Report-Sensitive Spot-Checking in Peer-Grading Systems

Hedayat Zarkoob, Hu Fu, Kevin Leyton-Brown

Peer grading systems make large courses more scalable, provide students with faster and more detailed feedback, and help students to learn by thinking critically about the work of others. A key obstacle to the broader adoption of peer grading is motivating students to provide accurate grades. To incentivize accurate grading, previous work has considered mechanisms that \emph{spot-check} each submission (i.e., randomly compare it to a TA grade) with a fixed probability. In this work, we introduce a mechanism that spot checks students in a way that depends on the grades they report, providing the same incentives at lower costs than the fixed-probability mechanism. We also show, surprisingly, that TA workload is reduced by choosing not to spot check some students even when TA assessments are available.

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