This is an article review on Julie E. Learned’s 2016 article entitled “The Behavior Kids.”
Article: “The Behavior Kids,” By Julie E. Learned, 2016.
Evidence from a case study by Learned (2106) suggest that lower socio-economic status, ethnicity and disability label are interrelated with each other and with lower educational success rates and higher discipline rates in students identified as struggling readers in the ninth grade. As Learned (2016) states
“Economically disadvantaged students were 48% of the district’s population but received 85% of suspensions; African Americans were 19% but received 60% of suspensions. These trends were pronounced at Moore High, which had the highest enrollment of economically disadvantaged students and youths of color in the district and the highest number of suspensions (584) and referrals (2,109). In total, 14% of Moore’s students were suspended during the year (p. 1295).”
Learned (2016) related this to the policy at Moore HS (fictional name of the school where the study took place) that labels students as struggling readers and placing these students into remedial classes based not only on reading levels but on behavioral concerns as well “..which drove tracked scheduling on the basis of ability and behavior (p. 1284).” The most recent data on the school’s performance was reported by Learned (2106), “In 2012, the school reported the lowest test scores and highest poverty rate (57% economically disadvantaged) in the district and county, and the state’s Department of Public Instruction ranked it at the second lowest accountability rating, ‘‘meets few expectations (p.1279).’’ This coupled with findings by Callahan (2005, as cited in Learned 2016) and Oakes (1995, as cited in Learned 2016) noting that minority students are disproportionately placed in lower educational tracks and that the students, regardless of actual achievement or reading levels, demonstrated lower academic gains compared to the higher-level tracks (p. 1274). Students that are identified as struggling learners and placed in these lower-tracks are also more likely to be referred and identified through special education leading them to be further segregated from their non-disabled peers (Ford & Russo, 2016). It is not only a racial or income inequity that faces many of the students in public schools, it is the realization that students in these subgroups also face an increased likelihood of being identified as a student with a disability and being coded (or labeled) as a special education student (Ford & Russo, 2016). The evidence provided in Ford and Russo’s (2016) study showed that black students are two times more likely to be referred and identified as a student with a disability through special education. Black students, once identified as special education students, are also more likely to be educated in a more restrictive environment that is separate from their non-disabled peers (Ford & Russo, 2016).
Funding for schools, specifically campuses within the district was also a concern. As an example, provided by Learned (2016),
“although 39% of Moore High compared to 62% of Forest High scored at proficient reading levels on the 2012–2013 state assessment, the schools received equal funding for literacy, which Ms. Long described as ‘‘inequitable.’’ She explained, ‘‘If we used Forest High’s cut scores, then like 60% of our kids would be in reading,’’ which was untenable given the available resources. Tracing the uses of funding across contexts shows how struggling reader identification was simultaneously institutional, as funding compelled schools to identify youths, and locally relative, as what qualified a youth for intervention in one school did not necessarily in another (pp. 1284-185).”
Yet this is not an issue isolated to Moore HS and the district it resides in, a study by Baker and Ramsey (2010) provided strong evidence that there is no evidence of a relationship between the prevalence of students identified with disabilities and the method of funding.. Furthermore, students with disabilities are not uniformly distributed between districts. The evidence showed that there is high geographic clustering of students with disabilities and that there is a strong relationship between poverty rates in a geographical location and disability rates (Baker & Ramsey, 2010).
In the public education system in the United States the long standing theory is that “..learning difficulty that characterize deficits as within-person, stable traits (Vygotsky, 1993 as cited in Learned, 2016, p. 1274).” Systematic change in public schools needs to occur in order to change this way of thinking. As Learned (2016) puts it “To understand youth reading difficulty, then, requires attention not only to individuals’ skills and practices but also to system-wide contexts that make apparent, if not produce, learning difficulty (1274).” Another way to understand this thinking is that the problem, and the solution, resides not only in the student but with the educators. Part of the systematic issues with the public education system that add to this issue lies in school discipline policies. Learned (2016) suggests “Like tracking, school discipline policies also serve as institutional contexts that can exacerbate or cause learning difficulty (1274).” A study by Skiba and colleagues (2104, as cited in Learned, 2016) found that the strongest predictor of campus level variables in discipline rates were based on the campus administrator’s perspective on discipline.
Specific to the students selected for this study, Learned reported “In April and May, two youths were expelled, and two stopped attending school (p. 1281).” A couple of aspects that I found disturbing were that the district/campus had no procedures for notifying students or parents that they were identified as struggling readers and therefor placed in remedial classes. With this, once a student was identified and placed on this remedial track it was very difficult if not impossible to be removed or exit from this label and schedule (Learned, 2016. P. 1285). This labeling of students led to students identifying themselves in relationship with their deficit labels. Although some students in study expressed power by protesting their labels, the students that requested to exit the intervention (three students attempted to exit) they were not able to exit the intervention due to systematic policy in the district that prevent exit through scheduling (Learned, 2016. p. 1285).
Learned (2016) reported findings through these case studies that students, regardless of being labeled as “..deficient readers and deviant youths…continually sought to reposition themselves as learners and readers (p.1283).” The labeling of students as student’s with reading deficits or behavioral problems was not an exact science and relied heavily of subjective information reported by previous year teachers (Learned, 2016. P. 1284). Part of this subjective information that placed students in intervention classes relied on a perceived notion of student engagement (Learned, 2016, p. 1289). Even when students excelled in other core subjects, they were unable, though requested, to change their schedules and exit from the intervention class (Learned, 2016, pp. 1290-1291). Although teachers strived to educate all students, they felt “..their efforts for change were limited as they interacted among complex district-wide networks (Learned, 2016, p. 1287). Ultimately, Learned (2016) found that student achievement was not affected, however discipline rates increased and the teachers felt more a need for compliance with the program requirements and disciplinary regulations imposed upon them to enforce were more important than teaching their students (Learned, 2016, pp. 1294-1295, 1297, 1300). Learned (2016) provided evidence through data analysis that indicated all eight of the students in this case study “..demonstrated proficient or improved reading at some point (p.1298).” In defiance of a system that seemed designed to unfairly designate students as learners with deficits in reading and behavioral problems by placing them in intervention classes based on highly subjective evidence, students were still able to perform.
References:
Baker, B. D., & Ramsey, M. J. (2010). What we don’t know can’t hurt us? Equity consequences of financing special education on the untested assumption of uniform needs. Journal of Education Finance, 35(3).
Ford, D. Y., & Russo, C. J. (2016). Historical and legal overview of special education overrepresentation: access and equity denied. Multiple Voices for Ethnically Diverse Exceptional Learners, 16(1), (pp. 50-57). http://libezp.nmsu.edu:2138/ehost/detail/detail?vid=5&sid=51cc9bb7-a34c-4126-953e-32360aea0e9c%40sdc-v-sessmgr03&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#AN=116917085&db=ehh
Learned, J.E. (2016). “The Behavior Kids”: Examining the conflation of youth reading difficulty and behavior problem positioning among school institutional contexts. American Educational Research Journal. 53(5), (pp. 1271-1309). https://libezp.nmsu.edu:2225/doi/full/10.3102/0002831216667545