Are U.S. Schools Closing the “Gifted Gap”? Analyzing Elementary and Middle Schools’ Gifted Participation and Representation Trends (2012

Abstract

This article tests hypotheses by examining variations in the percentage of elementary and middle schools offering gifted and talented programs as well as gifted student participation and representation between 2012 and 2016. Using the Office of Civil Rights and the National Center for Educational Statistics (NCES) Common Core data, we find that between 2012 and 2016, the percentage of schools with gifted programs declined slightly. Crucially, gifted participation is increasing faster in low-poverty schools than in high-poverty schools. Furthermore, suburban schools became more likely to have gifted programs than urban, rural, or town schools. However, gifted participation by urbanicity decreased across all four locales. Using only 2016 data, we show that students who are Black and Hispanic continue to be statistically underrepresented. We conclude with a brief discussion and policy implications.

Keywords

gifted gifted and talented participation representation race low-income high-poverty local norms universal screening policy education policy trends gifted gap

Introduction

This article examines variations in the percentage of elementary and middle schools offering gifted and talented (GT) programs as well as gifted student participation and representation between 2012 and 2016. Gifted programing may be in decline because of political backlash as well as the cyclical nature of interest in gifted programs. According to Jolly (2009), “no other special group of children has been so alternately embraced and repelled with so much rigor by educators and laypersons alike” (p. 37). This has led to “either or” propositions, such as the recent recommendation by a panel of investigators to eliminate all gifted programs in New York City public schools (Shapiro, 2019). Jolly (2009) also notes that the discourse on excellence and equity often mirrors the pendulum swing of society’s priorities. When there is a perceived need to compete in science internationally, excellence is sought and GT programs become a national priority. As public attention moves toward equity, however, political support for gifted education may weaken as opponents of the programs often frame them as vehicles of inequity.

The objective of this study is twofold—first, we analyze variability in access to GT programs among all public elementary and middle schools in the United States. Then we examine variability in representation and participation of students from low-income and minority backgrounds in GT programs during the study period. Drawing from the recent history of accelerated programming in the United States, we develop three hypotheses about how GT programs may have changed in recent years and test them by analyzing national data and conducting descriptive analyses. Specifically, we test hypotheses related to changes in the prevalence of gifted programming, overall GT participation, and GT participation by students from underrepresented groups.

The layout of the article is as follows. First, we begin with a brief historical background that lays out the history of differentiation programs and the controversies around “tracking.” Next, we review the literature on the participation of students from underrepresented groups in gifted programs. With this background in mind, we then present our research questions and hypotheses before explaining our data sources and research methods. Then we lay out our findings in the following sections, concluding by discussing study limitations, offering discussion of policy implications, and suggesting future research directions.

A Brief Historical Background

Differentiation and Tracking

Debates in the United States about whether and how to differentiate programming for students go back to at least 1892, when The Committee of Ten, spearheaded by the then Harvard President Charles Eliot, put forward a list of recommendations in response to a mismatch between high school curriculums and college admission requirements (Bohan, 2003; Dexter, 1906; Loveless, 1999). The Committee, which recommended that all students study a general, undifferentiated curriculum, based its proposals on what it considered to be the goals of education and its conviction that all students in a democracy should have a broad education. Twenty-six years later, the National Education Association (NEA) released its Cardinal Principles of Secondary Education, which, among other things, favored a differentiated curriculum in schools. In contrast to the recommendations of the Committee of Ten, the Cardinal Principles argued for curricula to meet the needs of different types of students, as its supporters believed that “cultivating student’s intellectual powers” was essential only to a few students who would go to college (Loveless, 1999, p. 32). Since this era, it has been common for American schools to offer differentiated programs (Gamoran, 2010; Wheelock, 1994).

In more recent years, the so-called “tracking wars” have seen debates among parents and educators on the value of differentiated programming and its impacts on achievement gaps. In her seminal 1985 book Keeping Track: How Schools Structure Inequality, Jeannie Oakes identified inequality as a leading problem in education and “tracking,” which she defined as “the process whereby students are divided into categories so that they can be assigned in groups to various kinds of classes,” as a key driver of inequality (Oakes, 2005, p. 3). In fact, tracking often refers more specifically to the use of differentiated programming with relatively rigid grouping and long timespans, such as when students are separated into academic and vocational pathways that persist over years. For example, Lucas (1999) refers to tracking as “the practice of dividing students into programs that rigidly prescribed their course of study and that admitted little opportunity for mobility from program to program” (p. 1).

The “tracking wars” both elevated these issues and prompted many equity-minded educators to oppose the concept of different classes for students achieving at different levels. For example, the National Governors Associations, the NEA, and the National Council of Teachers of English (NCTE) have recommended complete elimination of rigid, non-flexible tracking on the grounds that the practice was inequitable (Worthy et al., 2009). Still, objections to the elimination of differentiated programs have come from educators and parents who believe that cutting the programs would compromise the education of academically exceptional students or high achievers (Oakes, 2005). In California during the late 1980s, the California Association for the Gifted (CAG) also opposed the elimination of separate accelerated programs, arguing that gifted children deserved to have access to programs that challenged them (Loveless, 1999). Advocates asserted that “gifted students were as far from the norm and as ‘needy’ of separate learning environments as low-IQ students” (Oakes, 2005, p. 221). While some programs were merely re-branded, reforms during this era led to the elimination of programs or increased flexibility (Oakes, 2005). Some GT programs would therefore be regarded as flexible “ability grouping” rather than rigid “tracking.”

