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: 2038406
• 负责人: Rudolf Wiegand
• 金额: $ 22.29万
• 依托单位: Winthrop University
• 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: 2020-08-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038406&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.

该项目旨在通过改善本科计算机科学教育来服务国家利益。 为此，它计划协助教师提出高质量的多项选择题，以深入了解学生遇到的困难领域。 该项目将通过使用共同进化算法来识别用于同伴指导的多项选择问题的适当干扰（即不正确）答案来实现这一目标。当教师向学生提出多项选择题并要求他们提交个人答案时，一种常用的同伴指导形式就开始了。然后，学生在一群同学中讨论他们的答案，并提交一个小组共识答案，该答案可能与个人答案相同或不同。最后，讲师讨论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性答案。由此产生的多项选择题将可用于测验和测试，以及作为实体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的困难所在。 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，因此该软件不会专门针对计算机科学，而是可以用于跨 STEM 领域的课程。该项目基于共同进化的新颖应用技术作为理解学生与学生互动并生成适应不断变化的学生群体的教学制品的方法。这项工作的重点是开发新的共同进化技术的方法，这些技术还让学生参与编写同伴指导多项选择题的过程。这种方法利用了基于人类的进化算法中常见的技术。这些技术对于实现语义复杂的教学工件（例如多项选择题）的人工进化至关重要，否则这些技术无法自动生成。该项目的第一阶段将应用各种共同进化算法从教师编写的选项池中选择干扰因素。该项目的第二阶段将提供一个软件工具，允许学生从教师编写的库中选择干扰项，以解决向同龄人提出的问题。该项目的第三阶段将允许学生创作自己的干扰物。该项目将研究哪些算法能够生成在教学上最合理的干扰项，以及该算法方法与人类选择的干扰项相比如何。该项目得到了 NSF 改善本科生 STEM 教育计划：教育和人力资源的支持。 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。该项目属于参与学生学习轨道，通过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 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的评估进行评估，被认为值得支持。影响审查标准。