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

使用程序依赖图向学生传播有关编程作业的反馈并促进响应式教学

• 批准号: 1915404
• 负责人: Carlos Rivero
• 金额: $ 29.87万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915404&HistoricalAwards=false
• 关键词: Using; Program; Dependence; Graphs; Propagate

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.

在NSF改善本科生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能够提高他们评估学生对计算机科学课程的理解并对学生作业给予反馈的能力来服务于国家利益。在全国范围内，学生对计算机科学课程的兴趣正在迅速增加，这给各部门和教师带来了提供优质教育的压力。向学生提供有效的个性化反馈是学习过程中的关键部分，但合格教师数量有限，学生入学人数众多，在师生比例较高的情况下，提供此类反馈是一项挑战。这个参与的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。该项目旨在每年影响罗切斯特理工学院1000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，将Java和Python程序的控制流和数据流结合在一起。这些图将被用来使用图对齐来对相似的学生提交的报告进行聚类，并使用子图挖掘来检测语义预期代码模式。该平台的目标是通过提供分析来帮助教师了解个别学生和整个班级的表现，从而提高教学效率。它还被设计用来建议与班级进一步讨论的途径。该项目的技术评价将研究该平台在综合任务和实际任务中确定集群和模式的情况。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讲习班，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，以便能够将教学平台用于新作业，并评估该平台对教师评分和教学风格的影响。该教学平台面向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，以提高所有学生的STEM教育的有效性。通过参与的学生学习路径，该计划支持有前景的实践和工具的创建、探索和实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Learning to Recognize Semantically Similar Program Statements in Introductory Programming Assignments

学习识别入门编程作业中语义相似的程序语句

• DOI: 10.1145/3408877.3439599
• 发表时间: 2021
• 期刊: The 52nd ACM Technical Symposium on Computer Science Education
• 作者: Jawalkar, Mayur Sunil;Hosseini, Hadi;Rivero, Carlos R.
• 通讯作者: Rivero, Carlos R.

Customizing Feedback for Introductory Programming Courses Using Semantic Clusters

使用语义集群定制入门编程课程的反馈

• DOI: 10.1007/978-3-030-80421-3_30
• 发表时间: 2021
• 期刊: Lecture notes in computer science
• 作者: Marin, Victor J.;Hosseini, Hadi;Rivero, Carlos R.
• 通讯作者: Rivero, Carlos R.

Towards summarizing program statements in source code search

在源代码搜索中总结程序语句

• DOI: 10.1145/3341105.3374055
• 发表时间: 2020
• 期刊: Proceedings of the 35th Annual ACM Symposium on Applied Computing
• 作者: Marin, Victor J.;Bansal, Iti;Rivero, Carlos R.
• 通讯作者: Rivero, Carlos R.

Mind the Gap: The Illusion of Skill Acquisition in Computational Thinking

注意差距：计算思维中技能习得的幻觉

• DOI: 10.1145/3545945.3569749
• 发表时间: 2023
• 期刊: SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education
• 作者: Bao, Yeting;Hosseini, Hadi
• 通讯作者: Hosseini, Hadi

Improving Program Matching to Automatically Repair Introductory Programs

改进节目匹配，自动修复入门节目

• 发表时间: 2022
• 期刊: Springer
• 作者: Contractor, Maheen Riaz;Rivero, Carlos R
• 通讯作者: Rivero, Carlos R