When differentiated programs were eliminated, schools sometimes found that the changes had unintended consequences. Scholars have pointed out that, ironically, the dismantling of formal tracking programs may have contributed to the “increased informational gap between parents of middle- and upper-middle-class students and parents of low-income and minority students by removing clarity about the system that once existed” (Worthy et al., 2009, p. 227). More fundamentally, Loveless (1999) argues that the concerns about increased inequality due to tracking do not align with the data. Schools that moved to eliminate differentiation in the 1980s were doing so as measures of educational inequity, such as the black–white test score gap, were falling. Yet in the period after the mid-1980s, when Oakes and other reformers successfully worked to eliminate many forms of differentiation, measures of inequality rebounded and inequality continued to increase throughout the 1990s.

Participation of Students From Underrepresented Groups in Accelerated Programming

Given persistent achievement gaps, much recent work on the connection between differentiation and inequality has focused on the various methods and processes used to identify students for accelerated programming (Callahan et al., 2017; Card & Giuliano, 2016; Ford, 1998). An important theme is that many of the methods and processes used to identify students for GT programs may under-identify low-income and minority students who would benefit from acceleration. For example, teacher referrals and the use of norm-referenced standardized tests or some form of intelligence test for screening purposes is known to weed out students from low-income and traditionally underrepresented backgrounds because these tests may not reflect these students’ life experiences and cultures (Grissom et al., 2017).

To shed more light on teacher referrals as a method of identification, Lamb et al. (2019) use an inequity score to reveal that “increases in the percent of White teachers is associated with lower [inequity score], which indicates greater inequity in identifying Hispanic students for GT programming” (p. 11). These inequities were consistent across all district locales (city, suburban, town, and rural). Similarly, Grissom and Redding (2015) found that students who are Black are referred to gifted programs, particularly in reading, at significantly lower rates when taught by non-Black teachers. These findings illustrate important challenges and issues with teacher referral systems. If school districts are to improve the diversity and equity of their gifted programs, other identification and screening methods need to be emphasized. For example, Card and Giuliano (2016) showed that when schools utilize universal screening procedures without changing standards for gifted eligibility, there was a large increase in gifted participation and representation of economically disadvantaged and minority students.

Ford (1998) also illustrated concerns over the recruitment and retention of students from minority backgrounds in gifted programs. She noted that school and student-related factors that inhibit identification and retention of underrepresented groups include identification instruments and screening procedures, lack of proper teacher training in gifted and urban education, peer pressure from other minority students, as well as fear of social isolation in predominantly white gifted programs. Similarly, Yoon and Gentry (2009) attributed the disproportional representation of students in GT programs to varied definitions of giftedness, identification procedures, and identification policies.

Applying similar methods to Yoon and Gentry, Peters, Gentry, et al. (2019) find that gifted disproportionality has continued with a few state-level exceptions. Using a Representation Index (RI), an index that denotes the degree to which a certain group of students is represented in the gifted population compared with the total student population, they reported that at the national level, “African American, Latinx, and Native American students remain underrepresented (2016 RIs of 0.57, 0.70, and 0.87, respectively) and European American and Asian American students remain well represented (RIs of 1.18 and 2.01, respectively)” (p. 276). In addition, students with limited English proficiency (LEP) and those who received services under IDEA also continue to be underrepresented in gifted programs. They also note that “simply having state mandates does not appear to translate to proportionality” (p. 280).

To analyze variation in gifted identification within a southern, urban public school district that used a cognitive test for identification, Carman et al. (2018) concluded that the use of Cognitive Abilities Tests (CogAT) exhibited demographic group differences which leads to disproportional identification. They suggest that instruments that

Display group differences (which includes all cognitive-related tests) should only be used as part of a GT identification process with the understanding that users may not achieve the results they seek (proportional identification) solely through the use of a nonverbal instrument. (p. 204)

Other scholars (Hodges, Tay, Maeda, & Gentry, 2018) suggest that to have proportional identification, traditional methods of identification (i.e., IQ and standardized achievement tests) should be used in combination with nontraditional methods of identification (i.e., nonverbal tests, student portfolios, affective checklists). Despite concerns about the limitations of using standardized tests for gifted screening, school districts continue to over rely on these methods for identification. Callahan et al. (2017) used three leveled surveys of 1,566 school district personnel respondents (elementary, middle, and high school) in separate school districts and found that “sixty percent of the district coordinators reported using a predetermined score or percentile on an intelligence/aptitude test or achievement test as the qualifying criteria for receiving gifted education services automatically” (p. 29).

Other studies have also consistently documented the academic achievement gaps that exist among racial/ethnic subgroups and low-income students (Evans, 2005; Flores, 2007). While the reasons for the disparities are likely multiple, overwhelming evidence shows that Hispanic, Black, and students from low-income backgrounds tend to enter school less prepared than their White, Asian, and more affluent peers (Flores, 2007). Differences in resources, family structure, and access to quality educational programs have consequences that likely drive these disparities in academic preparation, with later consequences for who will be identified for acceleration.

A related issue is the wide variation in the achievement level of students in any given classroom, school, or grade. Firmender et al. (2013) analyzed 1,149 students in five diverse elementary schools, including a GT magnet school, and found a range in reading comprehension of nine grade levels among third graders. Among fourth graders in those schools, the range was 11 grade levels. These inequalities may come in various forms including “excellence gaps”—differences between subgroups of students performing at the highest level (Plucker & Peters, 2020). Plucker et al. (2013) have argued that underrepresentation (lack of equity) has contributed to large and growing excellence gaps. Peters and Engerrand (2016) have argued for a scalable method that could help balance the seemingly competing goals of equity and excellence. To some, the development of excellence is all that matters. However, research evidence suggests that excellence and equity do not need to be at odds (Harris & Plucker, 2014; Wright et al., 2017).

