Seeing students’ multiple identity layers and weaving them into the curriculum are both important ways to apply the concept of intersectionality in practice. Another key way to do this is to take a deep look at the student data schools already have access to. “In that data, you can really pull a true narrative of what students' experiences are as it relates to the relationship that they have with the adults in the building,” says Jennifer Coco, senior staff attorney for the Southern Poverty Law Center.
Discipline referrals, for example, offer great insights: Who’s being suspended or referred—and who’s doing the referring? Looking at school data with an intersectional interpretation of who can reveal important patterns.
For example, Coco, whose works centers on educational advocacy and halting the school-to-prison pipeline, notes the fact that African-American boys with disabilities are often suspended at disproportionate rates. Knowing this, she is able to link these intersecting identity characteristics (race, gender and ability) to biases and stereotypes, namely that educators may perceive unusual or non-normative behaviors as more intimidating in black males than they would in other students. “Layering behavioral health onto issues of gender and race really gives us a clear picture of who our most profoundly vulnerable students are,” she says. And it’s impossible see that layering of information without disaggregating the data.
Many school leaders are on board with the why of disaggregating data, but may be unsure about the how. There are data-visualization tools designed specifically with schools in mind, and Tableau Public is web-based and free. Still, the process can be intimidating. “I … would encourage people to turn to their local universities; it's where most of this stuff is happening, Coco explains. “Find the education department of your local university and see if they can refer you to the people who are making these programs.” The effort is worth it: Using a tool to cross-tabulate and disaggregate student data, she says, “takes weeks of work out of trying to do the disaggregation yourself.”
The good news is that discovering biased discipline patterns doesn’t always mean completely overhauling policies or professional development approaches. In Coco’s experience, the pattern can often be traced to a few overwhelmed teachers. In that case, the solution could be targeted supports to help the teachers recognize their biases and improve their classroom management. But, without looking at the data, it is impossible to know the extent of the problem or how to best allocate school resources to address it.
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