Metacognition and Misconceptions: Using Web-Based Writing Exercises in Gateway STEM Courses

该项目旨在通过提高通常在基础科学、技术、工程和数学（STEM）课程中挣扎的本科生的元认知技能和概念理解，为国家利益服务。众所周知，学生在电路分析和工程静力学等STEM课程中的困难往往源于学生无法准确识别他们的知识差距，并制定和遵循缩小这些差距的策略。这个过程是展示高级元认知技能的一种手段。本计划将在电路分析入门课程中采用新颖的以写作为中心的方法，以帮助学生建立心智模型，克服误解，并提高元认知技能。电路分析入门课程将要求学生运用物理世界中抽象现象的精确心智模型。学生们经常带着对抽象现象错误的思维模式进入这类课程。因此，概念上的改变对于掌握内容是必要的。该项目将加深学生对如何设计要求学生思考常见误解的问题的简短回答写作练习的理解。促进深度反思，提供即时和个性化的反馈，写作练习将评估他们的能力，使概念的变化和元认知技能的发展。事实上，反馈是即时的和个性化的，这是特别值得注意的，因为反馈被认为是最有效的，当它是及时的，并针对特定学生的精确解释。为了实现即时和个性化的反馈系统，写作练习将作为基于网络的应用程序实施，利用自然语言处理（NLP）的最新进展。为了确保该项目开发的软件广泛适用，来自全国多个学科的教师将使用研究团队开发的基于网络的模板，在他们的基础STEM课程中实施基于概念的写作练习。除了有助于更深入地了解如何在STEM基础课程中最好地使用简答题写作，以及提高这些课程的保留率外，该项目还将培训本科生和研究生使用人工智能算法实现的软件系统。NSF 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。通过参与学生学习轨道，该计划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。

This project aims to serve the national interest by enhancing the metacognitive skill and conceptual understanding of undergraduate students who typically struggle in foundational Science, Technology, Engineering, and Mathematics (STEM) courses. It is well known that student difficulties in STEM courses such as electric circuit analysis and engineering statics often arise from an inability of the student to accurately identify their knowledge gaps and to develop and follow strategies to close these gaps. This process is a means of exhibiting advanced metacognitive skill. The project will implement a novel writing-centric approach in an introductory circuit analysis undergraduate course to help students build mental models, overcome misconceptions, and enhance metacognitive skill. The introductory circuit analysis course will require students to utilize accurate mental models of abstract phenomena in the physical world. Students often enter such courses with faulty mental models of abstract phenomena. As such, conceptual change is necessary for content mastery.The project will develop a deeper understanding of how to design short-answer writing exercises that require the student to consider problems in which misconceptions are common. Promoting deep reflection and providing instantaneous and personalized feedback, the writing exercises will be evaluated for their ability to enable both conceptual change and metacognitive skill development. The fact that the feedback is to be both immediate and individualized is particularly notable as feedback is known to be most effective when it is timely and targets the precise interpretation of a given student. To achieve an instantaneous and personalized feedback system that works at-scale, the writing exercises will be implemented as web-based applications that leverage recent advances in Natural Language Processing (NLP). To ensure the software developed in this project is broadly applicable, instructors from multiple disciplines across the country will use the web-based templates developed by the research team to implement conceptual-based writing exercises in their foundational STEM courses. In addition to contributing to a deeper understanding of how to best employ short answer writing in STEM foundational courses as well as increasing retention in such courses, this project will train undergraduate and graduate students in implementing software systems made possible with algorithms employing artificial intelligence. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, 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.