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

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.