achievement gaps
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Author(s):  
Lama K. Farran ◽  
Robert A. Griffin

Purpose: Adolescent multilingual learners are at high risk for reading difficulties as evidenced by persistent achievement gaps. This article calls for a paradigm shift and aims to elucidate what constitutes promising second-language literacy instruction for multilingual adolescents, comprising effective literacy practices grounded in research, combined with an emphasis on individual learners and their sociocultural development. Cast in ecological systems and functionalist perspectives, this article provides a model for language and literacy instruction that is grounded in basic tenets of reading science within a sociocultural context. We outline strategies that focus on language as a basis for reading development followed by examples of authentic learning experiences designed to motivate students and nurture their love of reading. Conclusions: A solution to existing achievement gaps may be a promising approach that emphasizes both the science and love of reading, which entails targeted instruction rooted in the research evidence integrated into engaging and meaningful learning experiences, central to which is the acknowledgement of multilingual learners as individuals. The authors call for an intentional focus on accelerating the development of language through frequent use of and a genuine love for both the science of reading and the science of teaching reading.


2022 ◽  
pp. 262-280
Author(s):  
Veronica A. Keiffer-Lewis

Achieving equity in higher education involves more than just closing achievement gaps and mitigating the impact of historic oppression and underrepresentation. In this chapter, the author presents a framework for cultural humility as a pathway to equity for institutions of education, as well as an approach for the professional development of cultural humility practitioners. The cultural humility framework comprises four core principles as well as five transformational skills (i.e., dialogue, inquiry, self-reflection, conflict transformation, and identity negotiation). The chapter concludes with a discussion about how to implement this framework at both the classroom and institutional levels, as well as the implications of such training for achieving greater equity in higher education.


2021 ◽  
Vol 9 (4) ◽  
pp. 57
Author(s):  
LaTasha R. Holden ◽  
Sara A. Hart

In the US, undeniable evidence shows that socioeconomic inequities explain a high proportion of individual differences in school achievement. Although not all countries show this same effect due to socioeconomic status, it is consistently found that social inequities lead to achievement gaps. These achievement gaps then manifest into trajectories that set some individuals on a path of lower incomes, poorer health and higher mortality, lower wellbeing, and other poor adult outcomes. Like James Flynn so handily reminded the scientific literature that achievement gaps are explainable by environmental factors, the inequities we see around the world are based on environments some children are exposed to. In his work, Flynn stated his belief that the suppression of scientific work on intelligence would continue to lead to social inequities. We wish to take this idea and move it forward. We believe that the scientific construct of intelligence plays a key role in helping create a more equitable society through science. We also believe that the poor perception of intelligence, rooted in historical realities, means that it will continue to be misunderstood, feared, and misused, limiting how effective it could be in helping to close gaps in achievement and in creating a more equitable society.


2021 ◽  
Vol 6 ◽  
Author(s):  
Kimmo Eriksson ◽  
Jannika Lindvall ◽  
Ola Helenius ◽  
Andreas Ryve

We reassess the relation between students’ socioeconomic status (SES) and their achievement by treating SES as multidimensional instead of unidimensional. We use data from almost 600,000 students in 77 countries participating in the 2018 PISA assessment of student achievement in math, science, and reading. The composite measure of SES that PISA uses can be broken down into six component variables that we here use as simultaneous predictors of achievement. This analysis yields several new insights. First, in the typical society, two predictors (books at home and parents’ highest occupational status) clearly outperform the rest. Second, a new composite measure based only on these two components often reveals substantially larger achievement gaps than those reported by PISA. Third, the analysis revealed remarkable differences between societies in the relation between achievement and wealth possessions. In most societies, the independent effect of wealth possessions on student achievement was zero or even slightly negative—but in the least developed societies it was strongly positive. These findings have implications for how SES achievement gaps should be measured and interpreted.


