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The First Year of Pandemic Recovery: A District-Level Analysis

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The distribution of child physicians and early academic achievement

Objective: To describe the distribution of pediatricians and family physicians (child physicians) across school districts and examine the association between physician supply and third-grade test scores.

Data Sources and Study Setting: Data come from the January 2020 American Medical Association Physician Masterfile, the 2009–2013 and 2014–2018 waves of American Community Survey 5-Year Data, and the Stanford Education Data Archive (SEDA), which uses test scores from all U.S. public schools. We use covariate data provided by SEDA to describe student populations.

Study Design: This descriptive analysis constructs a physician-to-child-population ratio for every school district in the country and describes the child population served by the current distribution of physicians. We fit a set of multivariable regression models to estimate the associations between district test score outcomes and district physician supply. Our model includes state fixed effects to control for unobservable state-level factors, as well as a covariate vector of sociodemographic characteristics.

Data Collection: Public data from three sources were matched by district ID.

Principal Findings: Physicians are highly unequally distributed across districts: nearly 3640 (29.6%) of 12,297 districts have no child physician, which includes 49% of rural districts. Rural children of color in particular have very little access to pediatric care, and this inequality is more extreme when looking exclusively at pediatricians. Districts that have higher child physician supplies tend to have higher academic test scores in early education, independent of community socioeconomic status and racial/ethnic composition. While the national data show this positive relationship (0.012 SD, 95% CI, 0.0103–0.0127), it is most pronounced for districts in the bottom tertile of physician supply (0.163 SD, 95% CI, 0.108–0.219).

Conclusions: Our study demonstrates a highly unequal distribution of child physicians in the U.S., and that children with less access to physicians have lower academic performance in early education.

Has the Opioid Crisis Affected Student Learning? A National Analysis of Growth Rates

School District and Community Factors Associated With Learning Loss During the COVID-19 Pandemic

Local Achievement Impacts of the Pandemic

Uneven Progress: Recent Trends in Academic Performance Among U.S. School Districts

Is separate still unequal new evidence on school segregation and racial academic achievement gaps.

The Geography of Rural Educational Opportunity

States as Sites of Educational (In)Equality: State Contexts and the Socioeconomic Achievement Gradient

Gender Achievement Gaps in U.S. School Districts

In the first systematic study of gender achievement gaps in U.S. school districts, we estimate male-female test score gaps in math and English Language Arts (ELA) for nearly 10,000 school districts in the U.S. We use state accountability test data from third through eighth grade students in the 2008-09 through 2014-15 school years. The average school district in our sample has no gender achievement gap in math, but a gap of roughly 0.23 standard deviations in ELA that favors girls. Both math and ELA gender achievement gaps vary among school districts and are positively correlated – some districts have more male-favoring gaps and some more female-favoring gaps. We find that math gaps tend to favor males more in socioeconomically advantaged school districts and in districts with larger gender disparities in adult socioeconomic status. These two variables explain about one fifth of the variation in the math gaps. However, we find little or no association between the ELA gender gap and either socioeconomic variable, and we explain virtually none of the geographic variation in ELA gaps.

To download a data file with the gender achievement gap estimates produced in this paper, please click here to sign the data use agreement. Upon signing, you will be redirected to the Stanford Education Data Archive where you can download the data file from this paper.

This map displays Empirical Bayes estimates of the average achievement gaps in math and English language arts in nearly 10,000 U.S. public school districts. A gap of zero indicates that there is no achievement gap in that district. Negative gaps (shown in orange) indicate that female students score higher on average than male students in the district; positive achievement gaps (shown in blue) indicate that male students score higher on average than female students in the district. The gaps displayed are in standard deviation units; for reference, a third of a standard deviation gap is approximately a one grade level difference.

Educational Opportunity in Early and Middle Childhood: Using Full Population Administrative Data to Study Variation by Place and Age

I use standardized test scores from roughly forty-five million students to describe the temporal structure of educational opportunity in more than eleven thousand school districts in the United States. Variation among school districts is considerable in both average third-grade scores and test score growth rates. The two measures are uncorrelated, indicating that the characteristics of communities that provide high levels of early childhood educational opportunity are not the same as those that provide high opportunities for growth from third to eighth grade. This suggests that the role of schools in shaping educational opportunity varies across school districts. Variation among districts in the two temporal opportunity dimensions implies that strategies to improve educational opportunity may need to target different age groups in different places.

Are public schools in the United States engines of mobility or agents of inequality? Can schools in low-income communities provide a pathway out of poverty, or are the constraints of poverty too great for schools to overcome? Such questions are at the heart of debates about the role of education in social mobility in the United States. Despite decades of research, however, we still lack clear answers.

In this article, I provide new evidence to inform these debates. It suggests that the lack of a clear answer to the question is explained in part by the substantial variation in the role of schooling in shaping educational opportunity across places. Early childhood conditions are more important in some places, educational opportunities during the elementary and middle school years more important in others.

The Geography of Racial/Ethnic Test Score Gaps

The Relationship Between Test Item Format and Gender Achievement Gaps on Math and ELA Tests in 4th and 8th Grade

Test Score Growth Among Chicago Public School Students, 2009-2014

How much do test scores vary among school districts? New estimates using population data, 2009-2015

Why Did So Many Public Schools Stay Remote During the COVID Crisis?

Extreme Measures: A National Descriptive Analysis of Closure and Restructuring of Traditional Public, Charter, and Private Schools

District-Level School Choice and Racial/Ethnic Test Score Gaps

How Do Charter Schools Affect System-Level Test Scores and Graduation Rates? A National Analysis

Evaluating Education Governance: Does State Takeover of School Districts Affect Student Achievement?

Racial and Socioeconomic Test-Score Gaps in New England Metropolitan Areas: State School Aid and Poverty Segregation

Test-score data show that both low-income and racial-minority children score lower, on average, on states’ elementary-school accountability tests compared with higher-income children or white children. While different levels of scholastic achievement depend on a host of influences, such test-score gaps point toward unequal educational opportunity as a potentially important contributor. This report explores the relationship between racial and socioeconomic test-score gaps in New England metropolitan areas and two factors associated with unequal opportunity in education: state equalizing school-aid formulas and geographic segregation of low-income students. The underlying methods do not allow a strict causal interpretation; however, both aspects are strongly related to test-score gaps, with poverty segregation between school districts especially important in New England.

The report first explores the degree to which state school aid is progressive, that is, distributed disproportionately to districts with high fractions of students living in poverty; more progressive distributions are associated with smaller test-score gaps in high-poverty metropolitan areas. All U.S. states distribute some state revenue to support local school districts, but the extent to which such aid is focused on districts with greater concentrations of poverty varies considerably. The relationships estimated in the empirical analysis suggest that New England metro areas with high average district poverty in states with more progressive aid distributions, such as Springfield, Massachusetts, should see somewhat smaller racial and socioeconomic test-score gaps than metro areas with lower district poverty in states with less progressive school aid, such as Burlington, Vermont; that predicted difference in white-Black test-score gaps amounts to about one-quarter of the actual difference between Springfield’s gap and Burlington’s gap.

The second factor explored is poverty segregation; test-score gaps are larger in metropolitan areas where, compared with white children or higher-income children, minority children or lowincome children go to school with, or are in school districts with, more students from low-income families. Partly because school districts (and cities and towns) are relatively small geographically in New England, poverty segregation in the region’s metropolitan areas is most pronounced between districts, not between schools within school districts. The sizes of the estimated relationships suggest that metro areas with the highest between-district poverty segregation, such as BridgeportStamford-Norwalk, Connecticut, should have markedly larger test-score gaps than metro areas with moderate poverty segregation between districts, such as Manchester-Nashua, New Hampshire; those predicted differences amount to 60 percent to 90 percent of the actual test-score gap differences between the Bridgeport and Manchester metro areas.

Parents’ Online School Reviews Reflect Several Racial and Socioeconomic Disparities in K–12 Education

Identifying Progress Toward Ethnoracial Achievement Equity across U.S. School Districts: A New Approach

The Reversal of Mount Laurel’s Regional Contribution Agreements and the Impact on White-Black Academic Achievement Gaps Across New Jersey: 2008–2014

Effects of Four-Day School Weeks on School Finance and Achievement: Evidence from Oklahoma

Status, Growth, and Perceptions of School Quality

The opioid crisis and community-level spillovers onto children’s education

Personal Belief Exemptions for School Entry Vaccinations, Vaccination Rates, and Academic Achievement

Universal Access to Free School Meals and Student Achievement: Evidence from the Community Eligibility Provision

County-Level Rates of Implicit Bias Predict Black-White Test Score Gaps in U.S. Schools

Crime and Inequality in Academic Achievement Across School Districts in the United States

Bias in the Air: A Nationwide Exploration of Teachers' Implicit Racial Attitudes, Aggregate Bias, and Student Outcomes

Are Achievement Gaps Related to Discipline Gaps? Evidence From National Data

The Effects of Student Growth Data on School District Choice: Evidence from a Survey Experiment

Immigration Enforcement and Student Achievement in the Wake of Secure Communities

Seda 2022 special release technical documentation.

Using Pooled Heteroskedastic Ordered Probit Models to Improve Small-Sample Estimates of Latent Test Score Distributions

Can Repeated Aggregate Cross-Sectional Data Be Used to Measure Average Student Learning Rates? A Validation Study of Learning Rate Measures in the Stanford Education Data Archive

Stanford education data archive technical documentation.

The Stanford Education Data Archive (SEDA) is part of the Educational Opportunity Project at Stanford University (https:\edopportunity.org), an initiative aimed at harnessing data to help scholars, policymakers, educators, and parents learn how to improve educational opportunities for all children. SEDA includes a range of detailed data on educational conditions, contexts, and outcomes in schools, school districts, counties, commuting zones, and metropolitan statistical areas across the United States. Available measures differ by aggregation; see Sections I.A. and I.B. for a complete list of files and data.

By making the data files available to the public, we hope that anyone who is interested can obtain detailed information about U.S. schools, communities, and student success. We hope that researchers will use these data to generate evidence about what policies and contexts are most effective at increasing educational opportunity, and that such evidence will inform educational policy and practices.

Validation methods for aggregate-level test scale linking: A case study mapping school district test score distributions to a common scale

Using Heteroskedastic Ordered Probit Models to Recover Moments of Continuous Test Score Distributions from Coarsened Data

Practical Issues in Estimating Achievement Gaps from Coarsened Data

Estimating Achievement Gaps from Test Scores Reported in Ordinal "Proficiency" Categories

Long-Term Trends in Private School Enrollments by Family Income

Income Segregation between Schools and School Districts

60 years after brown: trends and consequences of school segregation.

