Peer assessment is a method that has shown a positive impact on learners' cognitive and metacognitive skills. It also represents an effective alternative to instructor-provided assessment within computer-based education and, particularly, in massive online learning settings such as MOOCs. Various platforms have incorporated this mechanism as an assessment tool. However, most of the proposed implementations rely on the random matching of peers. The contributions introduced in this article are intended to step past the randomized approach by modeling learner matching as a many to many assignment problem, and then its resolution by using an appropriate combinatorial optimization algorithm. The adopted approach stands on a matching strategy that is also discussed in this article. Furthermore, we present two key steps on which both the matching strategy and the representation of the problem depend: 1) modeling the learner as an assessor, and 2) clustering assessors into categories that reflect learners’ assessment competency. Additionally, a methodology for increasing the accuracy of peer assessment by weighting the scores given by learners is also introduced. Finally, compared to the random allocation of submissions, the experimentation of the approach has shown promising results in terms of the validity of assessments and the acceptance of peer feedback.

同伴评估是一种对学习者的认知和元认知技能产生积极影响的方法。在基于计算机的教育中，尤其是在大型在线学习环境（例如，MOOC）中，它也可以替代教师提供的评估。各种平台都将该机制作为评估工具。但是，大多数建议的实现方式都依赖于对等方的随机匹配。本文介绍的贡献旨在通过对学习者匹配进行多对多分配问题建模，然后通过使用适当的组合优化算法来解决学习者建模问题，从而超越随机方法。所采用的方法基于本文还将讨论的匹配策略。此外，我们提出了匹配策略和问题表示形式所依赖的两个关键步骤：1）将学习者建模为评估者，以及2）将评估者聚类为反映学习者评估能力的类别。另外，还介绍了通过加权学习者给出的分数来提高同伴评估准确性的方法。最后，与提交的随机分配相比，该方法的实验在评估的有效性和对同伴反馈的接受方面显示出令人鼓舞的结果。还介绍了通过加权学习者给出的分数来提高同assessment评估准确性的方法。最后，与提交的随机分配相比，该方法的实验在评估的有效性和对同伴反馈的接受方面显示出令人鼓舞的结果。还介绍了通过加权学习者给出的分数来提高同assessment评估准确性的方法。最后，与提交的随机分配相比，该方法的实验在评估的有效性和对同伴反馈的接受方面显示出令人鼓舞的结果。