Practical Methods for Semi-automated Peer Grading in a Classroom Setting

Abstract

Peer grading, in which students grade each other's work, can provide an educational opportunity for students and reduce grading effort for instructors. A variety of methods have been proposed for synthesizing peer-assigned grades into accurate submission grades. However, when the assumptions behind these methods are not met, they may underperform a simple baseline of averaging the peer grades. We introduce SABTXT, which improves over previous work through two mechanisms. First, SABTXT uses a limited amount of historical instructor ground truth to model and correct for each peer's grading bias. Secondly, SABTXT models the thoroughness of a peer review based on its textual content, and puts more weight on the more thorough peer reviews when computing submission grades. In our experiments with over ten thousand peer reviews collected over four courses, we show that SABTXT outperforms existing approaches on our collected data, and achieves a mean squared error that is 6% lower than the strongest baseline on average.