Studies (notably Card & Giuliano, 2016) have shown that it is possible to combat underrepresentation while also maintaining rigorous standards. Screening procedures based on recommendations and referrals may discriminate against students from low-income and minority backgrounds whereas universal screening may increase their participation (Card & Giuliano, 2016). Other alternatives that might mitigate underrepresentation of students from low-income and minority backgrounds include nontraditional identification methods (Hodges, Tay, Maeda, & Gentry, 2018). Hodges et al. provide evidence that nontraditional methods narrow the proportional identification gap between underrepresented and represented populations. However, they contend that nontraditional methods alone do not address the issue of education inequity. To address education inequity and effectively deal with disproportionality, they suggest the use of nontraditional methods of identification in conjunction with other pathways.

With the lessons of the “tracking wars” in mind, it is worthwhile to assess the extent to which access to GT programs for low-income and minority students may be increasing or decreasing over time as well as potential changes in the prevalence of schools offering gifted programs. Pointing to connections between policy, funding, and inequality, Kettler et al. (2015) found that only Florida and Virginia had “policies and plans to provide equitable funding for gifted education to ensure equitable distribution of programming opportunities” (p. 102). They showed that rural schools, small schools, and schools with larger economically disadvantaged populations allocate less fiscal and human resources to gifted services. Indeed, schools in rural areas have unique challenges and the complexities of rural gifted education require nuance. Rasheed (2020) reviewed the literature on rural gifted education and noted that funding constraints in many rural schools imply that gifted testing is only offered to those students who are referred for gifted identification. Puryear and Kettler (2017) compared gifted opportunities in rural versus non-rural districts in Texas, finding that “gifted and potentially gifted students living in rural locations tend to have fewer opportunities for gifted education services than students living in nonrural locales” (p. 151). Lawrence (2009) conducted a comprehensive review of the literature from 1990 to 2003 and found that students in rural settings are less likely to be identified as gifted and generally have fewer opportunities for gifted education services. Comparing types of non-rural schools, Baker (2001) found that students in districts that are large, suburban, or both large and suburban, are more likely than those in urban districts to have access to GT programs.

Focusing on student factors, Peters, Gentry, et al. (2019) describe trends in gifted participation by student race/ethnicity, LEP status, and students with disabilities, finding that participation for these groups varies based on state gifted mandates such as whether a state mandates gifted identification, accelerated services, or both. The present study extends this work by describing trends in access to and participation in GT programs for schools of different sizes, with different levels of student poverty, and different settings (urban, suburban, town, and rural). We also analyze race/ethnicity trends specific to high-poverty schools to determine whether they differ from overall national trends. By contextualizing these trends, we are able to explore the extent to which school and locational factors may impact GT programs.

Research Questions and Hypotheses

The objective of this study is to evaluate variability in access to gifted programs among all public elementary and middle schools in the United States. Then analyze variability in representation and participation of students from low-income and minority backgrounds in gifted programs. To investigate, we developed the following research questions and hypotheses:

Research Questions

Research Question 1: Are elementary and middle school gifted programs becoming less common between 2012 and 2016? If so, to what extent?

Research Question 2: To what extent did overall gifted student participation decline between 2012 and 2016?

Research Question 3: During the same time period, is there a decrease in gifted student participation and representation of students who are Black and Hispanic in high-poverty schools? If so, to what extent?

Hypotheses

Hypothesis 1: The proportion of schools with gifted programs has not changed overtime. In other words, there is no difference between 2012 and 2016.

Alternative Hypothesis 1: The proportion of schools with gifted programs has fallen between 2012 and 2016.

Hypothesis 2: The proportion of students enrolled in gifted programs, especially in high-poverty schools and across locales, has not changed between 2012 and 2016.

Alternative Hypothesis 2: The proportion of students enrolled in gifted programs, especially in high-poverty schools and across locales, has decreased between 2012 and 2016.

Hypothesis 3: In high-poverty schools, racial participation and representation of Black and Hispanic students in gifted programs has not changed between 2012 and 2016.

Alternative Hypothesis 3: In high-poverty schools, racial participation and representation of Black and Hispanic students in gifted programs has decreased between 2012 and 2016.

Data and Methods

We conduct national analyses of GT programs for all public elementary and middle schools using data from two sources: The Department of Education’s Office for Civil Rights (OCR), and the National Center for Educational Statistics (NCES).

The OCR data include gifted program enrollment for each school by student racial/ethnic group, and the NCES data include the proportion of students who qualify for free and reduced-price lunch and school enrollments for each racial and ethnic group. First, we appended the 2011–2012, 2013–2014, and 2015–2016 OCR GT enrollment data.¹ Then, we merged the 2009–2010, 2010–2011, 2011–2012, 2012–2013, 2013–2014, 2014–2015, and 2015–2016 NCES school data on enrollment and other school-level data. As data describing the percentage of students qualifying for free or reduced-price lunch (FRPL) were missing for 2.7% of elementary and middle schools in the years of OCR data we analyze, for these data points, we use the most recent year of data available in NCES, or, when the “neighboring” years are available and not the same year, we use an average of the neighboring years. After preparing these data sources, we then merge them using the unique identifiers in both data sets.

Next, we divided gifted enrollment by school enrollment for each student subgroup and for the school overall to calculate the percentage of students enrolled in gifted education. School poverty is gauged by participation in the FRPL program.² We follow NCES’s poverty classification, which defines low-poverty schools as having no more than 25% of students on FRPL, middle-poverty schools between 25% and 75.0%, and high-poverty schools as those having 75.0% or more (NCES, 2020).

Our national sample includes 197,405 school-year observations of elementary and middle schools, 185,072 of which represent schools with more than 20 students per grade and are therefore included in all figures.³ These observations represent 70,538 unique elementary and middle schools. More than one quarter of the national sample (26.8%) are high-poverty schools, 19.2% are low-poverty, and a majority (54.0%) are classified as middle-poverty.

