In higher education courses, peer assessment activities are common for keeping students engaged during presentations. Defining precisely how students assess the work of others requires careful consideration. Asking student for numeric grades is the most common method. However, students tend to assign high grades to most projects. Aggregating the peer assessments thus results in all projects receiving the same grade. Moreover, students might strategically assign low grades to the projects of others so that their projects will shine. Asking students to order all projects from best to worst imposes a high cognitive load on them, as studies have shown that people find it hard to order more than a handful of items. To address these issues, we propose a novel Rating-to-Ranking peer assessment model, $R2R$, consisting of (a) an algorithm that elicits student assessments and (b) a protocol for aggregating grades into a single order. The algorithm asks students to evaluate projects and answer pairwise comparison queries. These are then aggregated into a ranking over the projects. $R2R$ was deployed and tested in a university course and showed promising results, including fewer ties between alternatives and a significant reduction in the communication load on students.

翻译：在高等教育课程中，互评活动常用于保持学生在演示环节的参与度。如何精确界定学生评价他人作业的方式需要审慎考量。要求学生给出数字评分是最常见的方法，但学生往往倾向于给大部分项目高分，导致互评结果汇总后所有项目获得相同分数。此外，学生可能通过策略性地给他人项目打低分来凸显自身表现。若要求学生对所有项目进行优劣排序，则会带来较高的认知负荷——研究表明人们难以对超过有限数量的项目进行排序。为解决这些问题，我们提出一种新型的评分到排名互评模型$R2R$，该模型包含：(a) 激发学生评价的算法机制，以及(b) 将评分聚合为单一排序的协议。该算法要求学生评价项目并回答成对比较查询，这些数据最终被整合为项目排名。$R2R$在大学课程中部署测试后展现出良好效果，包括显著减少选项间的并列现象，并大幅降低学生的沟通负担。