This project aims to serve the national interest by investigating the integration of existing submission and feedback delivery systems with teaching assistant (TA) training. Specifically, this work will explore the use of natural language processing techniques to provide feedback to TAs as they provide written feedback about studentsâ€™ work in introductory computer science courses. The project will investigate different ways to train TAs with direct instruction on effective feedback practices and culturally responsive teaching methods. This work has the potential to address the growing need for TAs to provide high-quality feedback, as computing class sizes are growing faster than faculty hiring. Low-quality feedback has been shown to negatively impact student success and willingness to continue in the discipline. Thus, improving the quality of TA feedback in computing courses could help more students to successfully enter the computing workforce.
The integrated submission and TA training system will consist of a server that communicates with an Integrated Development Environment via custom-built plug-ins to facilitate in-class communication between students, instructors, and TAs. Human-computer-interaction techniques will be used to design unobtrusive dashboards that will enable instructors to efficiently monitor TA feedback. The projectâ€™s research plan will investigate how the new training system changes TA perspectives, alters feedback practices, and affects undergraduate learning. The project will also investigate strategies for data visualization that enable efficient monitoring of TA performance. The deliverables will include a series of TA training modules, a dataset of TA feedback samples together with student ratings, a natural language processing classifier that recognizes helpful or unhelpful feedback, and a dashboard that shows TA feedback performance to instructors. 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.