One study claims that about half the test score gap between black and white high school students is already evident when children start school. A variety of different tests at kindergarten entry have provided evidence of such a gap, including the U.
While results differ depending on the instrument, estimates of the black-white gap range from slightly less than half a standard deviation to slightly more than 1 standard deviation. This early disparity in performance is critical, as research shows that once students are behind, they do not catch up. Children who score poorly on tests of cognitive skills before starting kindergarten are highly likely to be low performers throughout their school careers.
The evidence of the early appearance of the gap has led to efforts focused on early childhood interventions. Cultural and environmental factors The culture and environment in which children are raised may play a role in the achievement gap.
Jencks and Phillips argue that African American parents may not encourage early education in toddlers because they do not see the personal benefits of having exceptional academic skills.
As a result of cultural differences, African American students tend to begin school with smaller vocabularies than their white classmates. As a result, these children enter school with decreased word knowledge that can affect their language skills, influence their experience with books, and create different perceptions and expectations in the classroom context. Studies show that when students have parental assistance with homework, they perform better in school.
Students from single-parent homes often find it difficult to find time to receive help from their parent. Similarly, some Hispanic students have difficulty getting help with their homework because there is not an English speaker at home to offer assistance. Another explanation that has been suggested for racial and ethnic differences in standardized test performance is that some minority children may not be motivated to do their best on these assessments.
The first explanation is that standardized IQ tests and testing procedures are culturally biased toward European-American middle class knowledge and experiences. Claude M. According to Steele, minority test takers experience anxiety, believing that if they do poorly on their test they will confirm the stereotypes about inferior intellectual performance of their minority group.
Meanwhile, however, educational statisticians were fashioning a new way to think about and model the effect of schools and teachers on student learning. The Coleman report and similar studies had correlated tangible school resources with student outcomes, finding little. Yet the data also suggested there were substantial differences among schools that could not be explained by observed differences in resources. Thus beginning in the s, scholars began ask whether and how much assigning a student to School A versus School B versus School C might affect their test scores.
In late elementary and the middle school years, students learn, on average, only five or six points each year — leading to a downward bend in average student growth rate. Further, students in school A experience less deceleration of their growth in the later grades.
By doing this over enough students and schools, these models estimate the extent to which school assignments deflect students off their typical growth patterns.
Initial findings from such models agreed with the Coleman estimates. Tony Bryk and Stephen Raudenbush, who literally wrote the book on these newer modeling methods, used another Coleman dataset to show that differences among schools accounted for about one-fifth of the variability in student outcomes. Even as modeling techniques have improved, that has remained an upper bound.
In another two decades, experiments using lottery data from oversubscribed urban schools — in other words, the most desirable schools in the eyes of urban parents and likely the top performers among all schools — began to clarify the size of this advantage. In a study of oversubscribed Boston charters, for instance, economists estimated that these schools closed between half and two-thirds of the black-white test score gap each year of middle school. What drives these schools and other high performers is still a matter of debate.
Both early and recent evidence suggest that successful schools meet the most basic needs of their inhabitants: students and faculty report feeling safe, teachers have high expectations for students, and students attend to their studies seriously. Beyond this, key school characteristics have been hard to measure. Many — school trust, teacher collaboration, principal leadership, teacher working conditions, teacher efficacy, academic optimism — appear to positively predict student outcomes, but studies have yet to understand whether these are related or distinct from one another, and which ones are causally related to student outcomes.
Yet whether anyone can explain it or not, something associated with differences between schools does appear to explain student outcomes. But this research has also shown that in the context of the overall variability in child outcomes, schools still pack a weaker punch than many imagine. So far, scholars had been unable to fully untangle the causes of growing educational inequality.
To do it, researchers made clever use of an artifact of the U. Comparing summer learning to the corresponding school-year rates of growth would make the unique impact of schools visible. Although Heyns and others had designed studies based on this logic since the s, the best datasets for answering these questions were not created until nearly 30 years later, when the Early Childhood Longitudinal Study followed a nationally representative sample of children through their first years of school.
A second ECLS began tracking a new cohort of kindergartners in Both studies tested young students in the fall and spring, a key condition for differentiating summer from school-year growth. Analyses of both clearly show that students steadily learn during the school year, but that the average rate of learning drops to zero, in some subjects and grades, over the summer recess. Schools, when all is said and done, are fairly effective in teaching students at least some math, reading, and science each year.
