Three things that may be wrong with your school data

In the aftermath of educational reforms enacted under “No Child Left Behind” and the “Every Student Succeeds Act,” the collection of data, including student test scores and enrollment data, has been used by schools to identify “achievement gaps” rather than “opportunity gaps.” The rationale behind these data collection mandates, which were established by statute, was that schools would be better able to identify deficiencies affecting student performance and put additional resources into those areas that would then lead to improvements. Instead, the collection of data reflecting poor test scores often gave rise to a “deficit” way of thinking about the student. Under such a scenario, the student may be “tracked” by being placed into a lower-level course sequence and may become subject to lowered expectations which in turn could limit their ability to progress academically with more challenging and advanced coursework. These students may be denied the same educational opportunities that their peers have. The data may show a poor test score for a student, but it does not explain the underlying reasons as to why the student received a poor test score—and this is key. The following discussion focuses on three ways in which data collected by schools may obscure systemic inequities that can reduce the chances of achieving optimal learning outcomes for certain groups of students—particularly low-income and underserved students.

Data may not accurately reflect how school practices can hinder student learning

Researchers emphasize that using data to make decisions about teaching or curriculum without a critical awareness of existing school practices that may impede a student’s learning can widen performance gaps among groups of students. A paper co-authored by faculty from George Mason University’s School of Education illustrates this concept with an example where a third-grade teaching team meets to identify which students should be grouped together for differentiated reading instruction based on their reading fluency test scores. The teaching team may sort students into reading levels such as “high” and “low” and attribute these differences to inherent deficiencies in students’ abilities. However, the problem may not be linked to the aptitude or ability of the individual students but rather the problem might be systemic in nature and related to the books the children are assigned to read in class. Those students with low reading scores may find the literature boring or irrelevant and difficult to relate to from the vantage point of their own lives. The authors point out, “Decades of research has shown the importance of engaging young readers with literature that reflects their identities and experiences, and that schools have much room for improvement in ensuring access to these resources.”

Data may be misused as a “gatekeeper” to deny some students access to more challenging coursework

Existing studies warn against using data as a “gatekeeper” to keep certain students from accessing higher level, more challenging coursework. Very often the students who are most affected are marginalized students, students of color, or lower-income students from underserved communities who may have performed poorly in a course because they did not have access to the structural supports that would have resulted in better academic outcomes. In their paper, the Mason authors cite the case of a high school Advanced Placement (AP) history teacher who sought stricter enrollment policies after class data showed that many of his students in his open-enrollment class failed to take or pass the AP exam. The authors suggest that data in this situation is being misused to eliminate certain achievement gaps by barring students from taking the class through enrollment restrictions. This runs contrary to the goal of ensuring that the foundation of a classroom environment is built on equity. The authors explain that while the history teacher is making his decision to restrict enrollment based on data showing student failure rates, he is not analyzing the data on a deeper level which would require him to consider the structural supports that may or may not be available to his students and that would require him to think about how his teaching practices may be impacting students. The authors further note, “...if he is not engaging with data critically, the teacher may not be considering ways in which culturally, linguistically, and economically dominant students are systematically advantaged by policies, pedagogy, and curriculum that contribute to their success.”

The interpretation of certain data may be affected by implicit bias

Certain data requires critical reflection on the part of the teacher who may have an implicit bias, particularly when it comes to students of certain races, ethnicities, and cultures. Some studies have shown that a teacher’s personal bias around culture or race can lead to a false expectation that a student will perform poorly in school. This can impede the teacher’s ability to conduct an objective and critical analysis of data which requires that factors such as a student’s home life and economic circumstances be considered when evaluating a student’s performance. Mason researchers caution, “Without encouraging critical self-reflection steeped in an understanding of implicit bias and structural oppression, teachers’ use of data is unlikely to address well-documented causes of gaps (e.g., exclusionary curriculum and school policies, teacher deficit thinking, unjust distribution of resources) and thus fail to achieve their professed desired outcomes of equity.”

The authors of the paper propose a “critical data-driven decision making (CDDDM)” framework which demands that a greater level of attention be given to the structural and systemic inequities that exist in schools. The authors maintain that the use of the CDDDM framework can shift the analysis of school data away from an “achievement gap” approach to one centered on identifying “opportunity gaps.” Researchers contend that if educators limit their focus to student achievement data for the purpose of improving test scores and ignore the structural causes and conditions that negatively impact underserved or marginalized students of diverse races and ethnicities, the existing inequities in schools that deny these students educational opportunities will continue.

For more information on the use of school data, read this article by faculty at George Mason University’s School of Education. The Advanced Studies in Teaching and Learning (ASTL) program within the School of Education offers a variety of degree concentrations that help practicing teachers develop as equity-forward, content knowledge and pedagogical leaders. To learn more about ASTL degree options, please visit the program website.