As an assessment method based on a social constructivist
approach, peer assessment has become popular in recent years. When the
number of learners increases as in MOOCs, peer assessment is often con-
ducted by dividing learners into multiple groups to reduce the learner’s
assessment workload. However, in this case, a difficulty remains that
the assessment accuracies of learners in each group depends on the
assigned rater. To solve that problem, this study proposes a group opti-
mization method to maximize peer assessment accuracy based on item
response theory using integer programming. Experimental results, how-
ever, showed that the proposed method does not necessarily present
higher accuracy than a random group formation. Therefore, we further
propose an external rater selection method that assigns a few outside-
group raters to each learner. Simulation and actual data experiments
demonstrate that introduction of external raters using the proposed
method improves the peer assessment accuracy considerably.