– There were significant race by gender differences in students’ education and STEM occupational plans.
– Race and gender differences exsist in perceived cost utility and efficacy of education and occupation outcomes.
– Depending on the definition of STEM careers operationalized in the analysis, variation can be observed in the impact of gender, while the role of the expectancy-value constructs remains largely consistent across multiple definitions of STEM careers.
– While expectancy-value constructs such as utility, interest, and attainment value are significantly related to the STEM career plans of White students, fewer significant relationships between expectancy-value constructs and the STEM career plans of Black and Hispanic students were identified.
– There were significant race by gender differences in students’ education and STEM occupational plans.
– While controlling for prior achievement and race, gendered differential treatment was negatively associated with math beliefs and achievement, whereas relevant math instruction was positively associated with these outcomes.
– Gendered differential treatment by teachers in the 8th grade negatively related to student math importance and math grade within the same year.
– Gendered differential treatment by teachers in the 11th-grade was negatively related to 11th-grade SCMA.
– In 8th and 11th grade, relevant math instruction was positively related to students’ math importance and SCMA
– 8th-grade and 11th grade relevant math instruction had an indirect effect upon math importance via self-concept of math ability.
– Self-concept of math ability in the 8th grade partially mediated the relationship between 8th-grade relevant instruction and self-
concept of math ability in the 11th-grade.
– Maryland Math Achievement scores in the 9th grade partially mediated the relationship between 8th-grade gendered differential treatment and self-concept of math ability in the 11th grade.
– There are substantial socioeconomic differences in the subjects that young people study from age 14 to 16.
– Young people from advantaged households take more selective subjects, have higher odds of doing three or more facilitating subjects, higher odds of studying a full set of EBacc-eligible subjects (including English, Maths, History or Geography, two sciences and a modern or ancient language), but lower odds of taking Applied GCSEs (e.g. Applied Hospitality, Applied Health or Applied Manufacturing) than less advantaged young people.
– There were important differences by school characteristics, which may be a result of differential opportunities, subjects offered and within school policies.
– Even holding other factors constant, pupils in non-selective schools within selective local authorities study a less academically selective set of subjects.
– When considering university entry, and admission to high-status universities in particular, there are large raw differences associated with studying more academic combinations of subjects.
However, once differences in young people’s backgrounds and prior attainment associated with these differences in subjects studied are taken into account, these differences are, at most,
– The results for studying the full set of EBacc subjects and for studying any applied subjects do show residual associations with university attendance.
– If young people from different socioeconomic backgrounds were studying a more similar curriculum between ages 14 and 16 it would be unlikely to make much of difference to the inequality in university entry highlighted by previous studies.
– Household income, home ownership and higher parental education increase the odds of taking three STEM subjects
– Socio-economic differentials in access to STEM are largely driven by prior attainment.
– Participation in STEM subjects does not vary by school characteristics, with the exception of the proportion of Free School Meals (FSM) in the school which is negatively associated with doing three or more STEM subjects.
– Overall, 68% of students in the kindergarten sample and 69% of first grade students were assigned to teachers who share their ethno-racial identity.
– Overall, 38% of kindergarten and 71% in first-grade classes use ability grouping for reading.
– 27% of African American kindergartners were placed in low ability groups compared with 25% of Latino/a kindergartners and 18% of White kindergarten students.
– Around 44% of African American and 46% of Latino/a first graders were placed in low ability groups compared with 37% of White first graders.
– Having a same-race teacher has no direct and independent effect on student placement in higher ability groups in the kindergarten.
– By first grade, placement with same-race teachers has a strong positive and significant effect on Latino/a students’ ability group placement and a marginally positive effect on African American students’ ability group placement.
– Once previous ability group placement is controlled for, placement with same-race teachers continue to be a positive and significant predictor of Latino/a students’ ability group placement in the first grade.
– Teachers’ perceptions about students’ learning abilities are influenced to a certain extent by student–teacher ethno-racial congruence resulting in significant postive effects on higher group placements in kindergarten and first grade.
– Both African American and Latino/a students are significantly less likely to be placed in higher reading ability groups compared with White students.
– Male kindergartners are significantly less likely to be placed in higher ability groups.
– The higher the percentage of African American students in class, the more likely students will be placed in higher ability groups.
– Students from higher SES are more likely to be placed in higher ability groups. However, as the average classroom SES increases, students are significantly less likely to be placed in higher ability groups.
Academic Performance of African American High School Students Related to Socioeconomic Status and School Size
– There was a negative correlation between school level SES and reading at -.50, -.44 for mathematics, and -.35 for science performance.
