The Complexities of Researching Biases: Insights from a Study About Teachers’ Assessment of Student Work

• In Event: Poster Session 13
  In Poster Session: PS 13 Topic -Diversity, Equity & Social Justice

Abstract

In a time when almost half of elementary school-aged students in the United States live in poor or low-income households, including approximately 60% of Black, Hispanic, and American Indian children (as compared with 28% of their White and Asian peers) (Koball & Jiang, 2018), it is imperative to understand the potential social class biases of adults who interact with them on an almost daily basis: their teachers. However, identifying potential biases in teachers is a challenging task. This poster will explore the complexity of studying implicit biases by drawing on data from a study that focused on teachers’ social class bias as it intersected with race/ethnicity and gender by examining their assessment of student work. Fifth-grade teachers in California (N = 302) were asked to use a rubric to assess a single personal narrative and were told that it was written by a fifth-grade student, although it was actually written by researchers for the purpose of the study. Before assessing this writing, participants viewed (but did not assess) what they were told was another piece of the same student’s writing. The student demographics included in this first piece of writing (i.e., student’s social class background, race/ethnicity, gender) varied and represented one of twelve possible student profiles. This piece of writing was meant to prime the participants as to the demographics of the student whose work they were going to assess. After assessing the writing, participants were asked five questions that acted as a manipulation check and assessed whether or not the participants picked up on the student demographic clues presented in the first piece of writing they were asked to read. Factorial ANCOVAs were used to assess differences in writing scores based on students’ social class, race/ethnicity, and gender and the interactions between these variables and indicated a significant interaction between students’ social class and race/ethnicity. However, the manipulation check revealed that teachers did not always view their fictitious student as being of the intended race/ethnicity. This seemed to be especially true when teachers received a student profile that was a counter-narrative to what they may be used to seeing in California public schools. For example, when teachers misidentified the race/ethnicity of what was intended to be a poor White student, they most often indicated that they thought the student was Latinx. This same trend was also found with poor African American students, who were most often miscategorized as Latinx students. This misidentification may be due to the current demographic composition in California, in which 55% of public-school students are Latinx (California Department of Education, 2020) and almost 60% of Latinx children under 18 live in low-income homes (National Kids Count, 2018). In addition to emphasizing the importance of considering the demographic context when studying potential teacher biases, the themes that arose from examining the manipulation check also underscore the need to find innovative ways to continue to examine teachers’ social class assumptions and potential biases about students from different racial/ethnic groups.

Authors

• Lindsey Nenadal, California State University - Chico
  Presenting Author

• Rashmita S. Mistry, University of California - Los Angeles
  Non-Presenting Author