Since the Supreme Court’s 1954 Brown v. Board of Education decision, researchers and policymakers have paid close attention to trends in school segregation. While Brown focused on black-white segregation, we review the evidence regarding trends and consequences of both racial and economic school segregation. In general, the evidence regarding trends in racial segregation suggests that the most significant declines in black-white school segregation occurred at the end of the 1960s and the start of the 1970s. Although there is disagreement about the direction of more recent trends in racial segregation, this disagreement is largely driven by different definitions of segregation and different ways of measuring it. We conclude that the changes in segregation in the last few decades are not large, regardless of what measure is used, though there are important differences in the trends across regions, racial groups, and institutional levels. Limited evidence on school economic segregation makes documenting trends difficult, but in general, students are more segregated by income across schools and districts today than in 1990. We also discuss the role of desegregation litigation, demographic changes, and residential segregation in shaping trends in both racial and economic segregation.

One of the reasons that scholars, policymakers, and citizens are concerned with school segregation is that segregation is hypothesized to exacerbate racial or socioeconomic disparities in educational success. The mechanisms that would link segregation to disparate outcomes have not often been spelled out clearly or tested explicitly. We develop a general conceptual model of how and why school segregation might affect students and review the relatively thin body of empirical evidence that explicitly assesses the consequences of school segregation. This literature suggests that racial desegregation in the 1960s and 1970s was beneficial to blacks; evidence of the effects of segregation in more recent decades, however, is mixed or inconclusive. We conclude with discussion of aspects of school segregation on which further research is needed.

The widening academic achievement gap between the rich and the poor

Publication: Community Investments: Summer 2012

Almost fifty years ago, in 1966, the Coleman Report famously highlighted the relationship between family socioeconomic status and student achievement. Family socioeconomic characteristics continue to be among the strongest predictors of student achievement, but while there is a considerable body of research that seeks to tease apart this relationship, the causes and mechanisms of this relationship have been the subject of considerable disagreement and debate. Much of the scholarly research on the socioeconomic achievement gradient has focused largely on trying to understand the mechanisms through which factors like income, parental educational attainment, family structure, neighborhood conditions, school quality, as well as parental preferences, investments, and choices lead to differences in children’s academic and educational success. Still, we know little about the trends in socioeconomic achievement gaps over a lengthy period of time.

The question posed in this article is whether and how the relationship between family socioeconomic characteristics and academic achievement has changed during the last fifty years, with a particular focus on rising income inequality. As the income gap between high- and low-income families has widened, has the achievement gap between children in high- and low income families also widened? The answer, in brief, is yes. The achievement gap between children from high- and low-income families is roughly 40 percent larger among children born in 2001 than among those born twenty-five years earlier.

Brown Fades: The End of Court-Ordered School Desegregation and the Resegregation of American Public Schools

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Reframing Educational Outcomes: Moving beyond Achievement Gaps

• Sarita Y. Shukla
• Elli J. Theobald
• Joel K. Abraham
• Rebecca M. Price

School of Educational Studies, University of Washington, Bothell, Bothell, WA 98011-8246

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Department of Biology, University of Washington, Seattle, Seattle, WA 98195

Department of Biological Science, California State University–Fullerton, Fullerton, CA 92831

*Address correspondence to: Rebecca M. Price ( E-mail Address: [email protected] )

School of Interdisciplinary Arts & Sciences, University of Washington, Bothell, Bothell, WA 98011-8246

The term “achievement gap” has a negative and racialized history, and using the term reinforces a deficit mindset that is ingrained in U.S. educational systems. In this essay, we review the literature that demonstrates why “achievement gap” reflects deficit thinking. We explain why biology education researchers should avoid using the phrase and also caution that changing vocabulary alone will not suffice. Instead, we suggest that researchers explicitly apply frameworks that are supportive, name racially systemic inequities and embrace student identity. We review four such frameworks—opportunity gaps, educational debt, community cultural wealth, and ethics of care—and reinterpret salient examples from biology education research as an example of each framework. Although not exhaustive, these descriptions form a starting place for biology education researchers to explicitly name systems-level and asset-based frameworks as they work to end educational inequities.

INTRODUCTION

Inequities plague educational systems in the United States, from pre-K through graduate school. Many of these inequities exist along racial, gender, and socioeconomic lines ( Kozol, 2005 ; Sadker et al. , 2009 ), and they impact the educational outcomes of students. For decades, education research has focused on comparisons of these educational outcomes, particularly with respect to test scores of students across racial and ethnic identities. The persistent differences in these test scores or other outcomes are often referred to as “achievement gaps,” which in turn serve as the basis for numerous educational policy and structural changes ( Carey, 2014 ).

A recent essay in CBE—Life Sciences Education ( LSE ) questioned narrowly defining “success” in educational settings ( Weatherton and Schussler, 2021 ). The authors posit that success must be defined and contextualized, and they asked the community to recognize the racial undercurrents associated with defining success as limited to high test scores and grade point averages (GPAs; Weatherton and Schussler, 2021 ). In this essay, we make a complementary point. We contend that the term “achievement gap” is misaligned with the intent and focus of recent biology education research. We base this realization on the fact that the term “achievement gap” can have a deeper meaning than documenting a difference among otherwise equal groups ( Kendi, 2019 ; Gouvea, 2021 ). It triggers deficit thinking ( Quinn, 2020 ); unnecessarily centers middle and upper class, White, male students as the norm ( Milner, 2012 ); and downplays the impact of structural inequities ( Ladson-Billings, 2006 ; Carter and Welner, 2013 ).

This essay unpacks the negative consequences of using the term “achievement gap” when comparing student learning across different racial groups. We advocate for abandoning the term. Similarly, we suggest that, in addition to changing our terminology, biology education researchers can explicitly apply theoretical frameworks that are more appropriate for interrogating inequities among educational outcomes across students from different demographics. We emphasize that the idea that a simple “find and replace,” swapping out the term “achievement gap” for other phrases, is not sufficient.

In the heart of this essay, we review some of these systems-level and asset-based frameworks for research that explores differences in academic performance ( Figure 1 ): opportunity gaps ( Carter and Welner, 2013 ), educational debt ( Ladson-Billings, 2006 ), community cultural wealth ( Yosso, 2005 ), and ethics of care ( Noddings, 1988 ). Within each of these frameworks, we review examples of biology education literature that we believe rely on them, explicitly or implicitly. We conclude by reiterating the need for education researchers to name explicitly the systems-level and asset-based frameworks used in future research.

FIGURE 1. Research frameworks highlighted in the essay. The column in gray summarizes deficit-based frameworks that focus on achievement gaps. The middle column (in gold) includes examples of systems-based frameworks that acknowledge that student learning is associated with society-wide habits. The rightmost columns (in peach) include examples of asset-based models that associate student learning with students’ strengths. The columns are not mutually exclusive, in that studies can draw from multiple frameworks simultaneously or sequentially.

We will use the phrase “students from historically or currently marginalized groups” to describe the students who have been and still are furthest from the center of educational justice. However, when discussing work of other researchers, we will use the terminology they use in their papers. Our conceptualization of this phrase matches, as near as we can tell, Asai’s phrase “PEERs—persons excluded for their ethnicity or race” ( Asai, 2020 , p. 754). We also choose to capitalize “White” to acknowledge that people in this category have a visible racial identity ( Painter, 2020 ).

Positionality

Our positionalities—our unique life experiences and identities—mediate our understanding of the world ( Takacs, 2003 ). What we see as salient in our research situation arises from our own life experiences. Choices in our research, including the types of data we collect and how we clean the data and prepare it for analysis, adopt analytical tools, and make sense of these analyses are important decision points that affect study results and our findings ( Huntington-Klein et al. , 2021 ). We recognize that it is impossible to be free of bias ( Noble, 2018 ; Obermeyer et al. , 2019 ). Therefore, we put forth our positionality to acknowledge the lenses through which we make decisions as researchers and to forefront the impact of our identities on our research. Still, the breadth of our experiences cannot be described fully in a few sentences.

The four authors of this essay have unique and complementary life experiences that contribute to the sense-making presented in this essay. S.Y.S. has been teaching since 2003 and teaching in higher education since 2012. She is a South Asian immigrant to the United States, and a cisgender woman. E.J.T. has taught middle school, high school, and college science since 2006. She is a cisgender White woman. J.K.A. is a cisgender Black mixed-race man who comes from a family of relatively recent immigrants with different educational paths. He has worked in formal and informal education since 2000. R.M.P. is a cisgender Jewish, White woman, and she has been teaching college since 2006. We represent a team of people who explicitly acknowledge that our experiences influence the lenses through which we work. Our guiding principles are 1) progress over perfection, 2) continual reflection and self-improvement, and 3) deep care for students. These principles guide our research and teaching, impacting our interactions with colleagues (faculty and staff) as well as students. Ultimately, these principles motivate us to make ourselves aware of, reflect on, and learn from our mistakes.

Simply Changing Vocabulary Does Not Suffice

The term “achievement gap” is used in research that examines differences in achievement—commonly defined as differences in test scores—across students from different demographic groups ( Coleman et al. , 1966 ). Some studies replace “achievement gap” with “score gap” (e.g., Jencks and Phillips, 2006 ), because it defines the type of achievement under consideration; others use “opportunity gap,” because it emphasizes differences in opportunities students have had throughout their educational history (e.g., Carter and Welner, 2013 ; more on opportunity gaps later). The shift for which we advocate, however, does not reside only with terminology. Instead, we call for a deeper shift of using research frameworks that acknowledge and respect students’ histories and empower them now.

The underlying framework in research that uses “achievement gap” or even “score gap” may not be immediately apparent. Take for example two studies that both use the seemingly benign term, “score gap.” A close read indicates that one study attributed the difference in test scores between Black and White students to deficient “culture and child-rearing practices” ( Farkas, 2004 , p. 18). Thus, even though the researcher uses what can be considered to be more neutral terminology, the phrase in this context represents deficit thinking and blame. On the other hand, another study uses the term “score gap” to explore differences that have been historically studied through cultures of poverty, genetic, and familial backgrounds ( Jencks and Phillips, 2006 ). While these researchers discuss the Black–White score gap, they present evidence that examines this phenomenon with nuanced constructs, such as stereotype threat ( Steele, 2011 ) and resources available. These authors also mention ways to reduce score gaps, such as smaller class sizes and high teacher expectations ( Jencks and Phillips, 2006 ).

Some researchers who use the phrase “achievement gap” explicitly avoid deficit thinking and instead embrace an asset-based framework. Jordt et al. (2017) address systemic racism, just as Jencks and Phillips (2006) do. Specifically, Jordt et al. (2017) identified an intervention that affirmed student values that might also be a potential tool for increasing underrepresented minority (URM) student exam scores in college-level introductory science courses. The researchers found that this intervention produced a 4.2% increase in exam performance for male URM students and a 2.2% increase for female URM students. Thus, while they use “achievement gap” throughout the paper to refer to racial and gender differences in exam scores, the study focused on ways to support URM student success.

