Are Achievement Gaps Related to Discipline Gaps? Evidence From National Data

Abstract

There is growing interest in the relation between the racial achievement gap and the racial discipline gap. However, few studies have examined this relation at the national level. This study combines data from the Stanford Education Data Archive and the Civil Rights Data Collection and employs a district fixed effects analysis to examine whether and the extent to which racial discipline gaps are related to racial achievement gaps in Grades 3 through 8 in districts across the United States. In bivariate models, we find evidence that districts with larger racial discipline gaps have larger racial achievement gaps (and vice versa). Though other district-level differences account for the positive association between the Hispanic-White discipline gap and the Hispanic-White achievement gap, we find robust evidence that the positive association between the Black-White discipline gap and the Black-White achievement gap persists after controlling for a multitude of confounding factors. We also find evidence that the mechanisms connecting achievement to disciplinary outcomes are more salient for Black than White students.

The Role of Cognitive Stimulation in the Home and Maternal Responses to Low Grades in Low-Income African American Adolescents’ Academic Achievement

“…For African American parents, involvement in education takes place in an educational landscape in which African American students, on average, have lower levels of academic achievement than their peers in other racial/ethnic groups (Taylor et al 2018). School segregation (Reardon 2016), racial disparities in school discipline (Pearman et al 2019), and low teacher expectations for African American students (McKown and Weinstein 2008), among other factors, help fuel these gaps in achievement. As a consequence of the disadvantaged status of African American students in the American educational system, a disproportionate number of African American parents will be in a position to respond to academic underachievement on the part of their children.…”

Beyond Technocentrism: Coding as Experience

“…75 Margolis also argued that deficit-perspectives held by teachers regarding abilities of Black and Latino students actually result in the low performance of these students in computing classes, and more recent analysis of nationwide educational achievement data shows that teacher bias and other systemic obstacles hinder overall academic progress for Black students in the US. 76 It is therefore imperative for us to think carefully about where to situate computational abstractions. The predominant adoption of Wing's notions of computational thinking and computational abstractions locates them in the mind in the form of thought processes.…”

“…The predominant adoption of Wing's notions of computational thinking and computational abstractions locates them in the mind in the form of thought processes. 77 This unwittingly reifies the divide between vocational and intellectual work and furthers conceptualizations of technology that fail to consider the broader, infrastructural view beyond the child-computer dyad. As Margolis and colleagues have argued, this eventually leads us to erroneous conclusions about the ability of children, especially from marginalized groups whose struggles to overcome significant historical and systemic obstacles often remain invisible in their classroom work.…”

What Is the Stanford Education Data Archive Teaching Us About National Educational Achievement?

“…Data sets like SEDA enable researchers to not only point in a direction, but put a finger on magnitudes, in national context. Second, all of these articles linked educational data to other variables in other data sets, including the Quarterly Census of Employment and Wages (Shores & Steinberg, 2019), the Transitional Records Access Clearinghouse (Bellows, 2019;Kirksey et al, 2020), the Civil Rights Data Collection (Pearman et al, 2019), the American Community Survey , and MAP Growth (Kuhfeld et al, 2019). Whether as outcomes, covariates, or question predictors, these merged variables enable researchers to answer important descriptive and causal questions.…”