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Peer grading systems make large courses more scalable, provide students withfaster and more detailed feedback, and help students to learn by thinkingcritically about the work of others. A key obstacle to the broader adoption ofpeer grading systems is motivating students to provide accurate grades. Theliterature has explored many different approaches to incentivizing accurategrading (which we survey in detail), but the strongest incentive guaranteeshave been offered by mechanisms that compare peer grades to trusted TA gradeswith a fixed probability. In this work, we show that less TA work is requiredwhen these probabilities are allowed to depend on the grades that studentsreport. We prove this result in a model with two possible grades, arbitrarynumbers of agents, no requirement that students grade multiple assignments,arbitrary but homogeneous noisy observation of the ground truth which studentscan pay a fixed cost to observe, and the possibility of misreporting gradesbefore or after observing this signal. We give necessary and sufficientconditions for our new mechanism's feasibility, prove its optimality underthese assumptions, and characterize its improvement over the previous state ofthe art both analytically and empirically. Finally, we relax our homogeneityassumption, allowing each student and TA to observe the ground truth accordingto a different noise model.

Report-Sensitive Spot-Checking in Peer-Grading Systems