– There was a positive correlation between school size and reading at .10, .01 for mathematics, and .07 for science performance.
– School level SES and school size had significant impact on school
performance in reading.
– School level SES had significant impact on school performance in mathematics.
– School level SES and school size had significant impact on school performance in science.
Cracking the Code: Girls' and Women's Education in Science, Technology, Engineering and Mathematics (STEM)
The report is intended to stimulate debate and inform STEM policies and programmes at global, regional and national levels. Specifically, it aims to: i) document the status of girls’ and women’s participation, learning achievement, and progression in STEM education; ii) ‘crack the code’, i.e., decipher the factors that contribute to girls’ and women’s participation, achievement and progression in STEM education; and, iii) identify interventions that promote girls’ and women’s interest in and engagement with STEM studies.
Narrowing Pathways? Exploring the Spatial Dynamics of Postsecondary STEM Preparation in Philadelphia, Pennsylvania
What geographical factors are associated with the postsecondary STEM preparation of students from underrepresented groups in the School District of Philadelphia from middle to high school?
Can Class-Based Substitute for Race-Based Student Assignment Plans? Evidence from Wake County, North Carolina.
1. Were Wake County schools more racially integrated under the race-based or the socioeconomic-based pupil assignment plan? 2. Was overall student achievement higher under the race-based or socioeconomic-based plan? 3. Did achievement gaps increase or decrease under the race-based or socioeconomic-based plan? 4. Was school racial composition correlated with changes in performance under the race-based or socioeconomic assignment plan?
Do teachers’ instructional practices differentially affect the mathematics achievement of kindergarten students whose backgrounds differ in terms of their race/ethnicity, socioeconomic status (SES), and mathematic academic readiness?
Is housing status a predictor of student achievement in a large urban district, even after controlling for common correlates like income and race? Is the homelessness effect mediated by attendance? What school-level factors predict homeless student achievement?
Long-Term Consequences of School Segregation: The Impact of School SES, Racial Density and Racial Diversity on Future Earnings
What is the impact of school socioeconomic status (SES), school racial density, and school racial diversity on students’ future earnings?
Experimental Evidence on the Effect of Childhood Investments on Postsecondary Attainment and Degree Completion
Does having a small class size in K through 3rd have an impact on postsecondary outcomes?
Do housing voucher holders live next to good schools?
The Effects of Single-Sex Compared With Coeducational Schooling on Mathematics and Science Achievement: Data From Korea
– Results for eighth graders indicated no differences between students in single-sex and coeducational schools in mathematics and science achievement.
– Results from the 2003 TIMSS data replicated the finding: students’ mathematics and science achievement was unrelated to the gender composition of their school.
– For both the 2007 and the 2003 data sets, students’ performance was consistently significantly predicted by factors related to socioeconomic status; students (both boys and girls) performed better on the mathematics and science exams when their fathers had more education, their families had more resources, and a lower proportion of their schoolmates came from economically disadvantaged families.
– Both boys’ and girls’ mathematics performance was predicted by the amount of time spent on homework; students do worse when they spend relatively more time on mathematics homework (or students spend more time on homework when they are performing poorly).
The Enduring Impact of Race: Understanding Disparities in Student Disciplinary Infractions and Achievement
To what extent do persistent race gaps in educational outcomes stem from differences in the level of advantage that students bring to school or from differences in opportunities to succeed offered by the schools they attend?
Hypothesis 1: East Asian American students will be more likely to take a commercial SAT test preparation course than any other racial/ ethnic students. Hypothesis 2: Unlike other racial/ ethnic students, high-achieving students will be more likely than low-achieving students to take a commercial SAT test preparation course among East Asian American students. Hypothesis 3: East Asian American students will benefit more from taking a commercial SAT test preparation course for SAT performance than any other racial/ ethnic students.
What is the role of schools’ resources in mediating the effects of family SES on students’ postsecondary destinations?
Are ELL Students Underrepresented in Charter Schools? Demographic Trends in New York City, 2006-2008
Empirically examines the gap in English Language Learner (ELL) enrollment between charter schools and traditional public schools and looks at trends in this gap over several years of data in New York City.
Accuracy and Inaccuracy in Teachers' Perceptions of Young Children's Cognitive Abilities: The Role of Child Background and Classroom Context
1. Are teachers more or less accurate in predicting the cognitive skills of students with particular sociodemographic backgrounds? One would expect a certain amount of inaccuracy in teacher perceptions of their students’ skills. But is this error in teacher estimates randomly distributed, or is it systematically related to children’s socio-demographic characteristics?
