Peer grading systems aggregate noisy reports from multiple students to
approximate a true grade as closely as possible. Most current systems either
take the mean or median of reported grades; others aim to estimate students'
grading accuracy under a probabilistic model. This paper extends the state of
the art in the latter approach in three key ways: (1) recognizing that students
can behave strategically (e.g., reporting grades close to the class average
without doing the work); (2) appropriately handling censored data that arises
from discrete-valued grading rubrics; and (3) using mixed integer programming
to improve the interpretability of the grades assigned to students. We show how
to make Bayesian inference practical in this model and evaluate our approach on
both synthetic and real-world data obtained by using our implemented system in
four large classes. These extensive experiments show that grade aggregation
using our model accurately estimates true grades, students' likelihood of
submitting uninformative grades, and the variation in their inherent grading
error; we also characterize our models' robustness.

本文探讨了对等评分系统，提出了一种新的概率模型，该模型能够更准确地估计群体作业的分数，并且使用混合整数规划将计算结果更易解释。作者在多个大型班级进行了实验验证，结果表明，使用该模型可以更准确地估计真实的分数、学生提交低精度报告的可能性以及其固有的评分误差的变化。