Automated Feedback in Undergraduate Computing Theory Courses

本科计算机理论课程的自动反馈

• 批准号: 1819546
• 负责人: Ivona Bezakova
• 金额: $ 29.94万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1819546&HistoricalAwards=false
• 关键词: Automated; Feedback; Undergraduate; Computing; Theory

Computing theory poses and answers questions such as "Which problems are efficiently computable and which are not?" Answering such questions is important for any computer scientist and for any kind of software development. For example, it is better to determine if a problem is computable before spending a lot of time trying to write a program to solve it. Unfortunately, many students struggle with computing theory, because it is more abstract and mathematical than other computer science topics. As in any other knowledge area, students need to practice to get better at computing theory. A problem is that feedback on their work is not immediate and, while students wait for feedback, they stop interacting with the material. They may have to wait for days, since grading an assignment often takes a lot of instructor time and the instructors may have many assignments to grade. This project will increase the speed and, potentially, the quality of feedback to computing theory students by developing an automated feedback tool. The feedback will tell students whether a solution is correct or not, a convincing reason why an incorrect solution is incorrect, and provide information about the quality of a solution. Students will be able to use this immediate feedback to improve their solutions, get more practice, and increase their understanding of the material. In addition to building the feedback tool, this project aims to conduct research on the feedback tool's effectiveness. This project has the potential to contribute to the education of a strong computing workforce and to support development of students' independent learning skills. Although understanding computing theory concepts is very important, it is challenging. Typically, as a first step, students in computing theory classes learn about various models of computation. To understand more complex computational issues, students need to fully comprehend the possibilities and limitations of these models. JFLAP (Java Formal Languages and Automata Package) is a widespread tool that provides a way for students to interact with these concepts. However, like other interactive tools in this area, it does not provide detailed feedback on student solutions. This project will build a feedback and grading tool on top of JFLAP, to increase the likelihood that the feedback tool will have broad applicability. To accomplish this goal, the project will develop and evaluate the tool in the context of three research areas: (1) Computer Science Education: Do students who use the tool understand theoretical computer science concepts better than students who do not use the tool? (2) Theoretical Computer Science: How can software generate a convincing reason for why a student solution is incorrect? and (3) Artificial Intelligence: How can feedback be given about the quality of a student's solution? The project's research and software development activities will involve ten undergraduate students, who will be recruited with emphasis on including women and deaf/hard-of-hearing students. Thus, the project will directly contribute to these students' scientific and professional development. Project outcomes will be disseminated at scientific conferences and workshops, as well as at the University's innovation fair, which is attended by 35,000 visitors, including middle and high school students. Developing the feedback tool and completing research on its effectiveness has the potential to improve instruction and learning of computing theory.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.

计算理论提出并回答了诸如“哪些问题是有效可计算的，哪些不是？“解决这些问题对任何计算机科学家和任何类型的软件开发都很重要。 例如，在花大量时间编写程序解决问题之前，最好先确定问题是否可计算。不幸的是，许多学生在计算理论方面遇到了困难，因为它比其他计算机科学主题更抽象和数学化。与其他任何知识领域一样，学生需要练习才能更好地掌握计算理论。一个问题是，对他们的工作的反馈不是即时的，当学生等待反馈时，他们停止与材料互动。他们可能不得不等待几天，因为评分作业通常需要教师大量的时间，而且教师可能有许多作业要评分。这个项目将通过开发一个自动反馈工具来提高速度，并可能提高对计算理论学生的反馈质量。 反馈将告诉学生一个解决方案是否正确，一个令人信服的理由，为什么一个不正确的解决方案是不正确的，并提供有关解决方案的质量信息。学生将能够使用这种即时反馈来改进他们的解决方案，获得更多的练习，并增加他们对材料的理解。除了建立反馈工具外，本项目还旨在对反馈工具的有效性进行研究。 该项目有可能有助于培养一支强大的计算机队伍，并支持学生独立学习技能的发展。 虽然理解计算理论概念非常重要，但它具有挑战性。通常，作为第一步，计算理论课的学生学习各种计算模型。为了理解更复杂的计算问题，学生需要充分理解这些模型的可能性和局限性。JFLAP（Java Formal Languages and Automata Package）是一个广泛使用的工具，它为学生提供了一种与这些概念进行交互的方法。但是，与该领域的其他交互式工具一样，它不提供关于学生解决方案的详细反馈。本项目将在JFLAP的基础上构建一个反馈和评分工具，以增加反馈工具具有广泛适用性的可能性。为了实现这一目标，该项目将在三个研究领域的背景下开发和评估该工具：（1）计算机科学教育：使用该工具的学生是否比不使用该工具的学生更好地理解理论计算机科学概念？(2)理论计算机科学：软件如何生成一个令人信服的理由，为什么学生的解决方案是不正确的？（3）人工智能：如何反馈学生的解决方案的质量？ 该项目的研究和软件开发活动将涉及十名本科生，招募时将重点关注女性和聋哑/重听学生。 因此，该项目将直接有助于这些学生的科学和专业发展。 项目成果将在科学会议和讲习班以及大学的创新博览会上传播，该博览会有35 000名参观者参加，其中包括初中和高中学生。开发反馈工具并完成对其有效性的研究，有可能改善计算理论的教学和学习。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。

项目成果

The Complexity of $(P_k,P_{\ell})$-Arrowing

$(P_k,P_{ell})$-箭头的复杂性

• 发表时间: 2023
• 期刊: Fundamentals of Computation Theory (FCT
• 作者: Hassan, Zohair Raza;Hemaspaandra, Edith;Radziszowski, Stanislaw
• 通讯作者: Radziszowski, Stanislaw

Insight into Voting Problem Complexity Using Randomized Classes

使用随机类洞察投票问题的复杂性

• DOI: 10.24963/ijcai.2022/42
• 发表时间: 2022
• 期刊: International Joint Conference on Artificial Intelligence (IJCAI
• 作者: Fitzsimmons, Zack;Hemaspaandra, Edith
• 通讯作者: Hemaspaandra, Edith

Enumerating Nontrivial Knot Mosaics with SAT (Student Abstract)

用 SAT 枚举非平凡的结马赛克（学生摘要）

• DOI: 10.1609/aaai.v36i11.21645
• 发表时间: 2022
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Miller, Hannah

Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models

测试图形模型的下限：着色和反铁磁伊弦模型

• 发表时间: 2020
• 期刊: Journal of machine learning research
• 影响因子: 6
• 作者: Bezakova, Ivona;Blanca, Antonio;Chen, Zongchen;Stefankovic, Daniel;Vigoda, Eric
• 通讯作者: Vigoda, Eric

Using Weighted Matching to Solve 2-Approval/Veto Control and Bribery

使用加权匹配解决2-批准/否决控制和贿赂问题

• 发表时间: 2023
• 期刊: European Conference on Artificial Intelligence (ECAI
• 作者: Fitzsimmons, Zack;Hemaspaandra, Edith
• 通讯作者: Hemaspaandra, Edith