Answering the question about the role of schools in social stratification required asking how student growth rates differed over time. The answer was yes: during the school year, student growth resembles telephone wires tracking steadily up a hill.
During the summer months, however, those learning rates resembled more of a fan, with some children learning quickly, others not at all, and still others losing ground. A second version of this question focuses on the role of family income and parental education in explaining these summer and school year growth rates. Here, the results are again unequivocal. In other words, the students losing ground during the summer tend to come from poor families; children in non-poor families either hold their ground or gain, probably owing to the array of resources non-poor families marshal both within and outside the home.
But Downey and others disagree. For race and ethnicity, the story is more complicated. An analysis of a more recent wave of ECLS data by David Quinn of the University of Southern California and two Harvard colleagues suggests that black children learn more rapidly than or at the same pace as white students in some grades and subjects, but lag in others. The mixed findings are also true for Hispanic students. Similar to the story on why some schools perform better than others, there are no clear-cut explanations for these slower growth rates among black and Latino students.
It is likely that sorting among these explanations will take yet another set of studies and measures. The data also tell an interesting story regarding another ethnic group.
Is their performance good because of schools or in spite of schools? One explanation may be an artifact of the ways schools compensate for out-of-school social inequality. Most teachers also report, in surveys, directing most of their attention to struggling children rather than high-performers — another compensatory mechanism. Adam Gamoran, a sociologist who is now the President of the William T. We look at schools for poor kids and rich kids, and we see that achievement rates are different.
Graduation rates are different. College-going rates are different. And then we simply attribute those differences to schools. Another reason for the mismatch between the academic and public images of schooling may be that high schools, which include the years most vividly remembered by students and nearest to when students enter the labor market, may exacerbate inequality.
The best evidence that exists, in a recent paper by sociologists at New York University and Harvard, compares eighth and 10th grade data to suggest that high schools in Texas and Massachusetts are largely neutral with regard to academic inequality, with traditionally advantaged groups only slightly more likely to attend high schools that are better at boosting student achievement.
Whether student assignment to a track is itself overtly racially biased or simply results from prior student achievement patterns which themselves may reflect the effects of racial bias is a topic on which scholars have waged loud and extended arguments. But income, race, and ethnicity is correlated with track assignment, and students in higher tracks have opportunities to learn more challenging content from more qualified teachers, resulting in inequality growth. This points to the role social choices play in the production of inequality.
Tracking is viewed as a way to make instruction more efficient and prepare qualified students for the demands of college; middle school marks the beginning of mathematics tracking in most districts and humanities tracking in some.
Exposing all students to the same curriculum over those middle years, however, is a viable option. Delaying tracking until high school would preserve the equalizing effects of schools over the early adolescent period. Yet this is not a choice most states and districts make.
Thinking about solutions to academic inequality in terms of social choices highlights other possibilities, as well. One choice that research suggests would be targeting additional resources to the schools serving at-risk students. Downey points to a recent study that uncovered a modest gap between U. The author of this study, Joseph Merry, also compared Canadian and U. The U. The exact same size. And we can make policy decisions that change that. Such policy decisions would surely have to address income inequality, which itself is related to a complex set of social factors.
Such a wide-ranging discussion of the role of schools, families, race, and public policy choices would be unusual in U.
Families matter, and families are profoundly shaped by the contexts in which they find themselves. Finding policy solutions that work in both realms presents the challenge the next generation of scholars must solve. In math, the younger adolescents register average gains of 0. At both ages, the reading gains are less. The trend among younger adolescents amounts to just 0.
The differences in trend lines for students at different ages presents a puzzle for which we have no easy answer. Even setting aside the oldest students in our data, we see that the average improvement in test performance among and year-olds who take the NAEP tests and the TIMSS is larger than that registered by year-olds on the PISA tests.
This may reflect differences in test design, or it may suggest that the fade-out in gains begins in the early years of high school. The lack of a positive trend among year-olds for the past quarter century also suggests that high schools do not build upon gains achieved earlier, a signal, perhaps, that the high school has become a troubled institution.