For school-level analyses (e.g., in calculating the percentage of schools with gifted programs), we exclude very small schools—those with fewer than an average of 20 students per grade, as these schools are much less likely to have gifted programs and, by definition, serve fewer students.⁴

Findings

Section I: Availability and Access Overtime

During the period from 2012 to 2016, Figure 1 below shows that, the percentage of elementary and middle schools with gifted programs declined slightly from 69.5% of schools in 2012 to 68.5% of schools in 2016.

Figure 1.

Proportion of schools with gifted programs between 2012 and 2016 (down very slightly—average school N = 61,735). Source: 2012 – 2016 Office for Civil Rights data.

In Figure 2, we see that in the schools with gifted programs, participation declined very slightly during this period, from 8.8% of students in 2012 to 8.7% of students in 2016. Essentially, gifted enrollment held stable during this period.

Figure 2.

Gifted enrollment rates between 2012 and 2016 (down very slightly—average school N = 43,697). Source: 2012 – 2016 Office for Civil Rights data.

Section II: Gifted Program Variations

Gifted programs are much more common in larger schools. Figure 3 shows that for a school with 175 to 225 students, the probability of having a gifted program is 51.5%, whereas more than 75% of schools with more than 625 students have GT programs. More than 80% of large schools with more than 825 students have the programs.⁵

Figure 3.

Percentage of schools with gifted programs by school enrollment (204 school-year observations with greater than 1,500 enrollment excluded). Sources: 2015 – 16 National Center for Education Statistics and Office for Civil Rights data.

Analyzing the likelihood of having gifted programs and school enrollment disaggregated by year (2012, 2014, and 2016), we get the following graph, Figure 4. This graph indicates that there is very little variation in the percentage of schools with gifted programs by school enrollment for the three analyzed school years.

Figure 4.

Percentage of schools with gifted programs by school enrollment disaggregated by year. Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

The likelihood of having a gifted program does not vary much by school poverty level, and low-poverty schools are slightly less likely to have the programs than other schools (Figure 5). In 2016, Table 1 shows that 65.4% of low-poverty schools had gifted programs, whereas 69.9% of middle-poverty schools, and 68.1% of high-poverty schools had the programs.

Figure 5.

Percentage of schools with gifted programs by school poverty (2016 only). Sources: 2015-16 National Center for Education Statistics and Office for Civil Rights data.

Table 1.

Schools With Gifted Programs by Poverty-Level by Year.

Poverty	2012 (%)	2014 (%)	2016 (%)
Low	68.0	66.5	65.4
Middle	70.9	69.5	69.9
High	67.9	68.9	68.1

Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

Almost all of the decrease in the percentage of schools with gifted programs during the period of study comes from a decrease in low-poverty schools with the programs (see Table 1). The percentage of low-poverty schools with gifted programs dropped from 68.0% in 2012 to 65.4% in 2016, a decline of 3.8% (see Figure 6). The prevalence of the programs in middle-poverty schools also decreased slightly, but in high-poverty schools, the prevalence of gifted programs increased very slightly, rising from 67.9% to 68.1% of schools (a change of 0.3%) (Figure 6).

Figure 6.

Percentage change of schools with gifted programs by poverty. Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

The period of study saw slight declines in the likelihood of having a gifted program for schools in rural, urban, and town areas (see Table 2). When looking at the prevalence of programs by urbanicity, only suburban schools became more likely to have the programs during this period, although the increase of 1.2% is still very small (Figure 7).

Table 2.

Schools With Gifted Programs by Urbanicity by Year.

Locale	2012 (%)	2014 (%)	2016 (%)
Rural	71.2	67.4	68.9
Town	67.7	67.4	66.5
Suburb	68.1	68.5	68.9
City	70.5	71.6	68.6

Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

Figure 7.

Percentage change of schools with gifted programs by urbanicity. Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

In general, there is little correlation between the racial/ethnic makeup of a school and its likelihood of having a gifted program. Figure 8 below shows that schools that have very little minority enrollment are somewhat less likely to have gifted programs, and schools with nearly 100% minority enrollment are the least likely to have these programs.

Figure 8.

Percentage of schools with gifted programs against percent minority students. Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

Section III: Gifted Participation

Next, we turn to gifted participation in different types of elementary and middle schools with gifted programs. Gifted participation is highest in low-poverty schools, and it has been increasing steadily in those schools during the period of study (see Table 3). Gifted participation in low-poverty schools with the programs increased from 11.9% in 2012 to 12.7% in 2016, an increase of 6.7%. Participation in high-poverty schools also increased, although not as much as in their low-poverty counterparts. Participation in high-poverty schools rose from 5.6% to 5.8%—an increase of 3.6% (see Figure 9). In middle-poverty schools, gifted participation declined very slightly during the study period, falling 1.1% (Figure 9).

Table 3.

Gifted Enrollment Rates by School Poverty by Year.

Poverty	2012 (%)	2014 (%)	2016 (%)
Low	11.9	12.3	12.7
Middle	8.9	9.0	8.8
High	5.6	6.0	5.8

Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

Figure 9.

Percentage change in gifted enrollment rates by school poverty. Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

When looking at gifted enrollment in schools with gifted programs by urbanicity, enrollment falls in all schools in all types of places (see Table 4). Figure 10 indicates that the decline is greatest in towns, where gifted enrollment dropped 5.5% during the study period. Gifted enrollment was flattest in suburban schools, where the slight decrease does not even register a tenth of a percentage point. Gifted enrollment fell by 3.2% in urban areas and 2.4% in rural areas.⁶

Table 4.

Gifted Enrollment Rates by Urbanicity by Year.

Locale	2012 (%)	2014 (%)	2016 (%)
Rural	8.2	8.0	8.0
Town	7.3	7.0	6.9
Suburb	9.3	9.2	9.3
City	9.3	9.4	9.0

Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

Figure 10.

