Fairness in Test Scoring

By Randall D. Penfield. 2016

11 citations recorded by the source corpus.

Subfields: Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sociology and Political Science

1. Nitin Madnani
2. Ben Hutchinson
3. Anastassia Loukina
4. Klaus Zechner
5. Margaret Mitchell

Citations in the corpus

• 50 Years of Test (Un)fairness: Lessons for Machine Learning (2018, background): “ in the education field, and to this day DIF is in the toolbox of test designers. Items displaying a DIF are ideally examined further to identify the cause of bias, and possibly removed from the test [50]. 2.5 1980s and beyond With the start of the 1980s came renewed public debate about the existenc”
• Building Better Open-Source Tools to Support Fairness in Automated Scoring (2017, background): “To guard against such rater inconsistencies, responses for high-stakes tests are often scored by multiple raters (Wang and von Davier, 2014; Penfield, 2016).”
• The many dimensions of algorithmic fairness in educational applications (2019, methodology): “cess was set up following best practices in order to minimize any possible bias (Wang and von Davier, 2014; Penfield, 2016).”