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

该项目旨在通过改进本科计算机科学教育来服务于国家利益。为了做到这一点，它计划帮助教师生成高质量的多项选择题，为学生提供对困难领域的洞察。该项目将通过使用共同进化算法来识别用于同伴教学的多项选择题的适当干扰(即不正确)答案，从而实现这一目标。一种常用的同伴教学形式始于教师向学生提出一个多项选择题，并要求他们提交一个单独的答案。然后，学生们在一组同伴中讨论他们的答案，并提交一组共识答案，该答案可能与个人答案相同，也可能不相同。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分心答案。由此产生的多项选择题将在测验和测试中使用，并作为物理或虚拟课程中的同行教学活动的问题。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努力。最后，由于该软件可以使用学生或教职员工对任何问题的开放式答案，该软件将不会特定于计算机科学，但可以用于STEM领域的课程。该项目基于共同进化技术的新应用，作为一种理解学生与学生互动的方法，并生成适应不断变化的学生群体的教学人工制品。这项工作的重点是如何开发新的共同进化技术，让学生参与创作同伴教学多项选择题的过程。这种方法利用了基于人类的进化算法中常见的技术。这些技术对于实现人工进化语义复杂的教学人工制品至关重要，例如多项选择题，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，从教师创作的选项池中选择分心因素。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池中选择分心因素，以便向同龄人提出问题。该项目的第三阶段将允许学生创作自己的干扰项。该项目将研究哪些算法能够产生最具教学意义的声音干扰物，以及该算法方法与人类选择的干扰物相比如何。该项目得到了美国国家科学基金会改善本科生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。该项目处于参与学生学习轨道中，通过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。

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.