Percentage change in gifted enrollment by urbanicity. Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

Section IV: Proportionality by Race

Among all elementary and middle urban, suburban, and rural schools with gifted programs, a total of 8.7% of students are enrolled in gifted programs. Asian students constitute 4.9% of the overall student population and 8.6% of students enrolled in gifted programs. Black students constitute 15.0% of the student population and 9.9% of those enrolled in gifted programs. Hispanic students constitute 27.5% of the student population and 20.8% of students enrolled in gifted programs (see Figure 11 below). Finally, White students constitute 47.5% of the student population and 54.7% of students enrolled in gifted programs.

Figure 11.

Racial/ethnic proportionality compared against overall student population (2016 only). Sources: 2015-16 National Center for Education Statistics and Office for Civil Rights data.

Another way to look at the racial/ethnic proportionality of gifted programs is to examine the percentage of each student subgroup participating in gifted programs (see Figures 12 and 13 for 2016 and 2012 school years, respectively). In 2016, Figure 12 illustrates that compared with 8.7% of gifted students overall, 16.3% are Asian, 5.6% are Black, 6.3% are Hispanic, and 10.9% are White.When looking at gifted participation by race over time for all schools (low, middle, and high poverty), we find that participation drops slightly or remains very similar for all student groups. In Figure 14, we see that for Asian, Black, and White students, the share of students participating in gifted programs overtime drops slightly, with Black students experiencing the largest drop. For Hispanic students, the share of students participating rose by 0.5% in 2016 from 2012, a change that barely registers when rounding to a tenth of a percentage point. Hispanic students also increase as a proportion of the overall sample of students during this period, rising from 21.2% of students in 2012 to 23.1% of students in 2016.

Figure 12.

Racial/ethnic proportionality compared against overall gifted participation. Sources: 2015-16 National Center for Education Statistics and Office for Civil Rights data.

Figure 13.

Racial/ethnic proportionality compared against overall gifted participation. Sources: 2011-12 National Center for Education Statistics and Office for Civil Rights data.

Figure 14.

Percentage change in gifted participation rates by race (change over time for all schools—low, middle, and high poverty). Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

Section V: High-Poverty Schools’ Proportionality by Race

When looking at gifted participation in high-poverty schools with gifted programs, we saw a slight increase in overall participation from 2012 to 2016 (comparing Figures 15 & 16 below), and in Figure 17 we see that this increase is entirely attributable to an increase in Hispanic participation in these programs. Specifically, gifted participation drops for all student subgroups except Hispanic. For Asian, Black, and White students, the share of students participating in gifted programs overtime drops by 5.1%, 4.2%, and 3.3%, respectively. For Hispanic students, the share of GT participation rose by 5.8% in 2016 from 2012, a very large change considering all other subgroups experienced declines. However, Hispanic students also increase as a proportion of the overall sample of students in high poverty schools during this period, rising from 36.3 percent in 2012 to 41.5 percent of students in 2016.

Figure 15.

High-poverty schools’ racial/ethnic proportionality against their overall gifted participation. Sources: 2011-12 National Center for Education Statistics and Office for Civil Rights data.

Figure 16.

High-poverty schools’ racial/ethnic proportionality against their overall gifted participation. Sources: 2015-16 National Center for Education Statistics and Office for Civil Rights data.

Figure 17.

High-poverty schools’ percentage change in gifted participation by race overtime. Sources: 2012 – 2016 National Center for Education Statistics and Office for Civil Rights data.

As of 2015-16 school year, we see that in high poverty schools with gifted programs, although 5.8% of all students participate in GT programs, 12.9% of gifted students are Asian, 4.6% of gifted students are Black, 5.5% of gifted students are Hispanic, and 8.9% of gifted students are White (refer back to Figure 16).

Limitations

Although we are able to calculate the proportion of students qualifying for FRPL in each school, we are unable to identify which FRPL students enroll in gifted programming. Because we limit some analysis to schools with at least 75.0% of FRPL students, we can be confident that findings are applicable to many students who are high-poverty. In fact, 75.0% is only the lowest bound for our high-poverty schools, and more than half of the high-poverty schools in our analytic sample have more than 88% of their students qualifying for lunch benefits. Nevertheless, we can never be certain and we recognize that this is a limitation to our findings on participation and proportionality. In addition, because we are using non-restricted, publicly available, OCR data, there is a minor loss of granularity in our data set. There is also additional noise added to the data set due to rounding.

Finally, because of the nature of the data, we use binary classifications of gifted enrollment for students. We do not have data on the quality or characteristics of gifted programming, and this is known to vary considerably across schools. Whether gifted programming is targeted to specific subjects, what types of professional development that teachers of gifted students experience, and the extent to which students can engage curriculum outside of their grade level are questions beyond the scope of this study. Furthermore, we have no information about what constitutes “gifted programming,” as the OCR provides no guidance to states about what to include or exclude.⁷ Results are based on data reported to the U.S. Department of Education, and in some cases, the latter may be incomplete or simply incorrect. If states or districts vary in the way that they choose to report gifted enrollment or other related data, then we have a potential measurement validity issue.

