**3. Evidence supporting theory**

The importance and value of studying factors in urban schools and school systems in the South that disproportionately disadvantage African American students has been demonstrated (cf. Cartledge, Yurick, Singh, Keyes, & Kourea, 2011; Morris & Monroe, 2009; Lo, Mustian, Brophy, & White, 2011). In recent research, we documented that increases in the academic potential of a school serve as a protective feature decreasing the negative influence or "power rating" of low socio-economic characteristics as reflected in the community capital of a school; we used aggregated composites of community and school characteristics in a large southeastern city to predict school-level (*N* = 80) achievement (Porfeli, McColl, Wang, Algozzine, & Audette, 2011). We provide a summary of our work in the rest of this chapter.

#### **3.1 Context**

We completed our research in one of the top 25 largest public school systems in the United States (cf. Sable & Hoffman, 2005; Sable, Plotts, & Mitchell, 2011) and the largest in the state, with an enrollment of over 125,000 pre-kindergarten through 12th grade students. The ethnic distribution was approximately 43% African-American, 40% White, 10% Hispanic, 4% Asian, and 3% Native American or Multi-Racial. The elementary schools we studied were located in urban, suburban, and rural neighborhoods.

The total number of students enrolled in participating schools was over 55,000, and among them, 18% were kindergarteners, 17% were first graders, 16% were second graders, 16% were third graders, 17% were fourth graders, and 16% were fifth graders. The average student enrollment in the schools was 629 (*Range*=226-1372). Minority enrollments (65%) as well as socioeconomic (53% free or reduced lunch) and second language markers (6% students with limited English proficiency) reflect the overall district demographics and represent similar characteristics to those of the 100 largest public elementary school districts in the United States (cf. Sable & Hoffman, 2005; Sable, Plotts, & Mitchell, 2011). We obtained data from publicly-accessible web-based resources maintained by the school district. This information included many variables (e.g., percentage of student passing the reading and mathematics state-wide standardized tests, percentage of parents with incomes below \$25,000, percentage of students within each category of ethnicity groups, percentage of students identified as gifted and as having disabilities) including indicators of the community capital, academic achievement, and the student potential at the school level as well as control factors with potential moderating effects.

We defined community capital using a combination of variables that reflected financial, human, and social conditions of schools in our study. *Financial capital* was the percent of children receiving free or reduced lunch and the percent of parents who earn less than \$25,000. *Human capital* was the percent of mothers with some college education. *Social capital* was the percent of parents' attending parent-teacher conferences and volunteering in the school, the percent of children who reside is single parent households, and the percent of parents with limited English proficiency. Our measure of community capital also included elements pertaining to the neighborhoods and surrounding areas in which the school was expected to educate children. School-level academic potential was operationalized as the percent of children with a "gifted" classification and the percent of children with a recognized disability that could interfere with academic development (e.g., learning and other disabilities). We added the percent of African American children in each school and the total enrollment of each school to the regression models to represent the potential confounding impact of race and school size on the prediction model (see below). Others have used information on these variables similarly in research on predictors of academic achievement (Coladarci, 2006; Ilon & Normore, 2006; Olneck, 2000; Sirin, 2005).

We were interested in the characteristics of schools rather than the characteristics of students in them as predictors of overall achievement. The percent of students at the school who passed state-mandated end-of-grade reading and mathematics tests was available for these analyses. We calculated the difference between current and previous pass rates and used it to estimate the progress that schools made across a single academic year.

The end-of-grade reading test assessed components of the state's Standard Course of Study. The test consisted of eight reading selections with three to nine associated questions for each selection. Each student was asked to read five literary selections (two fiction, one nonfiction, one drama, one poem) and three informational selections (two content and one consumer). The variety of selections on each form allowed for the assessment of reading for various purposes: for literary experience, to gain information, and to perform a task.

The end-of grade mathematics test assessed the goals and objectives in the state's Standard Course of Study. On the test, students demonstrated knowledge of important principles and concepts, and related mathematical information to everyday situations. In order to align with the mathematics curriculum's addressing inquiry instruction and higher-order thinking, the test had an increased focus on processing information and higher-order thinking.

For the purposes of our research, we averaged the math and reading achievement to yield an overall indicator of the academic achievement of each school, because the correlation between reading and math pass rates for the elementary schools was 0.92. Our achievement variable reflected the average passing rate for each school on the mathematics and reading test and was used as an estimate of the "performance" of the school.

We employed a correlation design to test a theoretical model suggesting that community and school characteristics influence academic outcomes. Since we were interested in identifying the magnitude of the relationship between academic outcomes and other characteristics such as the community capital and academic potential, a multiple regression technique was used to investigate these relationships and to identify the relative impact of the characteristics on school-level academic outcomes independent of two control variables believed to influence achievement. Given that we were also interested in assessing the moderating impact of academic potential on the relationship between community capital and academic achievement, an interaction term (community capital by academic potential) was also included in the regression model.

#### **3.2 Findings**

22 Learning Disabilities

The importance and value of studying factors in urban schools and school systems in the South that disproportionately disadvantage African American students has been demonstrated (cf. Cartledge, Yurick, Singh, Keyes, & Kourea, 2011; Morris & Monroe, 2009; Lo, Mustian, Brophy, & White, 2011). In recent research, we documented that increases in the academic potential of a school serve as a protective feature decreasing the negative influence or "power rating" of low socio-economic characteristics as reflected in the community capital of a school; we used aggregated composites of community and school characteristics in a large southeastern city to predict school-level (*N* = 80) achievement (Porfeli, McColl, Wang, Algozzine, & Audette, 2011). We provide a summary of our work in

We completed our research in one of the top 25 largest public school systems in the United States (cf. Sable & Hoffman, 2005; Sable, Plotts, & Mitchell, 2011) and the largest in the state, with an enrollment of over 125,000 pre-kindergarten through 12th grade students. The ethnic distribution was approximately 43% African-American, 40% White, 10% Hispanic, 4% Asian, and 3% Native American or Multi-Racial. The elementary schools we studied were

The total number of students enrolled in participating schools was over 55,000, and among them, 18% were kindergarteners, 17% were first graders, 16% were second graders, 16% were third graders, 17% were fourth graders, and 16% were fifth graders. The average student enrollment in the schools was 629 (*Range*=226-1372). Minority enrollments (65%) as well as socioeconomic (53% free or reduced lunch) and second language markers (6% students with limited English proficiency) reflect the overall district demographics and represent similar characteristics to those of the 100 largest public elementary school districts in the United States (cf. Sable & Hoffman, 2005; Sable, Plotts, & Mitchell, 2011). We obtained data from publicly-accessible web-based resources maintained by the school district. This information included many variables (e.g., percentage of student passing the reading and mathematics state-wide standardized tests, percentage of parents with incomes below \$25,000, percentage of students within each category of ethnicity groups, percentage of students identified as gifted and as having disabilities) including indicators of the community capital, academic achievement, and the student potential at the school level as

We defined community capital using a combination of variables that reflected financial, human, and social conditions of schools in our study. *Financial capital* was the percent of children receiving free or reduced lunch and the percent of parents who earn less than \$25,000. *Human capital* was the percent of mothers with some college education. *Social capital* was the percent of parents' attending parent-teacher conferences and volunteering in the school, the percent of children who reside is single parent households, and the percent of parents with limited English proficiency. Our measure of community capital also included elements pertaining to the neighborhoods and surrounding areas in which the school was expected to educate children. School-level academic potential was operationalized as the

**3. Evidence supporting theory** 

located in urban, suburban, and rural neighborhoods.

well as control factors with potential moderating effects.

the rest of this chapter.

