the error. These results suggest that the generalised PeerRank method has some robustness against bias. The minimum in errors for averaging peer grades at around $r=1.5$ is likely an artefact of the model. Averaging peer grades tend to under-estimate the actual grade. Therefore a positive bias on the peer grades tends to reduce this.

RELATED WORK
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There is a large literature on peer assessment but the focus is mostly on pedagogical aspects of peer assessment (for example, how peer assessment itself contributes to the learning experience). There is less literature on how best to combine peer assessments together. Many of the peer assessments systems being used in practice today often have simple and rather ad hoc mechanisms for combining together peer assessments. In addition to multiple choice questions and computer grading, peer assessment has been used on a number of Coursera courses. Students first train with a grading rubric. To get feedback on their own work, a student has to grade five essays. The student then receives peer grades from five other students. Using machine learning algorithms, Piech [*et al.*]{} have estimated and corrected for grader biases and reliabilities [@edm2013]. They demonstrate significant improvement in grading accuracy on real 