Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems
This addresses the scalability issue in education by enabling high-quality feedback for students in structured domains, representing a significant advance over previous methods.
The paper tackles the problem of providing automated feedback on open-ended assignments in structured domains like programming and graphics, achieving near human-level accuracy with a 50% improvement in block-based coding and major gains in other domains, while doubling grading accuracy and halving time in a classroom experiment.
Access to high-quality education at scale is limited by the difficulty of providing student feedback on open-ended assignments in structured domains like computer programming, graphics, and short response questions. This problem has proven to be exceptionally difficult: for humans, it requires large amounts of manual work, and for computers, until recently, achieving anything near human-level accuracy has been unattainable. In this paper, we present generative grading: a novel computational approach for providing feedback at scale that is capable of accurately grading student work and providing nuanced, interpretable feedback. Our approach uses generative descriptions of student cognition, written as probabilistic programs, to synthesise millions of labelled example solutions to a problem; we then learn to infer feedback for real student solutions based on this cognitive model. We apply our methods to three settings. In block-based coding, we achieve a 50% improvement upon the previous best results for feedback, achieving super-human accuracy. In two other widely different domains -- graphical tasks and short text answers -- we achieve major improvement over the previous state of the art by about 4x and 1.5x respectively, approaching human accuracy. In a real classroom, we ran an experiment where we used our system to augment human graders, yielding doubled grading accuracy while halving grading time.