John Mitchell

Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems

In this paper, we evaluate the most effective error message types through a large-scale randomized controlled trial conducted in an open-access, online introductory computer science course with 8,762 students from 146 countries. We assess existing error message enhancement strategies, as well as two novel approaches of our own: (1) generating error messages using OpenAI's GPT in real time and (2) constructing error messages that incorporate the course discussion forum. By examining students' direct responses to error messages, and their behavior throughout the course, we quantitatively evaluate the immediate and longer term efficacy of different error message types. We find that students using GPT generated error messages repeat an error 23.1% less often in the subsequent attempt, and resolve an error in 34.8% fewer additional attempts, compared to students using standard error messages. We also perform an analysis across various demographics to understand any disparities in the impact of different error message types. Our results find no significant difference in the effectiveness of GPT generated error messages for students from varying socioeconomic and demographic backgrounds. Our findings underscore GPT generated error messages as the most helpful error message type, especially as a universally effective intervention across demographics.