Domain Experts' Interpretations of Assessment Bias in a Scaled, Online Computer Science Curriculum

Abstract

Understanding inequity at scale is necessary for designing equitable online learning experiences, but also difficult. Statistical techniques like differential item functioning (DIF) can help identify whether items/questions in an assessment exhibit potential bias by disadvantaging certain groups (e.g. whether item disadvantages woman vs man of equivalent knowledge). While testing companies typically use DIF to identify items to remove, we explored how domain-experts such as curriculum designers could use DIF to better understand how to design instructional materials to better serve students from diverse groups. Using Code.org’s online Computer Science Discoveries (CSD) curriculum, we analyzed 139,097 responses from 19,617 students to identify DIF by gender and race in assessment items (e.g. multiple choice questions). Of the 17 items, we identified six that disadvantaged students who reported as female when compared to students who reported as non-binary or male. We also identified that most (13) items disadvantaged AHNP (African/Black, Hispanic/Latinx, Native American/Alaskan Native, Pacific Islander) students compared to WA (white, Asian) students. We then conducted a workshop and interviews with seven curriculum designers and found that they interpreted item bias relative to an intersection of item features and student identity, the broader curriculum, and differing uses for assessments. We interpreted these findings in the broader context of using data on assessment bias to inform domain-experts’ efforts to design more equitable learning experiences.

Publication
Learning @ Scale