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Peer grading is the process of students reviewing each others' work, such ashomework submissions, and has lately become a popular mechanism used in massiveopen online courses (MOOCs). Intrigued by this idea, we used it in a course onalgorithms and data structures at the University of Hamburg. Throughout thewhole semester, students repeatedly handed in submissions to exercises, whichwere then evaluated both by teaching assistants and by a peer gradingmechanism, yielding a large dataset of teacher and peer grades. We applieddifferent statistical and machine learning methods to aggregate the peer gradesin order to come up with accurate final grades for the submissions (supervisedand unsupervised, methods based on numeric scores and ordinal rankings).Surprisingly, none of them improves over the baseline of using the mean peergrade as the final grade. We discuss a number of possible explanations forthese results and present a thorough analysis of the generated dataset.

Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines