Too often within the education system, children from a young age are placed into a category identifying their privilege. Privilege, for students in the United States, means recognizing gaps in academic achievement between middle-class Whites and low-income minority youth. However, these questions still remain-should the color a child’s skin or how much their family earnings determine the success in school? For years, numerous researchers and scholars have studied the following factors that create difference in achievement (focusing on test scores) in relation to social status.
Throughout our research, looking at factors with difference in achievement and social status allowed us to look further at lower class characteristics within urban communities. With no hesitation, achievement factors in these communities were heavily expressed further. Factors included the following…
- Health Matters- In urban communities, students with poor health have effects with their health. For example, children with poor vision have disadvantages within the classroom (seeing materials, boards, and/or peers/teacher). In addition, many urban families have less adequate pediatric care causing more frequent absence (Carter and Welner 62).
- Lack of Affordable Housing- Student mobility is an important cause of low achievement in many urban districts. If a child has trouble finding “passable” housing, this could mean a child is frequently moving and/or only remain in the classroom for short amounts of time. This causes teachers to repeat content for the benefit of the students that miss often, but fallback for the overall class (no progression) (62).
- Involvement: Parenting Style- On average, children who are raised by non- college educated parents “follow instructions and never question authority” (63). Parents in urban communities lean towards the direct fashion of parenting compared to college-educated parent that result to critical thinking explanation of parenting. (63).
- English Language Learners:
During testing, there are many different factors to take into account especially when looking at the student body as a whole. There are many students that need different kinds of accommodations especially students who are still learning English as their second language; these students are called English Language Learners or ELL students. In schools there are different supports that can be present to aid ELL students such as aides, paraprofessionals, and translators at times. However, this does not always become the case for students who live in impoverished areas, and it can sometimes lead to some errors in the ways that ELL students are monitored. According to a discussion set forth by Rebecca J. Kopriva, David E. Wiley, and Jessica Emick in their work “Inspecting the Validity of Large-Scale Assessment Score Inferences for ELLs and Others under More Optimal Testing Conditions—Does it Measure Up?”, the authors demonstrate that generally teachers in the classrooms do not give a great measure of how well ELL students will do on standardized exams (Kopriva et al. pg. 34). In order to be able to interpret how ELL students progress in the classroom or in exams, it is important to be consistent, and sometimes that isn’t possible, which becomes a big problem when looking at standardized test scores.
In order to see if there was a relationship between the percentage of ELL students and achievement scores, it was important to use the information given by the state of Connecticut and to be able to see if there was a direct statistical relationship present between the two factors. From the Connecticut Open Data portal, we pulled two district level spreadsheets from the 2012-2013 school year: one was a spreadsheet that had educational indicators, which includes the number and percentage of ELL and special education students, and another spreadsheet that included data on achievement on the CMT. After merging the two data sets, we were able to plot them in a scatter plot and use a trendline to indicate a pattern, if any, in the placement of districts in the data. When first mapped, clearly there was a relationship in what percentage of ELL students were in the district and how well the average District Performance Index (or DPI) was, the higher percentage of ELL students present in the district the lower the average DPI score. In order to see if this relationship was statistically significant and not just a random coincidence, we also ran a P-value test which allows us to measure how statistically significant the relationship is. After adding in our r-value of 0.74632 and our 165 defined features, our P-value came to 0.001 which means that the relationship is considered to be statistically significant and not by chance. In conclusion, this means that the percentage of students who are identified as ELL and DPI scores do have a statistically significant relationship that should be explore further, and areas of higher need should be able to seek more resources to help this relationship improve.
Becker, Bronwyn E., and Suniya S. Luthar. “Social-Emotional Factors Affecting Achievement Outcomes Among Disadvantaged Students: Closing the Achievement Gap.” Educational Psychologist 37.4 (2002): 197-214. Web.
Carter, Prudence L., and Kevin G. Welner. “Building Opportunities to Achieve.” Closing the Opportunity Gap-What America Must Do to Give Every Child an Even Chance (2013): 61-75. Oxford University Press. Web.