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A Science Teacher-Friendly Primer on Implicit Bias

Mrs. Calvinski (pseudonym) is a high school science teacher in a large midwestern district. She considers herself to be a racial progressive and an antiracist. She has taken advanced courses on race, culture, and education. She teaches students of color, is involved in antiracist professional development, and is a supporter of an equality initiative. She has excellent rapport with all of her students, but she had never disaggregated education data to identify potential biases. During the pandemic, Mrs. Calvinski uncovered grade gaps which could potentially be attributed to implicit bias. This opening vignette represents an actual science teacher who agreed to participate in this project.

Background

Educators may deny implicit bias exists because they do not want American society, schools, or teachers to be racist (Miller 2019). Well-meaning educators may harbor racial bias, but they may not be aware it exists or how it impacts students. The concept of bias is a well-understood idea in science that must be properly addressed to ensure the integrity of the enterprise. There are dozens to hundreds of types documented in the literature. Selection bias (bias in properly selecting participants for a study) and sampling bias (bias where members of a population are less likely to be sampled) both involve procedural challenges in designing scientific research (Bamberger, Rugh, and Mabry 2012; Krathwohl 1998).

Bias can be introduced into scientific research through study design (e.g., selection bias), data collection (e.g., recall bias), or statistical analysis (e.g., analytical bias) (Pannucci and Wilkins 2010). Bias is not only important in the natural sciences, but it is also significant in fields like psychology and sociology (Carter et al. 2019). In education, implicit racial bias disproportionately affects students of color through grading, disciplinary practices, in-class support, and contact time. This article focuses on introducing science teachers to the scientific and sociocultural foundations of implicit bias by providing educators with a framework to investigate it.

Nature of bias

The online Oxford dictionary defines bias as “(unfair) prejudice in favor of or against one thing, person, or group compared with another.” It can be inferred that racial bias involves favoring or disfavoring groups of people based on skin color. Blatant racism is mostly unacceptable in education except perhaps in spaces where educators feel free to discuss resentments. Scholars argue that modern racist manifestations may have changed from blatant acts of racism (e.g., Jim Crow laws) to hidden, ordinary, widespread acts of racism in society (e.g., racial microaggressions) (Sue at al. 2007), which contribute to pervasive, systemic discrimination.

Racial bias may be directed by the subconscious mind (the nonconscious part of the mind guiding most of our decision-making) or the unconscious mind (repressed mental processes that silently influences decision-making) (Rezaee and Farahian 2015). Psychology research suggests unconscious racial bias is acquired through experiences in childhood. Many things we have learned about race as youngsters are suppressed in the mind; people unknowingly carry subconscious and unconscious biases (Greenwald and Banaji 1995). Conscious racial attitudes and stereotypes are those that have been recently acquired. The next section describes implicit bias in more depth.

Implicit bias

Psychologists have studied implicit bias for decades, but it has only recently come to the forefront of education. Implicit bias within broader processes and systems of racism has major implications for people of color, including, but not limited to, psychological impacts, psychiatric effects, discipline discrepancies, differential medical treatment, limited access to the housing market, lower-quality customer service, fewer teacher interactions, profiling by law enforcement, and stricter court sentencing. Sue et al. (2007) detailed the nature and types of implicit bias, defining racial microaggressions as “brief and commonplace daily verbal, behavioral, or environmental indignities, whether intentional or unintentional, that communicate hostile, derogatory, or negative racial slights and insults toward people of color” (p. 271).

After reviewing the social psychology literature, Sue et al. (2007) identified three categories of racial microaggressions as microassaults, microinsults, and microinvalidations (Figure 1). Microassaults are usually conscious verbal attacks or abusive language directed toward people of color, so they may not always be caused by implicit bias. Microassaults are often unconscious verbal or behavioral treatments that demean people of color. Microinvalidations are behaviors or comments that undercut the experiences of peoples of color.

Figure 1

Types of racial microaggressions with examples (Sue et al. 2007).

Type

Examples from science classrooms

Microassault (conscious)

“Quit acting like a rapper and sit yourself down to do something good for your life.”

Microinsult

“You are really great at math and science for coming from an inner-city, urban school.”

Microinvalidation

“I don’t see your color,” (color-blind ideology or racism), “so I don’t see what the big deal is. You are making too much out of this. We are all human beings.”

Environmental microinvalidation

A science classroom has over twenty posters of great scientists hanging on the walls. All of the scientists are White males with the exception of one White female. There are no great scientists of color.