**3.1 Context** 

Community capital was the strongest independent predictor of school-level academic achievement, with profound predictive power (*R*2 = .81) particularly given that the model

Achievement Gaps: Learning Disabilities, Community Capital, and School Composition 25

Using Coleman's (1988) concept of "public good," we believe it is important to consider all capital that is available to the school and the learning environment (i.e., community capital). As Sirin (2005) argues, the capital of a neighborhood should also be considered. This ideally captures the socioeconomic status (SES) of the neighborhood, which reflects the potential of businesses and residents to contribute resources to the school. Families, for example, can share financial capital with the school through support of the parent-teacher organization or through other opportunities to directly donate resources. Parrish, Matsumoto, and Fowler (1995) illustrate this fiscal capacity in their study that found that the higher neighborhood SES, measured by the value of owner-occupied housing and residents' educational attainment, correlated significantly with greater school expenditures per student. Another element of the neighborhood capital is the stability of the neighborhood and its capacity to create societal norms that may have an impact on the school. Coleman also includes safety as an element of social capital. A neighborhood in which parents feel that other adults will keep their children from harm's way has more social capital than one in which parents are

Our research supports a hypothesized interactive impact of community capital and academic potential on achievement. The academic potential of the student body reduced the "power rating" of community capital (Coladarci, 2006). Moreover, the overall achievement gap between schools is slightly narrowing, but is being largely offset by the declines in the impoverished, higher potential schools. Academic achievement change at the school level may, therefore, hinge on the issue of consistency when comparing configurations of higher or lower community capital and higher or lower academic potential. If a school with higher potential is situated in a neighborhood with more capital or a school with lower potential is in neighborhood with less capital, then the schools exhibit changes not exceeding ±1.3%. If a school with higher potential is situated in a lower capital neighborhood or a school with lower potential is in a higher capital neighborhood, then the achievement change across time is consistently a negative number (in the range of -1.7 and -4.3%). Where you go to school matters when summative and formative comparisons of performance are used to

The large urban district school examined in our study, therefore, appears to be making good progress in impoverished schools with high concentrations of students with disabilities, but they may be leaving their schools with higher concentrations of gifted students in impoverished neighborhoods behind. On the contrary, the district school is having greater success maintaining their achievement levels in wealthier schools with higher concentrations of gifted students, but the district may be struggling to meet the needs of wealthier schools with higher concentrations of students with disabilities. The interactive influences of community characteristics and school composition represent a clear area for

In addition to considerations related to student assignment, concepts of community capital could be incorporated into many kinds of decisions made by local boards such as location of schools, approaches to financing schools, and community and parental involvement approaches to more equitably distribute these resources. By choosing to pay attention to

community capital, the board can refine its decision making in these areas.

fearful for the safety of their children.

drive policy and decision making.

**4.1 Implications for the use of community capital** 

further investigation.

did not directly account for aspects of the teaching situation (e.g., qualifications of the teachers or quality of the instruction) that schools aim to change as a way of boosting academic achievement. In other words, predictors that personnel at the school-level are generally unable to influence accounted for about 81% of the variability in achievement across the 80 elementary schools. Our regression results also suggested that the academic potential of the student body moderates the relationship between community capital and overall academic achievement while controlling for the total enrollment and race of the study body of the schools.

The community capital influence was greater in schools with less academic potential and weaker in schools with more potential. Recalling that academic potential is a construct combining the percentage of student with academic gifts and those with academic disabilities, overall academic achievement of schools was *more* influenced by community capital in schools that had a *lesser* fraction of gifted children and *greater* fraction of children with disabilities than schools reflecting the reverse proportions. Although statistically significant, the impact of this moderating influence was relatively weak. Schools with the least amount of community capital and academic potential demonstrated the *greatest gains* in overall academic achievement, *yet* the schools with the least amount of community capital and relatively high academic potential demonstrated the *greatest declines* in overall academic achievement. The schools in the wealthiest communities (e.g., 1 standard deviation above the mean on the community capital variable) generally demonstrated declines, with the greatest decline occurring in those schools with lower academic potential.
