Biased teacher expectations of students with migration backgrounds: Analysis with nationality stereotype clusters

Abstract Student migration backgrounds and socioeconomic status can bias teacher expectations. Such expectations can lead to discrimination as they are the basis for unfair teacher assessment and exclusion from learning opportunities. To identify students at risk of experiencing low biased expectations, researchers used a grouping of nationalities based on the stereotype content dimensions of competence and warmth. The migration backgrounds of 1036 students in grades 4–6 in Switzerland were grouped into four clusters (i.e., 1 = competent, warm; 2 = medium competent, cold; 3 = incompetent, cold; 4 = incompetent, warm). Students without migration backgrounds served as reference group. Blockwise multilevel regression analyses showed that teacher expectations for student mathematical achievement were lower for students in cluster 2 compared to the reference group of students without migration backgrounds after controlling for mathematics achievement, the highest value of the parents’ occupations (HISEI), and home language. Similar results were found for teacher expectations in German. Stepwise logistic multilevel regression analyses indicated that students with migration backgrounds from cluster 2 were significantly more likely expected by teachers to enter a school track with basic demands than students without migration backgrounds after controlling for achievement in mathematics and German, HISEI, and home language. Thus, some students with migration backgrounds are particularly at risk of being discriminated against by teachers. These findings must be discussed in teacher education and used to develop preventive strategies, considering the negative influence of biased low teacher expectations on students’ educational careers and, therefore, their participation in society.