Readers may question the veracity of the psychology of implicit bias. Whether you question implicit bias or not, please retain an open mind until you have explored the four recommendations. The next section helps to explain how implicit bias affects students of color.

The bigger picture

Implicit bias within the broader processes and systems of racism may have dramatic impacts on students of color. Bias influences how students of color interface with science curriculum and instruction; implicit bias has the tendency to reduce the overall quality of learning experiences. Bias typically results in increased special education referral, a reduction in gifted services, and greater statistical chances of stricter discipline for minor offenses (Miller 2019).

Whenever teachers’ judgments are influenced by subconscious or unconscious processes, which is most of the time, students of color have the opportunity to suffer potential injustices or mistreatment. An example was Denise (pseudonym)—a freshman African-American student in Mrs. Stevenson’s (pseudonym) math class. Denise always seemed to be getting into trouble with Mrs. Stevenson. Mrs. Stevenson called Denise’s parents multiple times about her “behavior.” Denise’s science teacher wasn’t observing similar “behavior” problems. Denise reported to her science teacher, “[Mrs. Stevenson] does not like me because ... [student points to the Black skin on her hand]. She does not like Asians and Hispanic students [either.]” In this case, Denise resolved the differential treatment she experienced in math class and expressed it (Miller 2019). The next four paragraphs detail recommendations for how science teachers may begin recognizing bias.

Recommendation one:

The Project Implicit® IATs (Implicit Association Tests) are perhaps the foremost scientific surveys administered to measure implicit bias (Figure 2). The IATs may be accessed through the Project Implicit® website (Figure 2). Thousands of studies have been conducted using the association tests. The Race IAT, which we focus on here, involves a sorting test where faces are categorized on the basis of good and bad words. The program records how the words are associated with the facial images of African Americans and European Americans. Response times are recorded by the website, and this determines the level of automatic preference for European Americans or African Americans. Stronger preferences may indicate the presence of racial biases in automatic decision making (Project Implicit® 2021). After taking an IAT, I expect science teachers are ready to learn more .

Figure 2

Four of the fifteen Implicit Association Tests (IATs) developed by an international team of implicit social cognition researchers affiliated with Project Implicit®.

IAT Test

Description

Asian IAT

“This IAT requires the ability to recognize White and Asian-American faces, and images of places that are either American or Foreign in origin.”

Race IAT

“This IAT requires the ability to distinguish faces of European and African origin. It indicates that most Americans have an automatic preference for white over black.”

Native IAT

“This IAT requires the ability to recognize last names that are more likely to belong to Native Americans versus White Americans.”

Skin-tone IAT

“This IAT requires the ability to recognize light and dark-skinned faces. It often reveals an automatic preference for light-skin relative to dark-skin” (Project Implicit 2021).

Web Link: https://tinyurl.com/bzqq43f

The above web link will take you to the Project Implicit® website which lists fifteen IATs. The IATs were developed by an international team of researchers who study implicit social cognition.

 

Recommendation two: Become knowledgeable

There are too many details and nuances to race and racism to provide fair treatment in a short manuscript. However, one of the best ways to learn more about issues surrounding race, racism, and implicit bias is through continuous, reflective reading. Many bookstores currently have titles like DiAngelo’s White Fragility and Kendi’s How to Be an Antiracist (Figure 3(a)). Most of the information in these readings challenges traditional notions of color blindness (i.e., the idea that one’s race does not influence opportunities) and Whiteness, so an open mind is necessary.

Figure 3

Science teacher readings on race, racism, and implicit bias.

  • Alexander, M. 2012. The new Jim Crow: Mass incarceration in the age of colorblindness. New York: The New Press.
  • Bell, D. 2018. Faces at the bottom of the well: The permanence of racism. New York: Basic Books.
  • Delgado, R., and J. Stefancic. 2017. Critical race theory: An introduction. New York: NYU Press.
  • DiAngelo, R. 2018. White fragility: Why it’s so hard for white people to talk about racism. Boston: Beacon Press.
  • Emdin, C. 2016. For White folks who teach in the hood... and the rest of y’all too: Reality pedagogy and urban education. Boston: Beacon Press.
  • Emdin, C. 2010. Urban science education for the hip-hop generation. Rotterdam: Sense Publishers.
  • Kendi, I.X. 2019. How to be an antiracist. New York: One World.
  • Noguera, P.A. 2009. The trouble with black boys: ... And other reflections on race, equity, and the future of public education. San Francisco: Jossey-Bass.
  • Perry, T., Steele, C., and A.G. Hilliard. 2003. Young, gifted, and Black: Promoting high achievement among African-American students. Boston: Beacon Press.
  • Stevenson, B. 2019. Just mercy: A story of justice and redemption. New York: Spiegel & Grau.
  • Woodson, C.G. 2006. The mis-education of the Negro. San Francisco: Book Tree.

