Peer review, evaluation, and selection is a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of procedures or mechanisms for peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.
The peer selection problem is closely related to peer-based grading marking @cite_35 @cite_34 @cite_38 @cite_36 @cite_0 @cite_33 @cite_10 especially when students are graded based on percentile scores. For peer grading, mechanisms have been proposed that make a student's grade slightly dependent on the student's grading accuracy (see e.g., and ). However such mechanisms are not strategyproof as one may alter one's reviews to obtain a better personal grade.
@cite_35 @cite_38 @cite_33 @cite_36 @cite_0 @cite_34 @cite_10
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Crowdsourcing offers a practical method for ranking and scoring large amounts of items. To investigate the algorithms and incentives that can be used in crowdsourcing quality evaluations, we built CrowdGrader, a tool that lets students submit and collaboratively grade solutions to homework assignments. We present the algorithms and techniques used in CrowdGrader, and we describe our results and experience in using the tool for several computer-science assignments. CrowdGrader combines the student-provided grades into a consensus grade for each submission using a novel crowdsourcing algorithm that relies on a reputation system. The algorithm iterativerly refines inter-dependent estimates of the consensus grades, and of the grading accuracy of each student. On synthetic data, the algorithm performs better than alternatives not based on reputation. On our preliminary experimental data, the performance seems dependent on the nature of review errors, with errors that can be ascribed to the reviewer being more tractable than those arising from random external events. To provide an incentive for reviewers, the grade each student receives in an assignment is a combination of the consensus grade received by their submissions, and of a reviewing grade capturing their reviewing effort and accuracy. This incentive worked well in practice. Peer and self-assessment offer an opportunity to scale both assessment and learning to global classrooms. This article reports our experiences with two iterations of the first large online class to use peer and self-assessment. In this class, peer grades correlated highly with staff-assigned grades. The second iteration had 42.9p of students’ grades within 5p of the staff grade, and 65.5p within 10p. On average, students assessed their work 7p higher than staff did. Students also rated peers’ work from their own country 3.6p higher than those from elsewhere. We performed three experiments to improve grading accuracy. We found that giving students feedback about their grading bias increased subsequent accuracy. We introduce short, customizable feedback snippets that cover common issues with assignments, providing students more qualitative peer feedback. Finally, we introduce a data-driven approach that highlights high-variance items for improvement. We find that rubrics that use a parallel sentence structure, unambiguous wording, and well-specified dimensions have lower variance. After revising rubrics, median grading error decreased from 12.4p to 9.9p. We propose the PeerRank method for peer assessment. This constructs a grade for an agent based on the grades proposed by the agents evaluating the agent. Since the grade of an agent is a measure of their ability to grade correctly, the PeerRank method weights grades by the grades of the grading agent. The PeerRank method also provides an incentive for agents to grade correctly. As the grades of an agent depend on the grades of the grading agents, and as these grades themselves depend on the grades of other agents, we define the PeerRank method by a fixed point equation similar to the PageRank method for ranking web-pages. We identify some formal properties of the PeerRank method (for example, it satisfies axioms of unanimity, no dummy, no discrimination and symmetry), discuss some examples, compare with related work and evaluate the performance on some synthetic data. Our results show considerable promise, reducing the error in grade predictions by a factor of 2 or more in many cases over the natural baseline of averaging peer grades. Peer assessment is the most common approach to evaluating scientific work, and it is also gaining popularity for scaling evaluation of student work in large and distributed classes. The key idea is that each peer reviewer or grader rates a relatively small subset of the items, and that some method of manual, semi-automatic, or fully-automatic aggregation of all assessments defines the eventual rating of all items – the grade in peer grading, or whether to accept or reject a scientific manuscript. In this paper, we explore in how far a Bayesian Ordinal Peer Assessment (BOPA) method can provide additional decision support when making acceptance rejection decisions for a scientific conference. Using data from the 2015 ACM Conference on Knowledge Discovery and Data Mining (KDD), where this system was deployed, we discuss the potential merit of the BOPA approach compared to conventional decision support offered by the Microsoft Conference Management System (CMT). We describe Mechanical TA, an automated peer review system, and report on our experience using it over three years. Mechanical TA differs from many other peer review systems by involving human teaching assistants (TAs) as a way to assure review quality. Human TAs both evaluate the peer reviews of students who have not yet demonstrated reviewing proficiency and spot check the reviews of students who have. Mechanical TA also features "calibration" reviews, allowing students to quickly gain experience with the peer-review process. We used Mechanical TA for weekly essay assignments in a class of about 70 students, a course design that would have been impossible if every assignment had had to be graded by a TA. We show evidence that it helped to support student learning, leading us to believe that the system may also be useful to others.