2. To what extent do teacher characteristics and classroom and school contexts explain teacher perceptual accuracy? For example, are experienced teachers’ better judges of their students’ skills? Are teacher perceptions more accurate in racially, socioeconomically, or academically homogeneous classrooms? In smaller versus larger classrooms? In public versus private schools?
3. How is teacher accuracy influenced by the interplay between student and teacher or classroom characteristics? For instance, are teachers more accurate in estimating the skills of students with whom they share a racial-ethnic back ground? Are teacher assessments of low-SES children less biased in smaller classrooms?
Teacher Credentials and Student Achievement in High School. A Cross-Subject Analysis with Student Fixed Effects
Examining the relationship between teacher credentials and student achievement in high schools, especially in the core courses taken early in a student’s high school career.
Evaluates the effects of three San Diego, California school choice programs on integration by race, student achievement and parental education levels.
Schools Without Diversity: Education Management Organizations, Charter Schools, and the Demographic Stratification of the American School System
The study explores whether these EMO-operated charter schools integrate or segregate students by four key demographic characteristics: ethnic/minority classification, socioeconomic status, disabling condition and English language facility.
This article investigates how Michigan’s charter school policy influences the composition of students by race and socioeconomic status in urban traditional public schools.
This article proposes a new method for measuring school desegregation in multiracial districts, and uses the new method to measure the desegregation effects of magnet schools in Los Angeles.
Examines segregation between schools within a sector and variation within private voucher forprofit and non-profit (religious and secular) school sectors.
Explores the relative effects of school and neighborhood characteristics on student achievement.
This paper examines why segregation by educational disadvantage has only recently emerged as a policy issue in the Netherlands. In addition, it documents the levels and trends ofschool segregation in Dutch cities.
On the Determinants and Implications of School Choice: Comments Semi-Structural Simulations for Chile
Studies the effects of school choice on bothstudent welfare and socioeconomic segregation.
Academic and Racial Segregation in Charter Schools: Do Parents Sort Students into Specialized Charter Schools?
This article presents a dynamic model that focuses on how parental school choices affect the degree of racial and academic segregation that students experience in charter schools.
This study examines the impact of school choice on the degree of racial segregation by comparing
the conditions in the district schools students exited to the conditions in the charter schools they entered the following year.
Can Gaps in the Quality of Early Environments and Noncognitive Skills Help Explain Persisting Black-White Achievement Gaps?
Examination of whether or not early childhood environments and cognitive development/achievement skills influence the Black-White achievement gaps.
This study examines parents’ demand for sending their children to a public school located outside their residential school district.
They examine the distribution of the gap in test scores across races within New York City public schools and the factors that explain these group.
Analyzed elementary schools in five California metropolitan areas to examine the extent that the racial composition of schools deviates from neighborhood compositions, and investigate the potential for schools to promote racial integration.
Examination of race and class segregation within one urban school district prior to and then after integration plans were dismantled.
Examines whites’ stated residential preferences,not just for African American neighbors but also for Hispanics and Asians, to determine relationships between race and residential segregation.
1. Do African American students show significantly lower scores than other students on the Graduation Exit Examination (GEE)? 2. If they show lower scores, can differences in scores on this examination be explained by the SES of the family (as reflected by the occupational prestige and education of parents)? 3. Does the proportion of African Americans in a school tend to raise or lower the examination scores of students in general, controlling for the socio-demographic characteristics of individual students? 4. Can the effect of racial composition of schools be explained by family socioeconomic background of schoolmates? 5. Does the racial composition of schools affect African American and White Students achievement differently? What is the relative importance of these factors as between-school verses within-school determinants of academic achievement?
-This study finds significant school SES effects when cross-sectional models are estimated.
-These effects largely disappear when longitudinal models are applied, namely, value-added and student fixed effect models.
– There are some statistically significant effects remaining for school racial composition in two of the states and for various subgroups, but the magnitudes of the effects are small.
-Peer SES has no effects or only very small effects on academic achievement
-Large school SES effects often found in cross-sectional studies are artifacts of aggregation and are not a sound basis for SES-based school integration policies.
– The commonly used cross-sectional models for student achievement produce sizable estimates for school SES effects which are often comparable with the effect for student SES. However, in properly specified models using longitudinal data that either (a) control for students’ prior achievement or (b) control for stable differences between students, the effects of school SES are very small.
– The analyses presented in this article do not support the widely held view that school SES and school racial composition have strong effects on student achievement.