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

该项目旨在通过改善本科计算机科学教育来服务于国家利益。 为此，它计划帮助教师生成高质量的多项选择题，以深入了解学生努力的领域。 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题的适当干扰（即不正确）答案来实现这一目标。一种经常使用的同伴指导形式开始时，教师向学生提出一个多项选择题，并要求他们提交一个个人的答案。然后，学生们在一组同龄人中讨论他们的答案，并提交一个小组共识答案，该答案可能与个人答案相同，也可能不同。最后，教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答案。由此产生的多项选择题将可用于测验和测试，并作为物理或虚拟课程中的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，因此该软件不会专门针对计算机科学，而是可以用于STEM领域的课程。该项目基于共同进化技术的新颖应用，作为理解学生之间互动的方法，并生成适应不断变化的学生群体的教学工件。这项工作的重点是如何开发新的共同进化技术，也涉及学生在创作过程中的同伴指导多项选择题。这种方法利用了通常在基于人类的进化算法中发现的技术。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干扰器创作的选项池中选择干扰器。该项目的第二阶段将提供一个软件工具，使学生能够选择干扰源从创作池的问题给他们的同龄人。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最具教育意义的干扰物，以及算法方法与人类选择的干扰物相比如何。该项目由NSF改善本科STEM教育计划：教育和人力资源支持。 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。该项目是在学生学习的轨道，通过该计划支持的创建，探索和实施有前途的做法和工具。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.