Could appropriate use of data by educators lead to more equitable access to high-quality mathematics for K-12 students? This is the question three NC State College of Education faculty explore in their new book The Stories We Tell: Math, Race, Bias, and Opportunity.
Teaching Associate Professor Valerie Faulkner, Ph.D., Professor of Multicultural Studies Patricia L. Marshall, Ed.D., and Associate Dean for Faculty and Academic Affairs Lee V. Stiff, Ph.D., began collaborating several years ago. One of their articles, “Less is More,” examined how irrelevant data gets used in all manner of decision making including how education professionals group students for instruction. Following the publication of that article, Rowman and Littlefield approached the trio about expanding their work into a book.
“One of the things we examine is how teacher perspectives about students are often colored by ideas about race and by those features or group categories over which students have no control and really are not relevant to making a decision about who gets to have access to particular mathematics courses,” Marshall said.
The authors analyze the patterns of practice that are in place as children are sorted according to perceived needs. Through multiple case studies within and outside of schools, the book highlights what the authors refer to as “data doppelgangers” or “data doppels,” which occur when data or facts that are not relevant to a decision are used to inform the decision making process. In the world of education, these “data doppels” are pieces of information that are not indicative of students’ mathematics performance but are sometimes considered in the decision-making process for student placement.
“People think they’re using useful data to help kids be identified and to benefit the kids when in fact, they may be using data that’s not relevant or valuable or even accurate,” Stiff said. “Oftentimes, these data don’t speak to the qualifications one should have to participate in certain math programs.”
Case studies made famous by Malcolm Gladwell and Michael Lewis are explored in the book’s second chapter to provide a broader context of how “data doppels” and the use of too much data have interfered with decision making in other fields. The book explores, for example, the Goldman Formula — a four-data point scale that research shows has misidentified those at risk for immediate heart attack — as well as how gender balance changed in orchestras when the visual data involving gender and how a person looked while playing were removed or lessened in the audition process. The book then connects the “data doppel” phenomenon to educational access by exploring research as well as three case studies based on the authors’ own work in schools.
One such case study highlights an instance where review of data at a middle school revealed that one-third of seventh graders qualified for placement in pre-algebra classes based on district requirements, but were not given access to the class. The majority of those seventh graders were students of color, according to the data. After reviewing the data, the school’s principal insisted those students be placed in pre-algebra classes, where they performed well. The following year, after a change of leadership at the school, placement decisions reverted back to previous policies wherein students were placed in pre-algebra classes based solely on teacher and counselor recommendations. That change resulted in a large number of students of color being excluded from pre-algebra classes even when their grades met district guidelines for enrollment, according to the case study.
“Some students are meeting the criteria that’s been stated [for selection into advanced mathematics] and then these other soft data are being used at the decision-making table,” Faulkner said. “Those in charge of placement selection use the data but tweak it, and what we know from study after study is that these tweaks disadvantage black kids.”
The book explores how professional decision making, along with a more precise use of data, can impact mathematical performance outcomes. The authors suggest that, in some cases, too much irrelevant data is used to determine which students can access advanced mathematics. Instead, they recommend, educators should focus entirely on performance data when determining placement.