Discussion and Policy Implications

Our findings show that between 2012 and 2016, the percentage of elementary and middle schools with gifted programs declined slightly from 69.5% of schools in 2012 to 68.5% of schools in 2016. Although this finding confirms our first alternative hypothesis (that the proportion of schools with gifted programs has fallen between 2012 and 2016), it is very minimal, and the critical takeaway is that the period did not see any growth in the number of elementary and middle schools offering GT programs. This lack of growth is further indication of the field’s persistent struggle to convince policymakers at all levels of government of its importance. This was also evident in the 2009 U.S. government stimulus plan that approved more than 12 billion dollars for IDEA/IDEIA but no funds were allocated for gifted education and the existing Javits program was reduced to US$0 during the 2011 budget negotiations (Gallagher, 2015). Hodges, Tay, Desmet, et al. (2018) also demonstrate an overall trend of shrinking budgets allocated toward gifted education in Texas. Interestingly, they also found that the 2008 recession adversely affected GT funding for suburban school districts. Remarkably, a new report by the Institute for Educational Advancement found that most Americans support increased funding for gifted education as well as specific programmatic improvements (Jones & Gallagher, 2019). Although practically and politically challenging to implement policy changes to the administration and funding of gifted programs, there is a need for policymakers to begin acknowledging the important role gifted programs can play to both promote educational excellence and improve equity.

The period we study also highlight declines in the likelihood of having a gifted program for schools in rural, urban, and town areas. Only suburban schools became more likely to have the programs during this period, albeit by a small increase (just 1.2%). Importantly, we find that gifted participation or enrollment by urbanicity decreased across all four locales, a finding that agrees with our second alternative hypothesis (proportion of students enrolled in gifted programs, especially in high-poverty schools and across locales, has decreased between 2012 and 2016). This decline is greatest in towns, followed by cities, rural areas, and finally suburban areas. Therefore, not only did suburban schools see a slight increase in the number of gifted programs offered but they also experienced the smallest decline in participation rates. Crabtree et al. (2019) underscore this finding with evidence suggesting that urban school districts characterized by socioeconomic and racial segregation contribute to the national gifted gap to a greater extent than suburban and rural school districts.

We also find that from 2012 to 2016, gifted participation or enrollment is highest in low-poverty schools. In fact, gifted participation in low-poverty schools increased by 6.7%, while only increasing by 3.6% in high-poverty schools. That is, low-poverty schools have twice the increase in gifted participation than high-poverty schools. Therefore, not only did gifted programs in low-poverty schools already enroll twice the share of students high-poverty schools enroll (Yaluma & Tyner, 2018), participation is increasing faster in low-poverty schools. Crabtree et al. (2019) also find gifted gaps and extensive socioeconomic and racial disproportionality across a metropolitan school district, leading to reduced enrollment and participation of low-income and minority students in Advanced Placement (AP) courses and STEM opportunities.

These revelations might help explain findings from a recent report from Georgetown University’s Center on Education and the Workforce (CEW) that family affluence may be more important than individual talent for outcomes in K-12 and beyond. Specifically, their report shows that among students with similar academic potential in kindergarten, the test scores of economically disadvantaged students are more likely to decline and stay low during elementary, middle, and high school than the test scores of high-SES peers (Carnevale et al., 2019). If low-SES schools are not encouraging and cultivating young talent through high quality educational programs as much as high-SES schools, it is no surprise that students attending high-poverty schools are falling behind their low-poverty peers, overtime.

Grissom et al. (2019) also find that even among students with similar achievement and other background characteristics, higher SES students are more likely to receive gifted services than lower SES students, even within the same school. Considering the many family and neighborhood factors as well as raw economic differences between low- and high-poverty students, it is important that local school districts recognize how these students’ needs vary. Selecting students for gifted programming based on local norms at the school level may help account for differences in students’ backgrounds and represents a politically palatable, race-neutral mechanism for promoting racial diversity. Research indicates that the use of local norms or group-specific norms (a method of identification that compare students only to those who have had similar opportunities to develop the skills being tested) or a combination of group-specific and local norms can help mitigate racial and socioeconomic disproportionality (Peters & Gentry, 2012; Peters, Rambo-Hernandez, et al., 2019).

The other important finding from this study looks at proportionality of gifted students by race. Among all elementary and middle urban, suburban, and rural schools with gifted programs, we show that in the most recent data (2016), students who are Asian and White are statistically well represented in gifted programs whereas students who are Black and Hispanic continue to be statistically underrepresented. This is a phenomenon that has plagued gifted programs for many years and continues to do so. In high-poverty schools, we find that Black GT participation has declined by 4.2% (agreeing with one-half of our third alternative hypothesis which states that in high-poverty schools, racial participation and representation of Black and Hispanic students in gifted programs has decreased between 2012 and 2016). Conversely, Hispanic GT participation in high-poverty schools has increased by 5.8% (disagreeing with one-half of our third alternative hypothesis).

Although this is positive news for Hispanic students, they still continue to be underrepresented in GT programs. To improve gifted participation and representation of Black and Hispanic students, policymakers at the local levels need to heed to research evidence that delineate strategies to combat this problem. For example, Card and Giuliano (2016) discussed above provide evidence of a strategy that might work. Hodges, Tay, Maeda, and Gentry (2018) suggest the use of both traditional and nontraditional methods of identification. Other strategies may include increasing the workforce of teachers of color (Gershenson et al., 2016, 2018; Morgan, 2019). Although increasing teachers of color is a difficult challenge for many school districts, improving conditions and providing support in schools where most teachers of color work could help attract and retain many qualified teachers of color (Morgan, 2019).

Conclusion and Future Research

Adopting universal screening, norming entrance criteria locally, and improving the representation of teachers of color are the most promising methods to improving the representation of low-income and students of color in gifted programs. The recently proposed reforms to the admissions criteria for New York City’s selective high schools contained some of these elements, and, although those reforms were eventually shelved, policymakers and advocates should work to investigate how such reforms may be effectively implemented in their communities. Researchers, educators, and journalists should continue to shine a light on student diversity in accelerated programs, in particular by studying and evaluating programs implementing these newer approaches to promoting diversity.

