Using Coevolutionary Algorithms to Identify Distractor Answers for Multiple Choice Questions Used for Peer Instruction

使用共同进化算法来识别用于同伴教学的多项选择问题的干扰答案

• 批准号: 2012967
• 负责人: Alessio Gaspar
• 金额: $ 37.7万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2012967&HistoricalAwards=false
• 关键词: Using; Coevolutionary; Algorithms; Identify; Distractor

This project aims to serve the national interest by improving undergraduate computer science education. To do so, it plans to assist instructors in generating high quality, multiple choice questions that provide insights into the areas where students struggle. The project will accomplish this goal by using coevolutionary algorithms to identify appropriate distractor (i.e. incorrect) answers for multiple-choice questions used for peer instruction. A frequently used form of peer instruction starts when the instructor presents students with a multiple-choice question and asks them to submit an individual answer. The students then discuss their answers in a group of peers and submit a group consensus answer, which may or not be the same as the individual answers. Finally, the instructor discusses the solution and the distractors. The project will develop software to algorithmically select distractor answers that best reveal student understandings and misunderstandings. The resulting multiple-choice questions will be usable in quizzes and tests, and as questions for peer instruction activities in physical or virtual courses. The system will also provide instructors with data analytics and visualizations, thus helping them better understand how students are performing and where they are struggling. Finally, because the software can use open-ended answers generated by students or faculty to any question, the software will not be specific to computer science, but could be used for courses across STEM fields.This project is based on the novel application of coevolutionary techniques as an approach for understanding both student-student interactions and to generate teaching artifacts that adapt to changing student populations. The work focuses on ways to develop new coevolutionary techniques that also involve students in the process of authoring peer instruction multiple-choice questions. This approach leverages techniques generally found in Human-Based Evolutionary Algorithms. Such techniques are crucial to enabling the artificial evolution of semantically complex teaching artifacts, such as multiple-choice questions, that could not be automatically generated otherwise. The first stage of the project will apply various coevolutionary algorithms to select distractors from a pool of instructor-authored options. The second stage of the project will provide a software tool that will allow students to select distractors from the instructor-authored pool for questions given to their peers. The third stage of the project will allow students to author their own distractors. The project will study which algorithms are able to generate the most pedagogically sound distractors and how the algorithmic approach compares to human-selected distractors. This project is supported by the NSF Improving Undergraduate STEM Education Program: Education and Human Resources. The IUSE: EHR program supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the Engaged Student Learning track, through which 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.

本项目旨在通过改善本科计算机科学教育服务于国家利益。为此，它计划协助教师制作高质量的多项选择题，以深入了解学生的困难所在。该项目将通过使用协同进化算法来识别用于同伴指导的多项选择题的适当干扰（即错误）答案来实现这一目标。一种常用的同伴指导形式是，教师给学生一个选择题，让他们提交一个单独的答案。然后，学生们在一组同龄人中讨论他们的答案，并提交一个群体共识的答案，这个答案可能与个人的答案相同，也可能与个人的答案不同。最后，讲师讨论了解决方案和干扰因素。该项目将开发软件，通过算法选择最能揭示学生理解和误解的干扰答案。由此产生的多项选择题将用于小测验和测试，并作为物理或虚拟课程中同伴指导活动的问题。该系统还将为教师提供数据分析和可视化，从而帮助他们更好地了解学生的表现以及他们在哪里挣扎。最后，由于该软件可以使用学生或教师对任何问题生成的开放式答案，因此该软件不会特定于计算机科学，而是可以用于STEM领域的课程。这个项目是基于共同进化技术的新应用，作为一种理解学生之间互动的方法，并产生适应不断变化的学生群体的教学人工制品。这项工作的重点是开发新的共同进化技术的方法，这些技术也使学生参与编写同伴指导的多项选择题的过程。这种方法利用了基于人类的进化算法中常用的技术。这些技术对于实现语义复杂的教学工件（如多项选择题）的人工进化至关重要，否则这些工件无法自动生成。该项目的第一阶段将应用各种协同进化算法，从一堆由教师编写的选项中选择干扰因素。该项目的第二阶段将提供一个软件工具，让学生从教师编写的问题池中选择干扰因素，然后交给他们的同龄人。该项目的第三阶段将允许学生编写自己的干扰物。该项目将研究哪种算法能够产生最适合教学的干扰物，以及算法方法与人为选择的干扰物的比较。本项目由美国国家科学基金改进本科STEM教育计划：教育与人力资源资助。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。该项目处于“参与学生学习”轨道，通过该轨道，该项目支持有前途的实践和工具的创建、探索和实施。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。