In pursuit of improved language and clarity of intent, the term “achievement gap” should be replaced to reflect the research framework used to interrogate educational outcomes within and across demographic groups.

DEFICIT THINKING

Deficit thinking describes a mindset, or research framework, in which differences in outcomes between members of different groups, generally a politically and economically dominant group and an oppressed group, are attributed to a quality that is lacking in the biology, culture, or mindset of the oppressed group ( Valencia, 1997 ). Deficit thinking has pervaded public and academic discourse about the education of students from different races and ethnicities in the United States for centuries ( Menchaca, 1997 ).

Tenacious deficit-based explanations blame students from historically or currently marginalized groups for lower educational attainment. These falsities include biological inferiority due to brain size or structure ( Menchaca, 1997 ), negative cultural attributes such as inferior language acquisition ( Dudley-Marling, 2007 ), and accumulated deficits due to a “culture of poverty” ( Pearl, 1997 ; Gorski, 2016 ). More recently, lower achievement has been attributed to a lack of “grit” ( Ris, 2015 ) or the propensity for a “fixed” mindset ( Gorski, 2016 ; Tewell, 2020 ). While ideas around grit and mindset have demonstrable value in certain circumstances (e.g., Hacisalihoglu et al. , 2020 ), they fall short as primary explanations for differences in educational outcomes, because they focus attention on perceived deficits of students while providing little information about structural influences on failure and success, including how we define those constructs ( Harper, 2010 ; Gorski, 2016 ). In other words, deficit models often posit students as the people responsible for improving their own educational outcomes ( Figure 1 ).

Deficit thinking, regardless of intent, blames individuals, their families, their schools, or their greater communities for the consequences of societal inequities ( Yosso, 2006 ; Figure 1 ). This blame ignores the historic and structural drivers of inequity in our society, placing demands on members of underserved groups to adapt to unfair systems ( Valencia, 1997 ). A well-documented example of structural inequity is the consistent underresourcing of public schools that serve primarily students of color and children from lower socioeconomic backgrounds ( Darling-Hammond, 2013 ; Rothstein, 2013 ). Because learning is heavily influenced by factors outside the school environment, such as food security, trauma, and health ( Rothstein, 2013 ), schools themselves reflect gross disparities in resourcing based on historic discrimination ( Darling-Hammond, 2013 ). Deficit thinking focuses on student or cultural characteristics to explain performance differences and tends to overlook or minimize the impacts of systemic disparities. Deficit thinking also strengthens the narrative around student groups in terms of shortcomings, reinforces negative stereotypes, and ignores successes or drivers of success in those same groups ( Harper, 2015 ).

Achievement Gaps

The term “achievement gap” has historically described the difference in scores attained by students from racial and ethnic minority groups compared with White students on standardized tests or course exams ( Coleman et al. , 1966 ). As students from other historically or currently marginalized groups, such as female or first-generation students, are increasingly centered in research, the term is now used more broadly to compare any student population to White, middle and upper class, men ( Harper, 2010 ; Milner, 2012 ). Using White men as the basis for comparison comes at the expense of students from other groups ( Harper, 2010 ; Milner, 2012 ). Basing comparisons on the cultural perspectives of a single dominant group leads to “differences” being interpreted as “deficits,” which risks dehumanizing people in the marginalized groups ( Dinishak, 2016 ). Furthermore, centering White, wealthy, male performance means that even students from groups that tend to have higher test scores, like Asian-American students, risk dehumanization as “model minorities” or “just good at math” ( Shah, 2019 ).

Many researchers have highlighted the fact that the term “achievement gap” is a part of broader deficit-thinking models and rooted in racial hierarchy ( Ladson-Billings, 2006 ; Gutiérrez, 2008 ; Martin, 2009 ; Milner, 2012 ; Kendi, 2019 ). Focusing on achievement gaps emphasizes between-group differences over within-group differences ( Young et al. , 2017 ), reifies sociopolitical and historical groupings of people ( Martin, 2009 ), and minimizes attention to structural inequalities in education ( Ladson-Billings, 2006 ; Alliance to Reclaim Our Schools, 2018 ). Gutiérrez (2008) names this obsession with achievement gaps as a “gap-gazing fetish” that draws attention away from finding solutions that promote equitable learning ( Gutiérrez, 2008 ). Under a deficit-thinking model, achievement gaps are viewed as the primary problem, rather than a symptom of the problem ( Gutiérrez, 2008 ), and for decades they have been attributed to different characteristics of the demographics being compared ( Valencia, 1997 ). As such, proposed solutions tend to be couched in terms of remediation for students ( Figure 1 ).

Ignoring the social context of students’ education necessarily limits inferences that can be drawn about their success. Limiting measures of educational success, also conceptualized as achievement, to performance on exams or overall college GPA, often leaves out consideration of other potential data sources ( Weatherton and Schussler, 2021 ; Figure 2 ). This narrow perspective tends to perpetuate the systems of power and privilege that are already in place ( Gutiérrez, 2008 ). The biology education research community can instead broaden its sense of success to recognize the underlying historical and current contexts and the intersections of identities (e.g., racial, gender, socioeconomic) that contribute to those differences ( Weatherton and Schussler, 2021 ).

FIGURE 2. A selection of potential data sources that could inform researchers about within- and between-group differences in educational outcomes. This list does not encompass the full range of possible data sources, nor does it imply a hierarchy to the data. Instead, it reflects some of the diversity of quantitative and qualitative data that are directly linked to student outcomes and that are used under multiple research frameworks.

In biology education research, many papers still use the language of “achievement gap,” even in instances when researchers explicitly or implicitly use other nondeficit frameworks. While some may argue that this language merely describes a pattern, its origin and history is explicitly and inextricably linked to deficit-thinking models ( Gutiérrez, 2008 ; Milner, 2012 ). Thus, we join others in the choice to abandon the term “achievement gap” in favor of language—and frameworks—that align better to the goals of our research and to avoid the limitations and harm that can arise through its use.

Example: Focusing on Achievement Gaps Can Reinforce Racial Stereotypes

Messages of perpetual underachievement can inadvertently reinforce negative stereotypes. For example, Quinn (2020) demonstrated that, when participants watched a 2-minute video of a newscast using the term “achievement gap,” they disproportionately underpredicted the graduation rate of Black students relative to White students, even more so than participants in a control group who watched a counter-stereotypical video. They also scored significantly higher on an instrument measuring bias. Because bias is dynamic and affected by the environment, Quinn concludes that the video discussing the achievement gap likely heightened the bias of the participants ( Quinn, 2020 ).

Education researchers, just like the participants in Quinn’s (2020) study, inadvertently carry implicit bias against students from the different groups they study, and those biases can shift depending on context. Quinn (2020) demonstrates that just using the term “achievement gap” can reinforce the pervasive racial hierarchy that places Black students at the bottom. Researchers, without intending to, can be complicit in a system of White privilege and power if the language and frameworks underlying their study design, data collection, and/or data interpretation are aligned with bias and stereotype. If the goal is to dismantle inequities in our educational systems and research on those systems, the biology education research community must consider the historical and social weight of its literature to address racism head on, as progressive articles have been doing (e.g., Eddy and Hogan, 2014 ; Canning et al. , 2019 ; Theobald et al. , 2020 ).

SYSTEMS-LEVEL FRAMEWORKS

To move away from the achievement gap discourse—because of the history of the term, the perceived blame toward individual students, as well as the deficit thinking the term may imbue and provoke—we highlight some of the other frameworks for understanding student outcomes. We conclude discussion of each framework with an example from education research that can be reinterpreted within it, keeping in mind that multiple frameworks can be applied to different studies. We acknowledge two caveats about these reinterpretations: first, we are adding another layer of interpretation to the original studies, and we cannot claim that the original authors agree with these interpretations; second, each example could be interpreted through multiple frameworks, especially because these frameworks overlap ( Figure 1 ).

In this section, we begin at the systems level by examining opportunity gaps and educational debt. Rather than blaming students or their cultures for deficits in performance, these systems-level perspectives name white supremacy and the concomitant policies that maintain power imbalances as the cause of disparate student experiences.

Opportunity Gaps

The framework of opportunity gaps shifts the onus of differential student performance away from individual deficiencies and assigns solutions to actions that address systemic racism ( Milner, 2012 ; Figure 1 ). Specifically, opportunity gaps embody the difference in performance between students from historically and currently marginalized groups and middle and upper class, White, male students, with primary emphasis on opportunities that students have or have not had, rather than on their current performance (i.e., achievement) in a class ( Milner, 2012 ). Compared with deficit models, the focus shifts from assigning responsibility for the gap from the individual to society ( Figure 1 ).

Some researchers explore opportunity gaps by discussing the structural challenges that students from historically and currently marginalized groups have been facing (e.g., Rothstein, 2013 ). For example, poor funding in K–12 schools leads to inconsistent, poorly qualified, and poorly compensated teachers; few and outdated textbooks ( Darling-Hammond, 2013 ); limited field trips; a lack of extracurricular resources ( Rothstein, 2013 ); and inadequately supplied and cleaned bathrooms ( Darling-Hammond, 2013 ). Additional structural challenges that occur outside school buildings, but impact learning, include poor health and lack of medical care, food and housing insecurity, lead poisoning and iron deficiency, asthma, and depression ( Rothstein, 2013 ).

While the literature about opportunity gaps focuses more on K–12 than higher education ( Carter and Welner, 2013 ), college instructors can exacerbate opportunity gaps by biasing who has privilege (i.e., opportunities) in their classrooms. For example, some biology education literature focuses on how instructors’ implicit biases impact our students, such as by unconsciously elevating the status of males in the classroom ( Eddy et al. , 2014 ; Grunspan et al. , 2016 ).

Example: CUREs Can Prevent Opportunity Gaps.

Course-based undergraduate research experiences (CUREs) are one way to prevent opportunity gaps (e.g., Bangera and Brownell, 2014 ; CUREnet, n.d. ). Specifically, we interpret the suggestions that Bangera and Brownell (2014) make about building CUREs as a way to recognize that some students have the opportunity to participate in undergraduate research experiences while others do not. For example, students who access extracurricular research opportunities are likely relatively comfortable talking to faculty and, in many cases, have the financial resources to pursue unpaid laboratory positions ( Bangera and Brownell, 2014 ). More broadly, when research experiences occur outside the curriculum, they privilege students who know how to pursue and gain access to them. However, CUREs institutionalize the opportunity to conduct research, so that every student benefits from conducting research while pursuing an undergraduate degree.

