A model of a relative peer grading system for MOOCs

Abstract

This paper defines a model for a relative grading system that scales from low enrollment to large enrollment courses. The model allows peer grader to rank a small subset of submissions that are later merged into a global ranked set of all submissions for an assignment. This global set of ranked submissions and grades produced by expert graders(instructors and/or teachers assistants) who perform absolute grading of their own small subset of the submissions, are used to interpolate a cardinal grade for each submission of an assignment but excluding the ones graded by the experts. This model will be simulated to define the values of different parameters that produces valid and reliable grades. The research will also show that the model is scalable for different size courses by examining the effort of peer graders and expert graders.