The Tutor Engagement Assistant (TEA): Promoting High-Quality TA-Student Interactions

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This project aims to serve the national interest by developing a new software tool to support teaching assistant (TA)-student interactions. Increasing class sizes in computing have led to a greater reliance on undergraduate TAs to support student learning. Many TAs are often only slightly more experienced than the students they help. Providing help to a learner is a complex cognitive process that requires: 1) Understanding the problem, 2) Debugging the learner's incorrect solution, 3) Identifying conceptual gaps, and 4) Determining how to best fill the conceptual gaps. The goal of this project is the development of the Tutor Engagement Assistant (TEA) system. The TEA system will help undergraduate TAs to quickly debug student code and construct personalized problem sets to structure their interactions with students. This project hopes to lead to more efficient and educationally effective TA help in computing courses nationwide, and should contribute to research in the learning sciences, HCI, and computing education. This work is based on two theories: social constructionism and cognitive load. It draws on the ICAP framework that links cognitive engagement with active learning outcomes. The project will explore the impact of TEA on time on task, type of help, cognitive load, learner engagement, self-efficacy, attitude towards help, help-seeking behavior, and learning. The research plan consists of three phases: 1) baseline data collection, 2) formative evaluation, and 3) TEA deployment. The project will lead to 1) a detailed understanding of tutor-student interactions through observation studies, 2) new approaches to help tutors quickly understand student errors via a guided walkthrough of automatically generated corrections, 3) techniques for identifying and curating personalized problem sets for students, and 4) an evaluation of the impact of personalized problem sets on the quality of tutor-student interactions and student learning. 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.