Educational Debt

Ladson-Billings (2006) submits that American society has an educational debt, rather than an educational deficit. This framework shifts the work of finding solutions to educational inequities away from individuals and onto systems ( Figure 1 ). The metaphor is economic: A deficit refers to current mismanagement of funds, but a debt is the systematic accumulation of mismanagement over time. Therefore, differences in student performances are framed by a history that reflects amoral, systemic, sociopolitical, and economic inequities. Ladson-Billing ( 2006 ) suggests that focusing on debts highlights injustices that Black, Latina/o, and recent immigrant students have incurred: Focusing on student achievement in the absence of a discussion of past injustices does not redress the ways in which students and their parents have been denied access to educational opportunities, nor does it redress the ways in which structural and institutional racism dictate differences in performance. This approach begins by acknowledging the structural and institutional barriers to achievement in order to dismantle existing inequities. This reframing helps set the scope of the problem and identify a more accurate and just lens through which we make sense of the problem ( Cho et al. , 2013 ).

Example: NSF Supports Historically Black Colleges and Universities.

From my own (yet to be published) research, a participant described the HBCU where he studied physics as providing a “dome of security and safety.” In contrast, he recounted that when he attended a predominantly White institution, he constantly needed to be guarded and employ “his body sense,” an act that made him tense, defensive, and unable to listen. ( Rankins, 2019 , p. 50)

Example: Institutions Can Repay Educational Debt.

Institutions can repay educational debt by ensuring that their students have the resources and support structures necessary to succeed. The Biology Scholars Program at the University of California, Berkeley, is a prime example ( Matsui et al. , 2003 ; Estrada et al. , 2019 ). This program, begun in 1992 ( Matsui et al. , 2003 ) and still going strong ( Berkeley Biology Scholars Program, n.d. ), creates physical and psychological spaces that support learning: a study space and study groups, paid research experiences, and thoughtful mentoring. The students recruited to the program are from first-generation, low-socioeconomic status backgrounds and from groups that are historically underrepresented. When the students enter college, they have lower GPAs and Scholastic Aptitude Test scores than their counterparts with the same demographic profile who are not in the program. And yet, when they graduate, students in the Biology Scholars Program have higher GPAs and higher retention in biology majors than their counterparts ( Matsui et al. , 2003 ), perhaps because of the extended social support they receive from peers ( Estrada et al. , 2021 ). Moreover, students in this program report lower levels of stress and a greater sense of well-being ( Estrada et al. , 2019 ).

ASSET-BASED FRAMEWORKS

In this section, we continue to explore frameworks that move away from the achievement gap discourse, now focusing on models that build from students’ strengths. We have chosen two frameworks whose implications seem particularly relevant to and coincident with anti-racist research in biology education: community cultural wealth ( Yosso, 2005 ) and ethics of care ( Noddings, 1988 ). As before, we reinterpret articles from the education literature to illustrate these frameworks, and we once again include the caveats that we extend beyond the authors’ original interpretations and that other frameworks could also be used to reinterpret the examples.

Community Cultural Wealth

One asset-based way to frame student outcomes is to begin with the strengths that people from different demographic groups hold ( Yosso, 2005 ). Rather than focusing on racism, this approach focuses on community cultural wealth. The premise is that everyone can contribute a wealth of knowledge and approaches from their own cultures ( Yosso, 2005 ).

Community cultural wealth begins with critical race theory (CRT; Yosso, 2005 ). CRT illuminates the impact of race and racism embedded in all aspects of life within U.S. society ( Omi and Winant, 2014 ). CRT acknowledges that racism is interconnected with the founding of the United States. Race is viewed in tandem with intersecting identities that oppose dominant ones, and the constructs of CRT emerge by attending to the experiences of people from communities of color ( Yosso, 2005 ). Therefore, the experiences of students of color are central to transformative education that addresses the overrepresentation of White philosophies. CRT calls on research to validate and center these perspectives to develop a critical understanding about racism.

Community cultural wealth builds on these ideas by viewing communities of color as a source of students’ strength ( Yosso, 2005 ). The purpose of schooling is to build on the strengths that students have when they arrive, rather than to treat students as voids that need to be filled: students’ cultural wealth must be acknowledged, affirmed, and amplified through their education. This approach is consistent with those working to decolonize scientific knowledge (e.g., Howard and Kern, 2019 ).

Example: Community Cultural Wealth Can Improve Mentoring.

Thompson and Jensen-Ryan (2018) offer advice to mentors about how to use cultural wealth to mentor undergraduate students in research. They identify the forms of scientific cultural capital that research mentors typically value, finding that these aspects of a scientific identity are closely associated with majority culture. They challenge mentors to broaden the forms of recognizable capital. For example, members of the faculty can actively recruit students into their labs from programs aimed to promote the diversity of scientists, rather than insisting that students approach them with their interest to work in the lab ( Thompson and Jensen-Ryan, 2018 ). They can recognize that undergraduate students may not express an interest in a research career–especially initially—but that research experience is still formative. They can recognize that students who are strong mentors to their peers are valuable members of a research team and that this skill is a form of scientific capital. They can value the diverse backgrounds of students in their labs, rather than insisting that they come from families that have prioritized scientific thinking and research. In sum, the gaps that Thompson and Jensen-Ryan (2018) identify are in research mentors’ attitudes, rather than in student performance.

Assets can also be developed in the classroom. We interpret Parnes et al. ’s (2020) analysis of the Connected Scholars program as stemming from community cultural wealth. The Connected Scholars program normalized help-seeking and increased the help network available to first-generation college students, 90% of whom were racial or ethnic minorities, in a 6-week summer program that bridged students from high school to college. First-generation college students were provided explicit instruction on how to sustain these two types of support. The Connected Scholars intervention promoted help-seeking behaviors and seemed to mediate higher GPAs. Additionally, students in the intervention reported through a survey that they had better relationships with their instructors than students in the control group ( Parnes et al. , 2020 ). In other words, cultural wealth can be amplified in college for first-generation students (see also the Biology Scholars Program, discussed in the Opportunity Gaps section; Matsui et al. , 2003 ; Estrada et al. , 2019 ).

Ethics of Care

As a framework, ethics of care complements community cultural wealth, in that both are asset-based. A key difference is that community cultural wealth focuses on the assets that students bring, and ethics of care focuses on the assets that an instructor brings to create a classroom of respect and confidence in students.

A foundation of biology education research is that instructors want their students to learn, and it is buttressed by literature concerning students’ emotional well-being. For example, the field considers how students with disabilities experience active learning ( Gin et al. , 2020 ) and how group work promotes collaboration and learning ( Wilson et al. , 2018 ). Studies like these echo the philosophy of ethics of care developed by Noddings (1988) .

The premises of teaching through the ethics of care are that everyone—including students and instructors—has both an innate desire to learn and the capacity to nurture ( Pang et al. , 2000 ). In teaching, these premises form the basis for student–instructor relationships. Nieto and Bode (2012) caution against the oversimplification that caring means being nice: the ethics of care encompasses niceness, in addition to articulating high standards of performance. Instructors must also support and respect students as they meet those standards, especially when students did not recognize that they could meet those goals at the outset. This framework is about nurturing students to accomplish more than they thought possible.

Combining an inclusive culture, for example, through positive instructor talk ( Seidel et al. , 2015 ; Harrison et al. , 2019 ; Seah et al. , 2021 ), growth mindset ( Canning et al. , 2019 ), or increased course structure ( Eddy and Hogan, 2014 ), with evidence-based practices for teaching content ( Freeman et al. , 2014 ; Theobald et al. , 2020 ) has garnered recent attention as a way to create a powerful ethic of care in classrooms. For example, instructor talk, that is, what instructors say in class other than the content they are teaching, addresses student affect. Seidel et al. (2015) and Harrison et al. (2019) analyzed classroom transcripts to identify different categories of instructor talk. While further research can probe the impacts of instructor talk on student outcomes, the idea is consistent with the principles of ethics of care: for example, one category of talk describes the instructor–student relationship as one of respect, fostered through statements such as “People are bringing different pieces of experience and knowledge into this question and I want to kind of value the different kinds of experience and knowledge that you bring in” ( Seidel et al. , 2015 , p. 6). Instructor talk also generates a classroom culture of support and validation for marginalized students and overall builds classroom community ( Ladson-Billings, 2013 ).

Example: Departments Can Implement Care.

Gutiérrez (2000) presents an example of an entire department applying ethics of care to support how African-American students learn math. This study is an ethnography of a particularly successful STEM magnet program in a public high school with a population that is majority African American. In her analysis of the math department, Gutiérrez avoids the phrase “achievement gap,” while also recognizing that people outside the school assume a deficit model when considering the students . Instead, she illustrates how researchers can use an asset-based lens to build from knowledge about differences in performance ( Gutiérrez, 2000 ).

Gutierrez ( 2000 ) examines pedagogy that supports African-American students. She documents how a culture of excellence is developed within a school setting that promotes student achievement. This culture is complex, in that there are multiple layers of support that provide students with repertoires for advancement ( Gutiérrez, 2000 )—the emphasis is on how teachers create an environment where students are both challenged through the curriculum and supported along the way. The teachers in this study have a dynamic conception of their students, and they demonstrate a unified commitment to support the broadest array of students at their school. The institution itself, represented in part through the departmental chair, has values that empower teachers to support students, proactive commitment from teachers to find innovative practices to serve students, and a supportive chairperson.

The math department exhibited a student-centered approach that epitomizes ethics of care. The teachers in the math department rotated through all of the courses and were therefore familiar with the entire curriculum. This knowledge helped them support one another, sharing successful strategies and working to improve the courses. It set up an environment in which they prioritized making decisions collectively. This collaboration led to a sense of togetherness among teachers and a sense of investment in individual students’ successes. As a result, the teachers decided to remove less-challenging courses from the curriculum and replaced them with more advanced courses—against the recommendations of the school district. The chair of the department worked with the faculty to support student learning, consider course assignments, and choose topics for and frequency of faculty meetings. The chair also attended to teachers’ emotional needs, for example, by talking to teachers every day, working with teachers to determine the best strategies for evaluating teaching practices, and enacting a teaching philosophy that valued problem solving over achieving correct answers.

The support that the teachers provided each other coincided with strong support for students. For example, students attended the magnet program because they were interested in science; they notably did not have to take entrance exams or maintain a certain GPA. If students struggled with a subject, they received tutoring. The teachers also invited graduates of the program to come back and visit, keeping the students motivated by showing them success.

Example: Biology Instructors Can Adopt an Ethics of Care.

In much of the research on differential performance in our field, researchers focus on identifying strategies that help students, regardless of their histories, in their learning success. This asset-based approach acknowledges that students start at different places, but also that instructors can implement strategies that support all students in a trajectory toward common learning goals. This argument is often posited in terms of inclusive teaching (e.g., Dewsbury and Brame, 2019 ).