2021 ◽  
Author(s):  
LaTasha R Holden ◽  
Sara Ann Hart

In the US, undeniable evidence shows that socioeconomic inequities explain a high proportion of individual differences in school achievement. Although not all countries show this same effect due to socioeconomic status, it is consistently found that social inequities lead to achievement gaps. These achievement gaps then manifest into trajectories that set some individuals on a path of lower incomes, poorer health and higher mortality, lower wellbeing, and other poor adult outcomes. Like Flynn (1999) so handily reminded the scientific literature that achievement gaps are explainable by environmental factors, the inequities we see around the world are based on environments some children are exposed to. In the same work, Flynn stated his belief that the suppression of scientific work on intelligence would continue to lead to social inequities. We wish to take this idea and move it forward. We believe that the scientific construct of intelligence plays a key role in helping create a more equitable society through science. We also believe that the poor perception of intelligence, rooted in historical realities, means that it will continue to be misunderstood, feared, and misused, limiting how effective it could be in helping to close gaps in achievement and in creating a more equitable society.


2021 ◽  
pp. 019791832110415
Author(s):  
Tate Kihara

In the United States, there is a wide academic achievement gap, beginning in early childhood, between children with more and less educated parents. However, we know little about the differences in size and trajectories of achievement gaps associated with parental education and nativity. Drawing on two US education datasets that enable me to follow a cohort of children from kindergarten to high school, I estimate the size and trajectories of standardized test-score gaps associated with parental education, separately for children of native-born and immigrant parents. I find that the test-score gap between children with more and less educated native-born parents stays wide and stable from kindergarten entry to high school. In contrast, the test-score gap between children with more and less educated immigrant parents is narrower in kindergarten because of higher achievement of children with less educated immigrant parents, compared to their counterparts with less educated native-born parents. Moreover, the gap between more and less educated immigrant parents further narrows in their early life course because the achievement of children with less educated immigrant parents improves relative to children with more educated immigrant parents. Differences by parental nativity in the size and trajectories of achievement gaps associated with parental education can be partially explained by the fact that children with less educated immigrant parents have relatively greater resources than their peers with less educated native-born parents from early in life. My findings provide evidence that the “immigrant advantage” in academic achievement, a common finding in the literature on immigrant education in the United States, originates early in the life course.


2021 ◽  
Author(s):  
Astrid Marie Jorde Sandsør ◽  
Henrik Daae Zachrisson ◽  
Lynn A. Karoly

Socioeconomic achievement gaps measure the disparity in test scores between students from high and low socioeconomic backgrounds, commonly measured as a combination of parental income, education and occupation. However, educational data often limits the ability to create such measures of family background and link them to student test scores, leading researchers to arrive at different conclusions about levels and trends depending on the SES measure and estimation method. In this paper we disentangle the importance of each by using register data from Norway with precise measures of parental income and education. We show that results crucially depend on the SES measure, as parental income and education are not interchangeable measures of socioeconomic background. Achievement gaps by parental income in Norway are large, 0.55-0.93 standard deviations, and have increased by about 10% of a standard deviation over the 11-year time period we study, whereas achievement gaps by parental education are even larger, 0.86-1.15 standard deviations, but remain stable over the same period. Accounting for compositional changes in immigration decreases the magnitude of the gaps, whether measured by parental income or education, while trends remain the same.


Author(s):  
Andres Yi Chang

Social scientists frequently rely on the cardinal comparability of test scores to assess achievement gaps between population subgroups and their evolution over time. This approach has been criticized because of the ordinal nature of test scores and the sensitivity of results to order-preserving transformations that are theoretically plausible. Bond and Lang (2013, Review of Economics and Statistics 95: 1468–1479) document the sensitivity of measured ability to scaling choices and develop a method to assess the robustness of changes in ability over time to scaling choices. In this article, I present the scale_transformation command, which expands the Bond and Lang (2013) method to more general cases and optimizes their algorithm to work with large datasets. The command assesses the robustness of an achievement gap between two subgroups to any arbitrary choice of scale by finding bounds for the original gap estimation. Additionally, it finds scale transformations that are very likely and unlikely to benchmark against the results obtained. Finally, it also allows the user to measure how much gap growth coefficients change when including controls in their specifications.


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