Selected teen books on race, racism, and implicit bias.

  • Reynolds, J. and I.X. Kendi. 2020. Stamped: Racism, antiracism, and you: A remix of the National Book Award-winning Stamped from the Beginning. New York: Little, Brown Books for Young Readers.
  • Stone, N. 2020. Dear Justyce. New York: Crown Books for Young Readers.
  • Stone, N. 2017. Dear Martin. New York: Crown Books for Young Readers.
  • Thomas, A. 2017. The hate you give. New York: Balzer + Bray.
  • Watson, R. 2017. Piecing me together. New York: Bloomsbury.

Under the leadership of their principal, teachers at Northfield High School (pseudonym) in the midwest have formed book studies to learn more about race and racism by reading teen literature (Figure 3(b)). Northfield teachers have read Nic Stone’s Dear Martin to study the experiences of students of color at an elite preparatory academy. The main character Justyce is an Ivy-bound student who was racially-profiled. Several Northfield students agreed to participate in the book study and discussed issues of racism with other students. Continual reading and listening to the voices of the oppressed is necessary to truly situate contemporary race, racism, and implicit bias within education and society.

Recommendation three: Build an inclusive classroom

Many books and articles are dedicated to building racially-inclusive classrooms. Building a racially-inclusive classroom ultimately hinges on maintaining strong rapport and relationships with all students. Inclusive classrooms accept and honor students of all ethnic and racial backgrounds, so they should be free of racial microagressions. When teachers observe racial microaggressions, then an appropriate pedagogical intervention should address them (Kang and Garran 2018). Science teachers should consider writing a diversity and inclusion statement to help guide initial work toward building a racially-inclusive classroom. This statement could appear on course syllabi and parent correspondence, and be displayed in the science classroom. Diversity and inclusion statements should be viewed as living documents open to administrative, community, parent, and student feedback and input. A statement should help guide ongoing, iterative work to build racially-inclusive science classrooms where all students may engage in scientific practices (Figure 4).

Figure 4

A sample diversity and inclusion statement for a high school science classroom.

Respect for Diversity – It is my goal to make sure students from every background and viewpoint are equally well-served by the science instruction in this course. Diversity in the classroom adds significant value to the learning experience for everybody including the instructor. Scientific studies demonstrate the fact diversity improves the performance of teams. Diverse teams deliver better solutions to complex problems. This classroom will be a place where you are free to be the person you are; it will embrace people of all statuses including but not limited to gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and culture. If a situation becomes offensive in the class then I need to know about it immediately, so I may address it. I am always looking for ways to improve my instruction. Also, I am interested in finding new ways to help every student succeed. If you have suggestions about the class, I am always ready to listen to you. Further, if you have information you need to share about your life experiences, religion, family or other personal information, I am here to listen.

Resource Link: Brown University’s The Harriet W. Sheridan Center for Teaching and Learning, tinyurl.com/3r8szvtm

Recommendation four: Continuous feedback

Science teachers should consider continually engaging in qualitative and quantitative data collection that links to a feedback loop. Teachers often naturally collect data, even when they do not realize it, by making observations, asking questions, and grading formative assessments or short-cycle assessments (SCAs). Data is also available through semester or end-of-course student surveys, especially if it can be disaggregated by race. Mrs. Calvinski realized more males (44%) failed her class than females (18%), so she dug further and discovered no males of color received an A (Figure 5(b)). This bothered Mrs. Calvinski, but she was unable to disaggregate her end-of-the-semester course surveys.

Figure 5
(A) Distribution of summative grades for all males for the quarter; (B) average score results for Mrs. Calvinski’s semester survey for questions 10 and 15; (C) potential disaggregated data for question 10 on the semester survey. The question asked students “[Rate the] skill and responsiveness of the instructor (instructor was available and helpful);” (D) potential disaggregated data for question 15 on the semester survey. The question asked students “[Rate the] course content (the course was organized to al

(A) Distribution of summative grades for all males for the quarter; (B) average score results for Mrs. Calvinski’s semester survey for questions 10 and 15; (C) potential disaggregated data for question 10 on the semester survey. The question asked students “[Rate the] skill and responsiveness of the instructor (instructor was available and helpful);” (D) potential disaggregated data for question 15 on the semester survey. The question asked students “[Rate the] course content (the course was organized to allow all students to participate fully).”

