教师，并有可能影响任何学习计算机科学的学生。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。该奖项反映了NSF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。

With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE: EHR), this project aims to serve the national interest by enabling faculty to improve their ability to assess student understanding in computer science courses and give feedback on student assignments. Student interest in computer science courses is rapidly increasing nationwide, putting strain on departments and instructors to offer a quality education. Providing effective personalized feedback to students is a critical part of the learning process, but a limited number of qualified instructors and large student enrollments make providing such feedback a challenge when student to faculty ratios are high. This Engaged Student Learning track Exploration and Design tier project will develop a new teaching platform to assist instructors in computer science courses by automatically propagating feedback to a large body of students. In addition, the new teaching platform aims to help instructors understand collective strengths and weaknesses of students in their courses based on their assignment submissions. This project aims to affect over 1,000 undergraduate students each year at the Rochester Institute of Technology. The teaching platform developed by this project will analyze student program submissions to create program dependence graphs that combine control and data flows for Java and Python programs. The graphs will be used to cluster similar student submissions using graph alignment, and to detect semantic expected code patterns using subgraph mining. The goal of the platform is to promote improved teaching effectiveness by presenting analytics that will help instructors understand the performance of individual students and classes as a whole. It is also designed to suggest avenues for further discussion with the class. The technical evaluation of the project will study how well the platform identifies clusters and patterns in both synthetic and real assignments. Instructors from Rochester Institute of Technology, several neighboring universities, and local high schools will participate in training workshops, and the platform will be used in introductory courses at Rochester Institute of Technology. An additional goal of the project is to develop knowledge bases to enable the use of the teaching platform with new assignments, and to evaluate the impact of the platform on the instructors' grading and teaching style. The teaching platform targets instructors of computer science courses and has the potential to influence any student studying computer science. 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 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.