Addressing Bias in Writing Assessments: A QuantCrit Exploration of a Culturally Responsive Modified Scoring Approach

• In Event: AERA Roundtable Session Saturday 9:50 am Four Seasons Ballroom 1
  In Roundtable Session: Innovative Approaches to Quantitative Analysis: Addressing Bias, Model Constraints, and Intersectionality in Social Research (Table 3)

Abstract

This study employs QuantCrit to examine potential biases in curriculum-based measurement of written expression (CBM-WE), specifically the correct minus incorrect word sequences (CIWS) metric. Guided by DisCrit and QuantCrit frameworks, we investigate how this metric may disproportionately impact students from marginalized linguistic communities. We evaluate a modified CIWS scoring procedure designed to reduce bias against elementary students using nonmainstream American English features in writing. This research contributes to the AERA conference theme by critically examining assessment practices that may perpetuate educational inequalities and proposing a remedy to improve fairness in evaluating writing abilities of culturally and linguistically marginalized students. Our findings aim to advance educational equity and ensure academic assessments accurately reflect diverse competencies of all students.

Authors

• Lindy Johnson, Michigan State University

• Adrea Truckenmiller, Michigan State University

• Cherish Marie Sarmiento, Michigan State University

• Lauren Hennenfent, Michigan State University

• Katie Valentine, Michigan State University

• Pamella Moura, Michigan State University

• Gary A. Troia, Michigan State University