The surveys were completely anonymous without demographics (Figure 5(a)). Mrs. Calvinski’s student survey questions were well-suited to gauge how she served males of color; open response questions would have added value. What appeared to be high average scores (Figure 5(a)) could still conceivably demonstrate she was not optimally serving males of Color (Figures 5(c) and 5(d)). Mrs. Calvinski plans to write a diversity and inclusion statement for next school year (Figure 4). She also plans to collect demographic data in her course surveys, so she can determine how she is meeting the needs of her males of color (Figure 6). With high-quality qualitative and quantitative data, it is possible to ask whether science curriculum, instruction, and assessment (CIA) is aligned to one’s diversity and inclusion statement.

Figure 6

Potential questions Mrs. Calvinski (or any science teacher) could ask after reviewing educational data disaggregated by race.

  1. Why is the failure rate so high for male students?
  2. Why do white males have more A’s than males of color?
  3. How do males of color experience the science curriculum, instruction, and assessment (CIA)?
  4. How does the science teacher interact with parents beyond failure and discipline referral notifications?
  5. How could teacher bias possibly manifest itself in the science CIA?
  6. Is the science CIA culturally relevant and responsive to males of color?
  7. How much time is the science teacher spending with males of color?
  8. Are expectations the same for all male students?
  9. Does this science class celebrate and recognize the scientific contributions of diverse scientists?

Conclusion

Almost all racial bias manifests itself through subconscious and unconscious actions. Most science teachers aren’t aware they possess racial bias or how it affects students. Implicit racial bias often manifests itself in the form of racial microaggressions. The recommendations made in this commentary aim to help science teachers learn more about racial bias so they may address it in the classroom.

Acknowledgments

The author would like to thank Northfield City Schools (pseudonym) for being supportive of the ongoing work of studying implicit bias in public high schools. Further, the author is greatly indebted to the principal of Northfield High School for sharing ideas and resources. Finally, Thomas Poetter, PhD and Denise Taliaferro Baszile, PhD at Miami University deserve a huge thank you for making this project possible.


Kurtz Miller, PhD (kurtz.miller@myhhcs.org) is a science teacher at Huber Heights City Schools in Huber Heights, Ohio.

References

Bamberger, M., J. Rugh, and L. Mabry. 2012. RealWorld evaluation: Working under budget, time, data, and political constraints. Washington DC: Sage.

Carter, E.C., F.D. Schönbrodt, W.M. Gervais, and J. Hilgard. 2019. Correcting for bias in psychology: A comparison of meta-analytic methods. Advances in Methods and Practices in Psychological Science 2 (2): 115–144.

Diangelo, R. 2018. White fragility: Why it’s so hard for White people to talk about racism. Boston, MA: Beacon Tree.

Greenwald, A.G., and M.R. Banaji. 1995. Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review 102 (1): 4–27.

Greenwald, A.G., and L.H. Krieger. 2006. Implicit bias: Scientific foundations. California Law Review 94 (4): 945–967.

Kang, H, and A.M. Garran. 2018. Microaggressions in social work classrooms: Strategies for pedagogical intervention. Journal of Ethnic & Cultural Diversity in Social Work 27 (1): 1–13.

Krathwohl, D.R. 1998. Methods of educational and social science research. Long Grove, IL: Waveland Press, Inc.

Miller, K.K. 2019. The essence of awareness of implicit bias: A phenomenological case study of educators’ stories of coming to the realization they possess implicit bias (Doctoral dissertation, Miami University).

Pannucci, C.J., and E.G. Wilkins. 2010. Identifying and avoiding bias in research. Plastic and Reconstructive Surgery 126 (2): 619–625.

Project Implicit. 2021, April 21. https://implicit.harvard.edu/implicit/selectatest.html

Rezaee, M., and M. Farahian. 2015. Subconscious vs. unconscious learning: A short review of the terms. American Journal of Psychology and Behavioral Sciences 2 (3): 98–100.

Sue, D.W., C.M. Capodilupo, G.C. Torino, J.M. Bucceri, A. Holder, K.L. Nadal, and M. Esquilin. 2007. Racial microaggressions in everyday life: implications for clinical practice. American Psychologist 62 (4): 271–286.

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