Some papers that measure the effect of inclusive teaching practices may use “gap” language, perhaps as a historical artifact of our discipline. These papers emphasize the just mission to “close the gap”—or, in anti-deficit language, for all students to learn the material and perform well on assessments. For example, Theobald et al. (2020) conducted a meta-analysis of undergraduate STEM classes, drawing on 26 studies of courses reporting failure rates (44,606 students) and 15 studies (9238 students) that reported exam scores. Within these samples, they compared instruction in lecture format with instruction using active-learning strategies. The analysis compared the success of students from minoritized groups using these two teaching strategies and found conclusive evidence of the efficacy of active teaching for underrepresented student success in STEM courses. The powerful implication of this study is that college STEM instructors can mitigate some of the effects of oppression that students have experienced in their lifetime.

In another study demonstrating the philosophy of ethics of care, Canning et al. (2019) found narrower racial disparities in performance in courses taught by instructors who had a growth mindset about their students’ ability to learn, compared with instructors who viewed level of achievement as fixed. In fact, they found that the instructor mindset had a bigger impact on student performance than other faculty characteristics ( Canning et al. , 2019 ). While they focused on the negative consequences of instructors’ fixed mindset, the corollary is that a growth mindset can reflect an ethics of care that both motivates students and generates a positive classroom environment.

The successful instructors will also work to recognize their implicit biases and to ensure that they support a growth mindset for all students, regardless of demographic. This is particularly relevant, because implicit biases have “more to do with associations we’ve absorbed through history and culture than with explicit racial animus” ( Eberhardt, 2019 , p. 160). Realizing how our own socialization may have conditioned us to automatically produce harmful but hidden narratives warrants our attention ( Eberhardt, 2019 ).

MOVING FORWARD

Ladson-Billings (2006) reframed the performance of students from historically and currently marginalized groups from achievement gap to educational debt; this reframing has contributed to a movement to critically examine the term. At the same time, however, the term “achievement gap” has become a catchall used by researchers untethered from its deeper historical context.

Researchers choose words to describe their research that reflect their personal worldviews and research frameworks; in turn, these worldviews and frameworks influence future researchers. Every discipline grapples with terminology, and phrases that were common historically may fall out of use. In some instances, the terms themselves no longer suffice, so a simple “search and replace” may be all that is required to address the issue. The term “achievement gap,” however, is tied to specific frameworks that need to be acknowledged and redressed; it affects how research is designed, how results are interpreted, and what conclusions are drawn. Simply replacing “achievement gap” would not address the undermining nature of deficit-based research frameworks.

Researchers who used the term “achievement gap” may not have intended to use a deficit-thinking framework in their study. In fact, as we have demonstrated with our examples, some powerful articles exist in biology education research that used the term and also implicitly used one of the systems-level or asset-based frameworks we identified.

In these examples, we have reinterpreted the results of primary research with the frameworks we identified. This leads to two points of caution. The first is that we are adding another layer of interpretation, one that the original authors may not have intended. The second is that each example could be interpreted through multiple frameworks, especially because these frameworks overlap ( Figure 1 ). For example, Bangera and Brownell (2014) identify barriers to participating in independent undergraduate research experiences. Course-based undergraduate research experiences (CUREs) offer research opportunities to students who previously could not access them. As discussed earlier, we posited CUREs as an example of a way to reduce opportunity gaps. However, we could also have interpreted the act of implementing a CURE as repaying an educational debt by repairing a form of bias typical within the academy ( Figure 1 ).

Addressing educational inequities requires that biology education researchers quantify differences in performance across demographic groups ( Figure 2 ) and must be done with the utmost care. Disaggregating data is necessary, as is analyzing those data with a just framework that dismantles racial hierarchies and carefully considers the sources of data used to understand those inequities. The frameworks we choose affect our analysis; we must avoid the common trap of assuming that quantitative data and data analysis are free from bias. To illustrate the degree of subjectivity that enters data analysis, Huntington-Klein et al. (2021) found that when seven different researchers received copies of the same data set, each reported different levels of statistical significance, including one researcher who found an effect that was opposite to what the others found. Moving away from analyses based on the phrase “achievement gap” will avoid unintentionally reinforcing the racial bias and better reflect the intention of disaggregating data to quantify differences in performance across demographic groups to actively dismantle persistent educational inequities.

In addition to disaggregating and diversifying data on outcomes ( Figure 2 ), the biology education research community must consider how definitions of success may center White, middle-class ways of knowing and performing ( Weatherton and Schussler, 2021 ). In their recent essay, Weatherton and Schussler (2021) reported that, in articles published in LSE between the years 2015 and 2020, the word “success,” when defined, largely meant high GPAs and exam scores. This narrow definition of success prioritizes scientific content, whereas there are additional admirable goals by which success could be measured ( Figure 2 ; see also Weatherton and Schussler, 2021 and references therein). Moreover, the scientific skills that are valued are Eurocentric, rather embodying a diversity of scientific approaches ( Howard and Kern, 2019 ). In addition to the limitations of narrowly defining success as exam performance, it should be noted that tests themselves are not always fair or equitable across all student populations ( Martinková et al. , 2017 ); success measured in this way should be interpreted with caution, particularly when comparing students across different courses, institutions, or identities.

As we discussed earlier, instructors’ and researchers’ deep beliefs about educational success and achievement necessarily impact their actions. For this reason, we propose that interrogating the frameworks we use is necessary and that such interrogation should acknowledge harm that may have been inflicted. While writing this essay, for example, our understandings of the frameworks underlying our own research, teaching, and other engagements have grown. Much like the research studies we discuss, our intentions, actions, and frameworks can be and have been out of alignment. For example, our own actions with respect to departmental policies, course designs, and program structures have not always reflected the principles to which we subscribe. Although this essay focuses on frameworks in research, we provide a list of some questions that we have asked of ourselves and that could catalyze reflection in all areas of our professional work ( Table 1 ).

In conclusion, we have presented four ways to frame differences in academic performance across students from different demographic groups that firmly reject deficit-based thinking ( Figure 1 ). The notions of opportunity gaps and educational debt demonstrate how systems thinking can recognize socio-environmental barriers to student learning. Asset-based frameworks that include community cultural wealth and ethics of care can help identify actions that institutions, instructors, and students can take to meet learning goals. We hope that researchers in the field move forward by 1) avoiding, or at least minimizing, deficit thinking; 2) explicitly stating asset-based and systems-level frameworks that celebrate students’ accomplishments and move toward justice; and 3) using language consistent with their frameworks.

ACKNOWLEDGMENTS

We thank Starlette Sharp and our external reviewers for helpful feedback on this article. We live and work on the lands of the Kizh/Tongva/Gabrieleño, Duwamish, and Willow (Sammamish) People past, present, and future. We also acknowledge the people whose uncompensated labor built this country, including many of its academic institutions.

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Submitted: 3 June 2021 Revised: 20 December 2021 Accepted: 2 February 2022

© 2022 S. Y. Shukla et al. CBE—Life Sciences Education © 2022 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Socioeconomic Inequality and Educational Outcomes pp 7–17 Cite as

A Review of the Literature on Socioeconomic Status and Educational Achievement

• Markus Broer 19 ,
• Yifan Bai 19 &
• Frank Fonseca 19  
• Open Access
• First Online: 16 May 2019

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Part of the book series: IEA Research for Education ((IEAR,volume 5))

The foundations of socioeconomic inequities and the educational outcomes of efforts to reduce gaps in socioeconomic status are of great interest to researchers around the world, and narrowing the achievement gap is a common goal for most education systems. This review of the literature focuses on socioeconomic status (SES) and its related constructs, the association between SES and educational achievement, and differences among educational systems, together with changes over time. Commonly-used proxy variables for SES in education research are identified and evaluated, as are the relevant components collected in IEA’s Trends in International Mathematics and Science Study (TIMSS). Although the literature always presents a positive association between family SES and student achievement, the magnitude of this relationship is contingent on varying social contexts and education systems. TIMSS data can be used to assess the magnitude of such relationships across countries and explore them over time. Finally, the literature review focuses on two systematic and fundamental macro-level features: the extent of homogeneity between schools, and the degree of centralization of education standards and norms in a society.

• Centralization versus decentralization
• Educational inequality
• Forms of capital
• Homogeneity versus heterogeneity
• International large-scale assessment
• Student achievement
• Socioeconomic status
• Trends in International Mathematics and Science Study (TIMSS)

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Educational inequality occurs in multiple forms. Van de Wefhorst and Mijs ( 2010 ) discussed its existence through the inequality of educational opportunity in terms of the influence of social background on students’ test scores, as well as in learning, as expressed by the performance distribution in test scores. According to the authors, these two characteristics of inequality are conceptually different in that an educational system may have equality in terms of dispersion (or variance) in educational achievement but inequality in terms of opportunities; yet, in general, societies that are equal in terms of dispersion are also more equal in terms of opportunities.

Different education systems take part in each cycle of TIMSS, but 25 education systems took part in the grade eight mathematics student assessment in both 1995 and 2015. For these 25 participating systems, the average mathematics achievement score increased by only five score points between 1995 and 2015 (Mullis et al. 2016 ). Focusing only on more recent trends, for the 32 education systems that participated in the grade eight mathematics student assessment in both 2011 and 2015, there was a gain of nine scale score points between 2011 and 2015, suggesting that many of the education systems with the largest gains are those starting from a low base. As there is limited information on family and home background and its relationship with TIMSS international achievement, this spread in achievement is not sufficient to explain why education systems perform differently. Therefore, our study focuses on the other aspect of educational inequality, namely how SES background is related to educational achievement. In the next two sections of this chapter, we review the concept and measurement of socioeconomic status, and the literature regarding the relationship between family SES and student academic achievement. The rest of this chapter focuses on differences between the various education systems and changes in educational inequality over time.

2.1 Socioeconomic Status and Related Constructs and Measures

The American Psychological Association (APA) defines socioeconomic status as “the social standing or class of an individual or group” (APA 2018 ). SES has been commonly used as a latent construct for measuring family background (Bofah and Hannula 2017 ). However, among empirical studies, there is no consensus on how to best operationalize the concept. In many studies, the measurement of SES does not receive much attention, with very limited discussion over why certain indicators were used rather than others (Bornstein and Bradley 2014 ). Liberatos et al. ( 1988 ) argued that there was no one best measure, because the choice of the SES measure depended on the conceptual relevance, the possible role of social class in the study, the applicability of the measure to the specific populations being studied, the relevance of a measure at the time of study, the reliability and validity of the measure, the number of indicators included, the level of measurement, the simplicity of the measure, and comparability with measures used in other studies.

