Using Large Language Models to Assess Tutors' Performance in Reacting to Students Making Math Errors
Abstract
Research suggests that tutors should adopt a strategic approach when addressing math errors made by low-efficacy students. Rather than drawing direct attention to the error, tutors should guide the students to identify and correct their mistakes on their own.
Questions this source addresses
- How can large language models be used to evaluate the quality of a tutor's response to a student's math error?
- How should tutors respond when a low-efficacy student makes a math error, according to this research?
- What role does automated LLM-based assessment play in scaling effective tutoring practices?
- Why does guiding students to self-correct their mistakes work better than direct error correction for students with low self-efficacy?