Universal screening, for example, has become more common, and future research should investigate the effectiveness of new programs as they roll out. For instance, as districts or schools implement reforms that boost diversity, do the changes result in lowered academic standards or negative peer effects? And how do student growth trajectories change with the reform of identification policies? Researchers should also work to help practitioners and the public understand the extent to which disproportionate admission across groups is attributable to biased selection policies as opposed to legitimate differences in academic preparedness across student groups. Better understanding of these questions will not only help practitioners to decide how to maximize student development, but will also assist with the political hurdles to sustaining these reforms by providing information to the public that may allay fears that idealistic policies come at an unacceptable cost to some students.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Christopher B. Yaluma

Notes

About the Authors

Christopher B. Yaluma is a PhD student in Public Policy and Management at John Glenn College of Public Affairs, Ohio State University. His primary research interests are school choice, analysis and evaluation of local and state education policies, and access to gifted and talented programs for low-income and minority students. He has published national reports analyzing access to elementary charter schools as well as access to gifted programs. He holds a bachelor’s degree in physics from Berea College and a masters’ degree in transformative education with a concentration in leadership, policy, and research from Miami University.

Adam Tyner is an associate director of research at the Thomas B. Fordham Institute, where he helps develop and manage Fordham’s research projects. Prior to joining Fordham, he served as senior education analyst at Hanover Research, where he executed data analysis projects and worked with school districts and other education stakeholders to design custom studies. His work has appeared and been cited in national and international media such as The Economist, The New York Times, Forbes, Education Week, Education Next, and The Diplomat, as well as in numerous local outlets. He holds a PhD in political science from the University of California, San Diego, where he completed his doctoral dissertation on the integration of rural-to-urban migrant workers in China’s cities. He also holds a Bachelor of Arts in international studies from the University of Oklahoma.

References

Baker

B. D.

(2001). Gifted children in the current policy and fiscal context of public education: A national snapshot and state level funding analysis of Texas. Educational Evaluation and Policy Analysis, 23, 229–250.

Bohan

C. H.

(2003). Early vanguards of progressive education: The committee of ten, the committee of seven, and social education. Journal of Curriculum & Supervision, 19(1), 73.

Boyd

Lankford

Loeb

Rockoff

Wyckoff

(2008). The narrowing gap in New York City teacher qualifications and its implications for student achievement in high-poverty schools. Journal of Policy Analysis and Management, 27(4), 793–818.

Callahan

C. M.

Moon

T. R.

(2017). Describing the status of programs for the gifted: A call for action. Journal for the Education of the Gifted, 40(1), 20–49.

Card

Giuliano

(2016). Universal screening increases the representation of low-income and minority students in gifted education. Proceedings of the National Academy of Sciences of the United States of America, 113(48), 13678–13683.

Carman

C. A.

Walther

C. A.

Bartsch

R. A.

(2018). Using the Cognitive Abilities Test (CogAT) 7 nonverbal battery to identify the gifted/talented: An investigation of demographic effects and norming plans. Gifted Child Quarterly, 62(2), 193–209.

Carnevale

A. P.

Fasules

M. L.

Quinn

M. C.

Campbell

K. P.

(2019). Born to win, schooled to lose: Why equally talented students don’t get equal chances to be all they can be. Georgetown University Center on Education and the Workforce. https://1gyhoq479ufd3yna29x7ubjn-wpengine.netdna-ssl.com/wp-content/uploads/FR-Born_to_win-schooled_to_lose.pdf

Crabtree

L. M.

Richardson

S. C.

Lewis

C. W.

(2019). The gifted gap, STEM education, and economic immobility. Journal of Advanced Academics, 30(2), 203–231.

Dexter

E. G.

(1906). Ten years’ influence of the report of the committee of ten. The School Review, 14(4), 254–269.

10.

Evans

(2005). Reframing the achievement gap. Phi Delta Kappan, 86(8), 582–589.

11.

Firmender

J. M.

Reis

S. M.

Sweeny

S. M.

(2013). Reading comprehension and fluency levels ranges across diverse classrooms: The need for differentiated reading instruction and content. Gifted Child Quarterly, 57(1), 3–14.

12.

Flores

(2007). Examining disparities in mathematics education: Achievement gap or opportunity gap? The High School Journal, 91(1), 29–42.

13.

Ford

D. Y.

(1998). The underrepresentation of minority students in gifted education: Problems and promises in recruitment and retention. The Journal of Special Education, 32(1), 4–14.

14.

Gallagher

J. J.

(2015). Political issues in gifted education. Journal for the Education of the Gifted, 38(1), 77–89.

15.

Gamoran

(2010). Tracking and inequality. In Ball

S. J.

Apple

M. W.

Gandin

L. A.

(Eds.), The Routledge international handbook of the sociology of education (pp. 213–228). Routledge.

16.

Gershenson

Hart

Hyman

Lindsay

Papageorge

N. W.

(2018). The long-run impacts of same-race teachers (NBER No. w25254). National Bureau of Economic Research.

17.

Gershenson

Holt

S. B.

Papageorge

N. W.

(2016). Who believes in me? The effect of student–teacher demographic match on teacher expectations. Economics of Education Review, 52, 209–224.

18.

Grissom

J. A.

Redding

(2015). Discretion and disproportionality: Explaining the underrepresentation of high-achieving students of color in gifted programs. AERA Open, 2(1). https://doi.org/10.2332858415622175

19.

Grissom

J. A.

Redding

Bleiberg

J. F.

(2019). Money over merit? Socioeconomic gaps in receipt of gifted services. Harvard Educational Review, 89(3), 337–369.

20.

Grissom

J. A.

Rodriguez

L. A.

Kern

E. C.

(2017). Teacher and principal diversity and the representation of students of color in gifted programs: Evidence from national data. The Elementary School Journal, 117(3), 396–422.

21.

Harris

Plucker

(2014). Achieving equity and excellence: The role of school mental health providers in shrinking excellence gaps. Gifted Child Today, 37(2), 111–118.