Historically, SES has been conceptualized and measured in various ways. Taussig ( 1920 ) conceptualized SES as the occupational status of the father. Later, Cuff ( 1934 ) adopted a score card proposed by Sims ( 1927 ) as a measure of SES; this included questions about items possessed by the home, parents’ education, father’s occupation, and other relevant information. Moving on from these early studies, development of instruments for measuring SES has become more complicated, including more advanced methods such as factor analysis or model-based approaches (NCES [National Center for Educational Statistics] 2012 ). By the 1980s, one general agreement had emerged: SES should be a composite variable, typically measuring education, income, and occupation, since these three indicators reflect different aspects of family background (Brese and Mirazchiyski 2013 ).

However, collecting this information is known to be challenging. Besides privacy concerns, there are also concerns about information accuracy (Keeves and Saha 1992 ). For example, the National Assessment of Educational Progress (NAEP) in the United States does not collect family income or parental occupation directly from students, as many of them are unable to accurately report such data (Musu-Gillette 2016 ). Similarly, TIMSS decided not to include questions about parental occupation and income because of doubts about the reliability and utility of similar information collected by previous IEA surveys (Buchmann 2002 ). Therefore, the grade eight student questionnaires for TIMSS include only three proxy components for SES: parental education, books at home, and home possessions (such as ownership of a calculator, computer, study desk, or dictionary), with some evolution in the home possession items over time owing to rapid advancements in technology over the 20 years of TIMSS (more recent items include the internet, or computer tablet, for example).

The abstract nature of the concept of SES leaves some room for researchers to decide what proxy variables to use as SES measures. Yang ( 2003 ), for example, found that the possession of a set of household items may be used as SES indicators. Despite variability and limitations in the measurement of SES, its association with student performance has been demonstrated in numerous studies (Sirin 2005 ).

2.2 Family SES and Student Achievement

Theoretical and empirical work has emphasized that family SES has an impact on children’s educational outcomes, examined mechanisms through which family SES is related to children’s achievement, and identified potential pathways behind this relationship, one of which uses three forms of capital: economic, cultural, and social capital (Bourdieu 1986 ; Coleman 1988 , 1990 ). In other words, differences in the availability of these forms of capital Footnote 1 across households eventually lead to disparities in children’s academic achievement (Buchmann 2002 ).

Bourdieu ( 1986 ) posited that capital can present itself in three fundamental forms and that economic capital is the source of all other forms of capital. The other types of capital are treated as transformed and disguised forms of economic capital. Economic capital can be used in pursuit of other forms of capital; for example, family income can be used to pay for organized after-school activities, to access elite educational opportunities, or to build up valuable social networks (Lareau 2011 ). Children from disadvantaged backgrounds are constrained by the financial resources they and their family possess (Crosnoe and Cooper 2010 ). As such, economic capital determines the extent to which parents can offer financial support to children’s academic pursuits.

In addition to economic capital, cultural capital, namely knowledge of cultural symbols and ability to decode cultural messages, helps parents transmit their advantages to children and to reproduce social class (Bourdieu 1986 ). According to Bourdieu ( 1986 ), an individual’s cultural capital can exist in an embodied state as well as in an objectified state. In the embodied state, cultural capital focuses on “physical capital,” where the body itself is a marker of social class, as particular embodied properties exist as a consequence of specific class practices (Tittenbrun 2016 ). Through this state, inequality in socioeconomic class can find expression in embodied ways, such as physical appearance, body language, diet, pronunciation, and handwriting. In the objectified state, inequality is expressed in forms of cultural goods, such as accessibility to pictures, books, dictionaries, and machines. Therefore, in this view, Bourdieu sees the body and cultural goods as forms of currency that result in the unequal accumulation of material resources and, by extension, represent an important contributor to class inequality (Perks 2012 ).

Children from higher social classes also have advantages in gaining educational credentials due to their families. Cultural capital is considered an important factor for school success. Yang ( 2003 ) suggested possession of cultural resources had the most significant impact on students’ mathematics and science achievement in most countries. If cultural resources are differentiated according to family background, and if some cultural resources have more value than others in the education system, it is reasonable to assume that differential achievement is related to an individual’s social class (Barone 2006 ). For example, a student’s social ability and language style, as well as attitudes toward the school curriculum and teachers, may differ according to social class origins (Barone 2006 ). As such, parental school choice in some countries favors children from those families that already possess dominant cultural advantages (i.e., children attending private schools in the United States), thus confirming the cultural inequalities between classes and status groups of families to produce educational inequalities among their children (Shavit and Blossfeld 1993 ). Lareau ( 1987 , 2011 ) further posited that middle-class parents have a different parenting style, which she termed concerted cultivation, fostering their child’s talent through organized activities, while working-class parents tend to have a natural growth parenting style, letting their children create their own activities with more unstructured time. Consequently, middle-class families prepare their children better for school since their parenting style is more valued and rewarded by the school system.

Finally, the possession of social capital reflects the resources contained in social relations, which can be invested with expected benefits (Bourdieu 1986 ). Differences in educational success can be attributed to different levels of existing social capital, which is produced in networks and connections of families that the school serves (Rogošić and Baranović 2016 ). Coleman ( 1988 ) developed a conceptual framework of social capital in which social structure can create social capital, through family, school, and community. The relationships between the family and the community may be used to explain the higher educational achievements of students based on expected achievements with respect to their socioeconomic status (Mikiewicz et al. 2011 ).

In summary, while the overall association between family SES and students’ academic achievement is well documented in theoretical and empirical work, the magnitude of the relationship between family SES and achievement differs across countries. This may be related to differences in education systems and jurisdictions, and societal changes over time.

2.3 Differences in Education Systems and Changes Over Time

In any society, there are two systematic and fundamental macro-level features that highlight the differences in education systems and how they have changed over time. First, is the extent of homogeneity among education systems. Second, is the degree of centralization of education standards and norms in a society. The association between family background and children’s achievement depends on the education system and the social context (i.e., the level of homogeneity and centralization). Where educational inequality is prominent, students from different backgrounds may demonstrate larger achievement gaps.

2.3.1 Homogeneous Versus Heterogeneous

Previous research has shown that students at lower levels of SES perform better in education systems with lower levels of inequality than their counterparts in countries with more significant SES differences (Ornstein 2010 ). That is, some education systems are more homogeneous than others, with schools being more similar to each other in terms of funding. As an example, Finnish households have a narrow distribution of economic and social status at the population level and their schools show little variation in terms of funding (Mostafa 2011 ).

Furthermore, Mostafa ( 2011 ) found that school homogeneity on a large scale is a source of equality since it diminishes the impact of school characteristics on performance scores. Finland is often seen as an example of a homogeneous education system with high levels of similarity between schools, which in turn reduces the impact of school variables on performance scores (Kell and Kell 2010 ; Mostafa 2011 ). More specifically, Montt ( 2011 ) examined more than 50 school systems, including Finland, in the 2006 cycle of PISA and found that greater homogeneity in teacher quality decreased variability in opportunities to learn within school systems, potentially mitigating educational inequality in achievement.

By contrast, Hong Kong has a relatively high-income disparity compared to other societies (Hong Kong Economy 2010 ). However, the relationship between socioeconomic status and mathematics achievement was found to be the lowest among the education systems participating in the 2012 cycle of PISA (Ho 2010 ; Kalaycıoğlu 2015 ). This suggests that, despite diversity in their SES background, most students from Hong Kong access and benefit from the education system equally. Hong Kong’s high performance in reading, mathematics, and science also suggests the average basic education is of high quality (Ho 2010 ).

However, in many other countries with heterogeneous education systems, educational inequality has manifested itself primarily through the stratification of schools on the basis of socioeconomic composition, resource allocation, or locale. For example, unlike schooling in many other countries, public schooling policies in the United States are highly localized. Local property taxes partially finance public schools, school assignments for students depend on their local residence, and neighborhoods are often divided by racial and socioeconomic background (Echenique et al. 2006 ; Iceland and Wilkes 2006 ). Cheema and Galluzzo ( 2013 ) confirmed the persistence of gender, racial, and socioeconomic gaps in mathematics achievement in the United States using PISA data from its 2003 cycle. Inequalities in children’s academic outcomes in the United States are substantial, as children begin school on unequal terms and differences accumulate as they get older (Lareau 2011 ; Lee and Burkam 2002 ).

In Lithuania, there has also been a growing awareness that an ineffectively organized or poorly functioning system of formal youth education increases the social and economic divide and the social exclusion of certain groups (Gudynas 2003 ). To ensure the accessibility and quality of educational services in Lithuania, special attention has traditionally been paid to a student’s residential location. Gudynas ( 2003 ) suggested that the achievement of pupils in rural schools in Lithuania was lower than that of pupils in urban schools, with the difference being largely explained by the level of parental education in rural areas, which was on average lower than that of urban parents. Similarly, in New Zealand, residential location is considered to be a barrier to educational equality. Kennedy ( 2015 ) observed that students residing in rural residential areas on average tend to have lower SES than those in urban areas, and receive a considerably shorter education than their counterparts living in urban centers, thereby promoting SES disparities in access to education.

In the Russian Federation, Kliucharev and Kofanova ( 2005 ) noted that the inequality between well-off and low-income individuals regarding access to education has been increasing since the turn of the century. According to Kosaretsky et al. ( 2016 ), the greatest inequality in educational access in the Russian Federation was observed in the 1990s, where the rising number of educational inequalities was largely determined by the accelerating socioeconomic stratification of the population, as well as significant budget cuts to education. Although the state articulated policies aiming for universal equality of educational opportunities, they argued that the policies were not implemented with the required financial and organizational support. As a result, in the immediate post-Soviet era, the Russian Federation has observed increasing educational inequality and some loss of achievement compared to the Soviet period.

A final example is Hungary. Horn et al. ( 2006 ) noted that OECD’s PISA studies in the early 2000s highlighted the need for the Hungarian school system to improve both in effectiveness and equality. They contended that achievement gaps among schools make the Hungarian education system one of the most unequal among the participating countries in the PISA 2000 and 2003 cycles. The variation in performance between schools in Hungary is alarmingly large, about twice the OECD average between-school variance (OECD 2004 ). By contrast, the within-school variance is less pronounced, suggesting that students tend to be grouped in schools with others sharing similar characteristics. In other words, students’ achievement gaps seemingly mirror the differences in socioeconomic backgrounds of students across different schools (OECD 2001 , 2004 ). In recent years, persistent education performance gaps with regard to socioeconomic background of students have been observed in Hungary, with 23% of the variation in students’ mathematics performance being explained by differences in their SES background, well above the average of 15% for OECD countries (OECD 2015 ).

2.3.2 Centralized Versus Decentralized

In addition to differences in homogeneity, education systems can be classified as centralized or decentralized. A centralized education system is one that would have centralized education funding (e.g., at the national level) across the education system with little local autonomy, while in decentralized education systems, municipalities oversee school funding for both public and private schools (Böhlmark and Lindahl 2008 ; Oppedisano and Turati 2015 ). Centralization generally leads to the standardization of curriculum, instruction, and central examinations in an education system, and can be helpful in reducing inequalities since it mitigates the influence of a student’s family background (Van de Wefhorst and Mijs 2010 ). By contrast, high levels of decentralization can create greater disparities between schools, especially when the level of funding is determined by the local context (Mostafa 2011 ).

