Interactive and Iterative Peer Assessment

TL;DR

This paper introduces a new peer assessment model combining an evaluation algorithm and a median-based aggregation protocol, reducing grade inflation and cognitive load during in-class project presentations.

Contribution

The paper presents a novel peer grading method that improves assessment accuracy and reduces student bias and cognitive burden in peer evaluation.

Findings

Fewer ties in project rankings using the new model

Significant reduction in students' cognitive and communication burdens

Effective in real university course deployment

Abstract

Iterative peer grading activities may keep students engaged during in-class project presentations. Effective methods for collecting and aggregating peer assessment data are essential. Students tend to grade projects favorably. So, while asking students for numeric grades is a common approach, it often leads to inflated grades across all projects, resulting in numerous ties for the top grades. Additionally, students may strategically assign lower grades to others' projects so that their projects will shine. Alternatively, requesting students to rank all projects from best to worst presents challenges due to limitations in human cognitive capacity. To address these issues, we propose a novel peer grading model consisting of (a) an algorithm designed to elicit student evaluations and (b) a median-based voting protocol for aggregating grades to a single ranked order that reduces ties. An application based on our model was deployed and tested in a university course, demonstrating fewer ties between alternatives and a significant decrease in students' cognitive and communication burdens.