22.

Hodges

Tay

Desmet

Ozturk

Pereira

(2018). The effect of the 2008 recession on gifted education funding across the state of Texas. AERA Open, 4(3). http://doi.org/10.1177/2332858418786224

23.

Hodges

Tay

Maeda

Gentry

(2018). A meta-analysis of gifted and talented identification practices. Gifted Child Quarterly, 62(2), 147–174.

24.

Jolly

J. L.

(2009). A resuscitation of gifted education. American Educational History Journal, 36(1/2), 37–52.

25.

Jones

Gallagher

S. A.

(2019). America agrees: Public attitudes towards gifted education. Institute for Educational Advancement. https://educationaladvancement.org/wp-content/uploads/2019/05/IEA-P-Full-Report-Web-1.pdf

26.

Kettler

Russell

Puryear

J. S.

(2015). Inequitable access to gifted education: Variance in funding and staffing based on locale and contextual school variables. Journal for the Education of the Gifted, 38(2), 99–117.

27.

Lamb

K. N.

Boedeker

Kettler

(2019). Inequities of enrollment in gifted education: A statewide application of the 20% equity allowance formula. Gifted Child Quarterly, 63(4), 205–224. http://doi.org/10.1177/0016986219830768

28.

Lawrence

B. K.

(2009). Rural gifted education: A comprehensive literature review. Journal for the Education of the Gifted, 32, 461–494.

29.

Loveless

(1999). The tracking wars: State reform meets school policy. Brookings Institution Press.

30.

Lucas

S. R.

(1999). Sociology of education series. Tracking inequality: Stratification mobility American high schools. Teachers College Press.

31.

Morgan

(2019). The ack of minority students in gifted education: Hiring more exemplary teachers of color can alleviate the problem. Clearing House: A Journal of Educational Strategies, Issues and Ideas, 92(4–5), 156–162.

32.

National Center for Education Statistics. (2020). Concentration of public school students eligible for free or reduced-price lunch. https://nces.ed.gov/programs/coe/indicator_clb.asp

33.

Oakes

(2005). Keeping track: How schools structure inequality (2nd ed.). Yale University Press.

34.

Peters

S. J.

Engerrand

K. G.

(2016). Equity and excellence: Proactive efforts in the identification of underrepresented students for gifted and talented services. Gifted Child Quarterly, 60(3), 159–171.

35.

Peters

S. J.

Gentry

(2012). Group-specific norms and teacher-rating scales: Implications for underrepresentation. Journal of Advanced Academics, 23(2), 125–144.

36.

Peters

S. J.

Gentry

Whiting

G. W.

McBee

M. T.

(2019). Who gets served in gifted education? Demographic representation and a call for action. Gifted Child Quarterly, 63(5), 273–287. http://doi.org/10.1177/0016986219833738

37.

Peters

S. J.

Rambo-Hernandez

Makel

M. C.

Matthews

M. S.

Plucker

J. A.

(2019). Effect of local norms on racial and ethnic representation in gifted education. AERA Open, 5(2). http://doi.org/10.1177/2332858419848446

38.

Plucker

J. A.

Hardesty

Burroughs

(2013). Talent on the sidelines: Excellence gaps and America’s persistent talent underclass. Center for Education Policy Analyses, University of Connecticut.

39.

Plucker

J. A.

Peters

S. J.

(2020). Excellence gaps in education: Expanding opportunities for talented students. Harvard Education Press.

40.

Puryear

J. S.

Kettler

(2017). Rural gifted education and the effect of proximity. Gifted Child Quarterly, 61(2), 143–152.

41.

Rasheed

(2020). Context and content in rural gifted education: A literature review. Journal of Advanced Academics, 31(1), 61–84. http://doi.org/10.1177/193220219879174

42.

Shapiro

(2019, August 26). Desegregation plan: Eliminate all gifted programs in New York. The New York Times. https://www.nytimes.com/2019/08/26/nyregion/gifted-programs-nyc-desegregation.html

43.

Wheelock

(1994). Alternatives to tracking and ability grouping. R&L Education.

44.

Worthy

Hungerford-Kresser

Hampton

(2009). Tracking and ability grouping. In Christenbury

Bomer

Smargorinsky

(Eds.), Handbook of adolescent literacy research (pp. 220–235). Guilford Publications.

45.

Wright

B. L.

Ford

D. Y.

Young

J. L.

(2017). Ignorance or indifference? Seeking excellence and equity for under-represented students of color in gifted education. Global Education Review, 4(1), 45–60.

46.

Yaluma

Tyner

(2018). A gifted gap? Gifted and talented services in high-poverty schools. Thomas B. Fordham Institute.http://edex.s3-us-west-2.amazonaws.com/publication/pdfs/%2801.31%29%20Is%20There%20a%20Gifted%20Gap%20-%20Gifted%20Education%20in%20High-Poverty%20Schools.pdf

47.

Yoon

S. Y.

Gentry

(2009). Racial and ethnic representation in gifted programs: Current status of and implications for gifted Asian American students. Gifted Child Quarterly, 53(2), 121–136.

Are U.S. Schools Closing the “Gifted Gap”? Analyzing Elementary and Middle Schools’ Gifted Participation and Representation Trends (2012–2016)

Abstract

Keywords

Introduction

A Brief Historical Background

Differentiation and Tracking

Participation of Students From Underrepresented Groups in Accelerated Programming

Research Questions and Hypotheses

Research Questions

Hypotheses

Data and Methods

Findings

Section I: Availability and Access Overtime

Section II: Gifted Program Variations

Section III: Gifted Participation

Section IV: Proportionality by Race

Section V: High-Poverty Schools’ Proportionality by Race

Limitations

Discussion and Policy Implications

Conclusion and Future Research

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Notes

About the Authors

References