Sweden is an example of a decentralized education system that was centralized until the implementation of wide-reaching reforms in the early 1990s (Hansen et al. 2011 ). The previously centralized Swedish school system has been thoroughly transformed into a highly decentralized and deregulated one, with a growing number of independent schools and parental autonomy in school choice (Björklund et al. 2005 ). Concurrently, examining multi-level effects of SES on reading achievement using data from IEA’s Reading Literacy Study from 1991 and PIRLS data from 1991 to 2001, the SES effect appears to have increased in Sweden over time, with between-school differences being greater in 2001 than in 1991, suggesting school SES has a strong effect (Hansen et al. 2011 ).

Similarly, there has also been growing debate about educational inequality in the Republic of Korea in recent years. By analyzing grade eight TIMSS data from the 1999, 2003, and 2007 cycles of the assessment, Byun and Kim ( 2010 ) found the contribution of SES background on student achievement had increased over time. They suspected the higher educational inequality might be related to various factors, including a widening income gap and recent educational reforms geared toward school choice, as well as increased streaming by academic ability and curriculum differentiation created by a decentralized education system.

Researchers have found evidence to support the view that decentralized education systems in developed countries perform better than centralized systems in terms of reducing students’ achievement inequality (see, e.g., Rodríguez-Pose and Ezcurra 2010 ). Conversely, Causa and Chapuis ( 2009 ) used PISA data for the OECD countries to confirm that decentralized school systems were positively associated with equity in educational achievement. Furthermore, according to PISA 2000 and 2006, in European countries inequality in educational outcomes has apparently declined in decentralized school systems, while it has concomitantly increased in centralized systems (Oppedisano and Turati 2015 ).

Mullis et al. ( 2016 ) argued that efficiency and equality can work together. They found that many countries have improved their TIMSS national averages while also reducing the achievement gap between low- and high-performing students. Similarly, an analysis using TIMSS scores from 1999 and 2007 discovered a prominent inverse relation between the within-country dispersion of scores and the average TIMSS performance by country (Freeman et al. 2010 ; Mullis et al. 2016 ). The pursuit of educational equality does not have to be attained at the expense of equity and efficiency.

In conclusion, the positive association between family background and children’s achievement is universal. However, the magnitude of such associations depend on the social context and education system. In other words, the achievement gap between students from different backgrounds is more pronounced in education systems where overall inequality (e.g., income inequality) is strong. Narrowing the achievement gap is a common goal for most education systems. But it is well understood that stagnant scores for low-SES students and declines in the scores of high-SES students should not be seen as an avenue for enhancing equality. Rather, education systems should strive for equality by improving the performance of all students while focusing on improving the achievement of low-SES students at a faster rate to reduce gaps in achievement (Mullis et al. 2016 ). In recognition of this, our study not only focuses on how inequalities in educational outcomes relate to socioeconomic status over time for select participating education systems in TIMSS but also tracks the performance of low-SES* Footnote 2 students separately. In order to make a comparable trend analysis, we first constructed a consistent measure of family SES* based on a modified version of the TIMSS HER. Chapter 3 describes the data and methods used in the study and Chap. 4 presents the trends in SES* achievement gaps of the 13 education systems that participated in three cycles of TIMSS, including the 1995 and 2015 cycles.

Note that family socioeconomic status is clearly related to Bourdieu’s theory of capital in the empirical world. Conceptually, however, they do not equate with each other.

The SES measure used in this study is a modified version of the TIMSS home educational resources (HER) index and does not represent the full SES construct, as usually defined by parental education, family income, and parental occupation. In this report, we therefore term our measure SES* to denote the conceptual difference (Please refer to Chap. 1 for more details).

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Achievement at school and socioeconomic background—an educational perspective

• Sue Thomson 1  

npj Science of Learning volume  3 , Article number:  5 ( 2018 ) Cite this article

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INTRODUCTION

Educational achievement, and its relationship with socioeconomic background, is one of the enduring issues in educational research. The influential Coleman Report 1 concluded that schools themselves did little to affect a student’s academic outcomes over and above what the students themselves brought to them to school—‘the inequalities imposed on children by their home, neighbourhood and peer environment are carried along to become the inequalities with which they confront adult life at the end of school’ (p. 325). Over the intervening 50 years, much has been added to the research literature on this topic, including several high-quality meta-analyses. It has become ubiquitous in research studies to use a student’s socioeconomic background, and that of the school they attend, as contextual variables when seeking to investigate potential influences on achievement.

The two articles in this issue of Science of Learning touch on aspects of this discussion rarely included in the educational research literature. The article by Smith–Wooley et al. 2 asks whether whether it is the influence of the student socioeconomic background that is the greater influence or whether the parents are passing down intellectually advantageous genes to their offspring. In contrast, the article by van Dongen et al. 3 suggests that that it is likely a combination of genetics and socioeconomic background, and they examine the effect of environment on the epigenetic status of genes that are involved in learning and memory.

What do we mean by socioeconomic background?

The definition of socioeconomic background used varies widely, even across educational research. In the Organisation for Economic Cooperation and Development’s (OECD) rigorous large-scale international assessment of more than 70 countries over 15 years, the Programme for International Student Assessment (PISA), socioeconomic background is represented by the index of Economic, Social and Cultural Status, which is a composite score derived by principal components analysis and is comprised of the International Socioeconomic Index of Occupational Status; the highest level of education of the student’s parents, converted into years of schooling; the PISA index of family wealth; the PISA index of home educational resources; and the PISA index of possessions related to 'classical' culture in the family home. 4

However, examining Sirin’s 5 meta-analysis of the research into socioeconomic status and academic achievement finds that many studies use a combination of one or more of parental education, occupation and income, others include parental expectations, and many simply use whether the student gets a free or reduced-price lunch. The latter factor is most commonly used as it is readily available from school records rather than having to ask questions about occupation and education of students or parents, yet Hauser 6 as well as Sirin have argued that it is conceptually problematic and should not be used. Other studies have used family structure, 7 , 8 family size, 9 and even residential mobility. 10

Sirin’s meta-analysis, however, found that the traditional definitions of socioeconomic background were not as strongly related to educational outcomes for students from different ethnic backgrounds, for those from rural areas, or for migrants. Its use in developing countries is particularly problematic. For example, in examining the effect of household wealth on educational achievement, Filmer & Pritchett 11 found that many poor children in developing countries either never enrol in school or attend to the end of first grade only. Even within developing countries, the gap in enrolment and achievement between rich and poor was found to be only a year or two, in other countries 9 or 10 years. Often in developing countries low achievement and enrolment is attributable to the physical unavailability of schools.

Similarly education achievement is measured in many ways—achievement on a set test in certain subject areas, completion of numbers of years of schooling, entrance to university, for example.

What does this mean for educators when they are reviewing the research? It means that they need to exercise some caution. The results and the conclusions will obviously vary, as the research is, essentially, looking at different influencers and not the same influence each time. So, when the argument is made that the relationship is not stable, this may well be because the variable under consideration is different.

School-level socioeconomic background

While the Coleman Report concluded that schools themselves added little to effect outcomes, the school environment, in particular the social background of a student’s peers at the school, has certainly been found to be positively related to student achievement. On average, a student who attends a school in which the average socioeconomic status is high enjoys better educational outcomes compared to a student attending a school with a lower average peer socioeconomic level. 12 , 13

Relationship between achievement and student socioeconomic background

There is some discussion about the size of the effect, however the relationship between a student’s socioeconomic background and their educational achievement seems enduring and substantial. Using data from PISA, the OECD have concluded that 'while many disadvantaged students succeed at school … socioeconomic status is associated with significant differences in performance in most countries and economies that participate in PISA. Advantaged students tend to outscore their disadvantaged peers by large margins' (p. 214). 14 The strength of the relationship varies from very strong to moderate across participating countries, but the relationship does exist in each country. In Australia, students from the highest quartile of socioeconomic background perform, on average, at a level about 3 years higher than their counterparts from the lowest quartile. 15 Over the 15 years of PISA data currently available, the size of this relationship, on average, has changed little, and over the now 50 years since the publication of the Coleman Report, the gap between advantaged and disadvantaged students remains.

How are these effects transmitted?

What the continued gap between advantaged and disadvantaged students highlights is that despite all the research, it is still unclear how socioeconomic background influences student attainment.

There are those that argue that the relationships between socioeconomic background and educational achievement are only moderate and the effects of SES are quite small when taking into account cognitive ability or prior achievement. 16 Cognitive ability is deemed to be a genetic quality and its effects only influenced to a small degree by schools. Much of the body of research, particularly that generated from large-scale international studies, would seem to contradict this reasoning.

Others have argued that students from low socioeconomic level homes are at a disadvantage in schools because they lack an academic home environment, which influences their academic success at school. In particular, books in the home has been found over many years in many of the large-scale international studies, to be one of the most influential factors in student achievement. 15 From the beginning, parents with higher socioeconomic status are able to provide their children with the financial support and home resources for individual learning. As they are likely to have higher levels of education, they are also more likely to provide a more stimulating home environment to promote cognitive development. Parents from higher socioeconomic backgrounds may also provide higher levels of psychological support for their children through environments that encourage the development of skills necessary for success at school. 17

The issue of how school-level socioeconomic background effects achievement is also of interest. Clearly one way is in lower levels of physical and educational resourcing, but other less obvious ways include lower expectations of teachers and parents, and lower levels of student self-efficacy, enjoyment and other non-cognitive outcomes. 15 There is also some evidence that opportunity to learn (particularly in mathematics) is more restricted for lower socioeconomic students, with ‘systematically weaker content offered to lower-income students [so that] rather than ameliorating educational inequalities, schools were exacerbating them’. 18

Conclusions

If the role of education is not simply to reproduce inequalities in society then we need to understand what the role of socioeconomic background more clearly. While much research has been undertaken in the past 50 years, and we are fairly certain that socioeconomic background does have an effect on educational achievement, we are no closer to understanding how this effect is transmitted. Until we are, it will remain difficult to address. In this edition of Science of Learning, two further contributions to this body of knowledge have been added—and perhaps indicate new paths that need to be followed to develop this understanding.

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Schmidt, W. H., Burroughs, N. A., Zoido, P., & Houang, R. T. The role of schooling in perpetuating educational inequality: an international perspective. Educ. Res. 44 , https://doi.org/10.3102/0013189X15603982 (2015).

