Q&A: Tackling Bias in Math Teacher Education
色情视频 mathematician Daniel Reinholz discusses how simulation software used to train math teachers may be reinforcing racist and sexist stereotypes.
During the COVID-19 pandemic, K-12 pre-service teachers lacked opportunities to get real-world teaching experience as classrooms moved to virtual space. In response, teacher training programs increasingly incorporated third-party software that simulates a classroom environment using avatars controlled by an actor in place of real students. Post-pandemic, this software continues to be used for early field experiences or to supplement in-person student teaching.
A published June 6 in the journal Mathematics Teacher Educator reports that when used to train math teachers, such software may reinforce racist and sexist stereotypes. Mathematics teacher educator Liza Bondurant, formerly of Delta State University and now at Mississippi State University, led the research in collaboration with 色情视频 associate professor of mathematics and statistics Daniel Reinholz.
色情视频 NewsCenter鈥檚 Susanne Clara Bard asked Reinholz about the impact of the study.
How did pre-service teachers use the simulation software in your study?
We used a third-party mixed-reality simulation software platform that is widely used by teacher education programs across the country. The simulation software created a virtual classroom with avatars of five students of different genders, racial backgrounds, personality traits and typical behaviors. Real actors in the background 鈥� trained by the software company 鈥� played the roles of the avatars based on those characteristics. Based on those profiles, they would act like students for a given math lesson, such as fractions or polynomials, interacting with the pre-service teachers.
What did you measure?
Our goal was to understand whether stereotypes were present in the simulations, and if those stereotypes could impact how pre-service teachers interacted with the virtual students. Using the (Equity QUantified in Participation) tool I鈥檝e co-developed over the past 10 years 鈥� with Niral Shah at the University of Washington 鈥� we also analyzed the relationship between student and teacher behaviors.
How did the teachers perceive the simulated student avatars?
What we found overall was that of the five avatars, one was unambiguously identified as a white woman by the pre-service teachers. That student seemed to have good intentions in math class but was somewhat shy. The pre-service teachers identified the other four avatars as racially ambiguous students of color. From the researchers鈥� perspective, one student clearly felt like a South Asian-American boy, based on the name, and based on how the boy enacted stereotypes about Asians in mathematics. One young woman of color was depicted as rude and disruptive, and another tried to please the teacher, but didn鈥檛 really understand the math. Then there was a young man of color who made jokes in class and could not focus.
How did these stereotypes affect the teachers鈥� interactions with the student avatars?
The EQUIP tool allowed us to understand the patterns of interactions that occurred between pre-service teachers and the virtual students in the third-party software. Our data showed that the pre-service teachers spent more time talking about math with the white and South Asian-American students. On the flip side, when the teachers interacted with the negatively characterized students of color, they were primarily focused on managing the students鈥� behavior rather than the math. And there are a couple of consequences for that.
We know that when students get to participate, do the math, and see themselves as mathematicians, it supports the learning process. In this simulated scenario, only some students got a chance to do meaningful math. Although we initially hoped the third-party simulation software would disrupt stereotypes and promote equitable participation patterns, we found that the nature of the simulations may have reinforced stereotypes. It鈥檚 concerning that pre-service teachers could carry these same biases into classrooms with real students.
What are the biggest takeaways from your study?
On the surface, people think of math as objective and neutral, and not having anything to do with race, gender, or identity in general. But when we go into the classroom, it鈥檚 actual people doing the math, so identities matter. Research is clear that students have different math experiences based on their backgrounds, because of how they are treated differently because of their backgrounds.
One of the goals with this study was to take an equity lens to thinking about a virtual simulation for future teachers. But not many others in the field had really been asking whether this could promote equity or could this actually be a hindrance to equity in math classrooms.
Math stereotypes related to race and gender are everywhere in our popular culture, and they are problematic. Our advice in the paper for companies designing these simulations is to create avatar characters that challenge these stereotypes, rather than reinforcing them. We know that stereotypes have real impacts on students, and disadvantage many groups, including women, students of color, and disabled students in mathematics. We do this type of research because we want every learner to thrive in math class and learn how to know, use, and enjoy math.
