A Regression Approach to Fairer Grading
Robert J. Vanderbei, Gordon Scharf, Daniel Marlow
TL;DR
This paper introduces a statistical method to normalize grading differences across courses by estimating course inflation and student aptitude, aiming to promote fairer academic assessments.
Contribution
It proposes a novel regression-based approach to quantify and adjust for grading variability, enhancing fairness in academic evaluations.
Findings
Effective estimation of course inflation and student aptitude.
Improved fairness in grading through statistical normalization.
Potential for broader application in educational assessment systems.
Abstract
In this paper we describe a statistical procedure to account for differences in grading practices from one course to another. The goal is to define a course "inflatedness" and a student "aptitude" that best captures one's intuitive notions of these concepts.
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Taxonomy
TopicsEducational Assessment and Pedagogy · Educational Technology and Assessment