In any event, there is no sign of a rising tide that lifts all boats at age 17 when these students are going into further schooling or into the labor force. Importantly, the age anomaly that we see in the trends in achievement levels is not found in the performance gaps. Constant social gaps are found across all age groups. The achievement gap between haves and have-nots in the U. That gap has not widened, as some have suggested. The question remains: why has the gap remained constant?
The tempting answer is that nothing significant enough has happened to alter its size. But this would ignore a wide variety of factors that have shifted over the years. It is more likely that some changes within families and within schools have worked to close the socioeconomic achievement gap while other changes have widened it, with these factors largely offsetting one another.
Socioeconomic differences in the age of the mother at the birth of the child have also increased in the past 50 years. The incidence of single-parent households has increased and is likewise concentrated at the lower end of the socioeconomic spectrum.
Each would tend to exacerbate socioeconomic achievement gaps. But these negative factors could be offset by other, countervailing demographic changes. So have differences in the number of siblings in the household. Both factors are important determinants of student achievement. The balance among all these factors may well have left the family contribution to the achievement gap at much the same level today as it was for cohorts born in the s.
Similarly, there may be opposing forces within the educational system that have offset one another. On the one side, over the past 50 years, the federal government has enacted compensatory education programs for school-age children and the Head Start program for students at ages three and four.
Brown v. Board of Education and the Civil Rights Act of accelerated school desegregation, particularly in the South. The Individuals with Disabilities Education Act funded school services for students with disabilities, a group disproportionately composed of children from low-income families. States systematically changed their funding of local schools, often in response to court orders, leading to more equal funding between rich and poor school districts.
Overall school funding increased dramatically on a per-student basis, quadrupling in real dollars between and And finally, states have introduced measures holding schools accountable for student performance, as required by the No Child Left Behind Act. Accountability mandates were disproportionately directed toward schools serving low-income students. Each is aimed at closing gaps.
On the other hand, the quality of the teaching force—a centrally important factor affecting student achievement—may well have declined over the course of the past several decades. Women have greater access to opportunities outside the field of teaching.
These changes affecting the quality of the teaching force are likely to have had a disproportionately adverse effect on disadvantaged students. Collective-bargaining agreements and state laws have granted more-experienced teachers seniority rights, leaving disadvantaged students to be taught by less-effective novices.
In other words, a growing disparity in teacher quality across the social divide may have offset the impacts of policies designed to work in the opposite direction. Two surprises emerge from this analysis of long-term trends in student-achievement levels and gaps across the socioeconomic distribution. First, gaps in achievement between the haves and have-nots are mostly unchanged over the past half century.
Second, steady gains in student achievement at the 8th-grade level have not translated into gains at the end of high school. Because cognitive skills as measured by standardized achievement tests are a strong predictor of future income and economic well-being, the unwavering achievement gap across the socioeconomic spectrum sends a discouraging signal about the possibilities of improved intergenerational social mobility. Perhaps more disturbing, programs to improve the education of disadvantaged students, while perhaps offsetting a decline in the quality of teachers serving such students, have done little to close achievement gaps.
These steadfast disparities suggest the need to reconsider the current direction of national education policy. Two areas for further exploration seem especially critical. First, researchers have uniformly found that teacher effectiveness is a predominant factor affecting school quality. While there has been ample commentary on teacher recruitment and compensation policies, few programs and policies at scale have directly focused on enhancing teacher quality, particularly for disadvantaged students.
Second, the achievement gains realized by students at age 14 fade away by age 17, yet policymakers have left high schools—like the achievement gap itself—in many ways untouched. Eric A. Paul E. Laura M. Ludger Woessmann is professor of economics at the University of Munich.
We use surveys from four testing programs to investigate achievement gaps and levels over time. These surveys use consistent data-collection procedures to trace the achievement of representative samples of U. Each data set comprises student-level data that we aggregate by demographic group. Data are available for math in select years from — and for reading from — We create a panel of math and reading scores for students age 13 and 17, beginning with the birth cohort, who turned 17 in In a typical year, approximately 17, students participate.
We create a panel of math and reading scores for 8th graders from — The Main NAEP is aligned to school curricula and designed to provide results for representative samples of students in the United States as a whole and for each participating state.
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