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Thomson, S. Achievement at school and socioeconomic background—an educational perspective. npj Science Learn 3 , 5 (2018). https://doi.org/10.1038/s41539-018-0022-0

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Generative Artificial Intelligence in education: Think piece by Stefania Giannini

Artificial Intelligence tools open new horizons for education, but we urgently need to take action to ensure we integrate them into learning systems on our terms. That is the core message of UNESCO’s new paper on generative AI and the future of education . In her think piece, UNESCO Assistant Director-General for Education, Stefania Giannini expresses her concerns that the checks and balances applied to teaching materials are not being used to the implementation of generative AI. While highlighting that AI tools create new prospects for learning, she underscores that regulations can only be built once the proper research has been conducted.

Readiness of schools to regulate the use of AI tools in education

In May, a UNESCO global survey of over 450 schools and universities found that fewer than 10% have developed institutional policies and/or formal guidance concerning the use of generative AI applications. The paper observes that in most countries, the time, steps and authorizations needed to validate a new textbook far surpass those required to move generative AI utilities into schools and classrooms. Textbooks are usually evaluated for accuracy of content, age-appropriateness, relevance of teaching and accuracy of content, cultural and social suitability which encompasses checks to protect against bias, before being used in the classroom.

Education systems must set own rules

The education sector cannot rely on the corporate creators of AI to regulate its own work. To vet and validate new and complex AI applications for formal use in school, UNESCO recommends that ministries of education build their capacities in coordination with other regulatory branches of government, in particular those regulating technologies.

Potential to undermine the status of teachers and the necessity of schools

The paper underscores that education should remain a deeply human act rooted in social interaction. It recalls that during the COVID-19 pandemic, when digital technology became the primary medium for education, students suffered both academically and socially. The paper warns us that generative AI in particular has the potential to both undermine the authority and status of teachers, and to strengthen calls for further automation of education: Teacher-less schools, and school-less education. It emphasizes that well-run schools, coupled with sufficient teacher numbers, training and salaries must be prioritized.

Education spending must focus on fundamental learning objectives

The paper argues that investment in schools and teachers, is the only way to solve the problem that today, at the dawn of the AI Era, 244 million children and youth are out of school and more than 770 million people are non-literate. Evidence shows that good schools and teachers can resolve this persistent educational challenge – yet the world continues to underfund them.

UNESCO’s response to generative AI in education

UNESCO is steering the global dialogue with policy-makers, EdTech partners, academia and civil society. The first global meeting of Ministers of Education took place in May 2023 and the Organization is developing policy guidelines on the use of generative AI in education and research, as well as frameworks of AI competencies for students and teachers for school education. These will be launched during the Digital Learning Week , which will take place at UNESCO Headquarters in Paris on 4-7 September 2023. The UNESCO Global Education Monitoring Report 2023 to be published on 26 July 2023 will focus on the use of technology in education.

UNESCO’s Recommendation on the Ethics of Artificial Intelligence

UNESCO produced the first-ever global standard on AI ethics – the ‘Recommendation on the Ethics of Artificial Intelligence’ in November 2021. This framework was adopted by all 193 Member States. The Recommendation stresses that governments must ensure that AI always adheres to the principles of safety, inclusion, diversity, transparency and quality.

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PERSPECTIVE article

This article is part of the research topic.

Dental Education Beyond Borders and Dentists of the Future

Dental education and practice: past, present, and future trends Provisionally Accepted

• 1 Department of Molecular Pathology, College of Dentistry, New York University, United States

The final, formatted version of the article will be published soon.

This position paper explores the historical transitions and current trends in dental education and practice and attempts to predict the future. Dental education and practice landscape, especially after the COVID-19 epidemic, are at a crossroads. Four fundamental forces are shaping the future: the escalating cost of education, the laicization of dental care, the corporatization of dental care, and technological advances. Dental education will likely include individualized, competency-based, asynchronous, hybrid, face-to-face, and virtual education with different start and end points for students. Dental practice, similarly, will be hybrid, with both face-to-face and virtual opportunities for patient care. Artificial intelligence will drive efficiencies in diagnosis, treatment, and office management.

Keywords: dental, Dentists, Education, future, Oral Health, Practice, trend

Received: 10 Jan 2024; Accepted: 02 Apr 2024.

Copyright: © 2024 Spielman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Andrew I. Spielman, New York University, Department of Molecular Pathology, College of Dentistry, New York City, United States

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Open Accessibility in Education Research: Enhancing the Credibility, Equity, Impact, and Efficiency of Research

Jesse i. fleming.

1 University of Virginia

Sarah E. Wilson

Sara a. hart.

2 Florida State University

William J. Therrien

Bryan g. cook.

Openness is a foundational principle in science. Making the tools and products of scientific research openly accessible advances core aims and values of education researchers, such as the credibility, equity, impact, and efficiency of research. The digital revolution has expanded opportunities for providing greater access to research. In this article, we examine three open-science practices—open data and code, open materials, and open access—that education researchers can use to increase accessibility to the tools and products of research in the field. For each open-science practice, we discuss what the practice is and how it works, its primary benefits, some important limitations and challenges, and two thorny issues.

In the fields of education and educational psychology (which we refer to collectively in this paper as education for the sake of brevity) a primary purpose of research is to generate credible evidence to inform practice, policy, and theory, with the end goal of improving learner outcomes. Many challenges exist to fulfilling this purpose. For example, collecting robust data sets and generating sound research materials require significant resources that many scholars do not have. Data and analytic code are seldom made publicly available to reproduce, examine the robustness of, and extend reported findings. A considerable portion of the published research base is behind a paywall, inaccessible to many of the policymakers and practitioners it is intended to benefit. Such issues can compromise the credibility (i.e., validity and legitimacy), equity (i.e., fair and impartial access to the process and products of research), impact (i.e., effect of research on policy, practice, theory, and other research), and efficiency (i.e., maximizing utility and minimizing unnecessary effort and expense) of education research. In this paper, we discuss how making key research tools and products openly accessible (i.e., open data, open materials, and open access to publications) can help increase the credibility, impact, equity, and efficiency of education research. We define openness as the accessibility of research tools and products, including research reports (i.e., manuscripts) but also data and materials, to both researchers and research consumers.

One component of the scientific revolution in the 17 th century is the openness of science afforded by the invention of the printing press and the advent of scientific journals ( Bartling & Friesike, 2014 ; Fecher & Friesike, 2014 ; Stracke, 2020 ). Publications appearing in scientific journals, such as the Royal Society’s Philosophical Transactions – published since 1665, enabled scientists to provide greater access to research findings than had been previously possible. Indeed, “publication” comes from the Latin publicatio , meaning “making public” ( Bartling & Friesike, 2014 ). Such openness extended the influence of scientists’ work and provided research consumers some opportunity to examine the methods used in reported research. Making public study methods is consistent with the empirical and skeptical stance of enlightenment scientists, as reflected in the motto of the Royal Society, adopted in 1663, nullius in verba or “take nobody’s word for it.” That is, credibility is based on verifiable, empirical research rather than authority or tradition.

Openness became a fundamental principle in science, as reflected in Merton’s (1973 [1942]) norm of communism in which scientific knowledge is considered “common property” (p. 274). Open collaboration between contemporaries, as well as between past and present generations, enables scientists to stand on one another’s metaphorical shoulders when developing knowledge bases. Openness is also consistent with Merton’s norm of universalism, which holds that “free access to scientific pursuits is a functional imperative” (p. 272). Access to scientific knowledge and tools, as well as the merit of scientific findings, should not depend on individuals’ attributes (e.g., status, nationality). Indeed, Merton suggested the norm of universalism represents the ethos of democratization, which he defined as the “elimination of restraints upon the exercise and development of socially valued capacities” (p. 273), in science.

Although journal publishing represented a significant leap in the openness and accessibility of science in the 1600s, the basic structure of publishing research has changed relatively little since, resulting in limited accessibility of the tools and products of science. For example, most published research lies behind a paywall and is not freely accessible unless one, or one’s institution, subscribes to the publisher ( Piwowar et al., 2018 ). Moreover, full data, analytic code, and materials used in research publications are not commonly made accessible. The advent of the internet has ushered in the possibility of a new era of openness in science, often referred to as open science, by providing the means to provide open access to research data, materials, and reports. Whereas open science encompasses a range of practices (see Gehlbach & Robinson, 2021 /this issue; Reich, 2021 /this issue), our focus in this paper is on reforms to make the tools and products of science broadly and equitably available, consistent with the democratic school of open science ( Fecher & Friesike, 2014 ).

We posit that increased access to the tools and products of science can benefit both education researchers and research consumers by enhancing the credibility, equity, impact, and efficiency of education research. For example, when an education researcher provides open access to the data and code used in a study, they enable other researchers to (a) independently analyze their data to verify the accuracy of reported analyses (i.e., enhance credibility), (b) conduct data-based research (i.e., enhance equity), (c) examine research questions beyond those of interest to the original researcher (i.e., enhance impact of the data), and (d) use their analytic code to examine similar research questions rather than having to develop their own (i.e., enhance efficiency). Although credibility, equity, impact, and efficiency are important goals in education research and the digital infrastructure exists to enable open practices, open science is not yet established as common practice among education researchers.

In this article, we describe and explore applications of open data (and analytic code), open materials, and open access (to scholarly manuscripts) in education research. Specifically, for each practice, we provide a brief overview of the practice, describe its primary benefits and limitations, and raise and discuss thorny issues that we expect will arise frequently but will not be easily resolved when education researchers apply the practice. Although we provide basic information regarding each of the open-science practices and how they work, this is not a how-to guide (see Open Science Framework [n.d.] and Soderberg [2018] for examples of how-to guides). Rather, our main intent is to (a) elucidate primary benefits and limitations, and (b) wrestle with and provide potential solutions to thorny issues for education researchers who are familiar with these open-science practices and are considering applying them. To introduce issues related to open science in education research, we use an extended vignette featuring Dr. Thompson, a hypothetical early-career education researcher.

Dr. Thompson received her Ph.D. in educational psychology from a research-intensive university. She is a mixed-methods researcher. She conducted an exploratory sequential design for her dissertation in which she interviewed a small purposeful sample of secondary students with disabilities using a protocol she adapted from another researcher, and then surveyed a larger and more representative sample regarding the role of self-advocacy in students’ identities and schooling. Dr. Thompson is now in the beginning of her second year as a faculty member at a large state college. She and her other early-career colleagues are feeling pressure to publish in high-tier journals to be promoted and tenured in the near future.

When she was a doctoral student, one of her committee members was an advocate of open science and had coached her to post the manuscript that came out of her dissertation as a preprint and share her survey as supplementary material when she published that article. Her professor had convinced her that open science benefited the field and it seemed to Dr. Thompson to be the right thing to do. However, she now felt considerable pressure to focus on accumulating publications in journals with high impact factors, and taking additional time to make her research open just didn’t seem a luxury she could afford. Moreover, when she thought about engaging in open data, open materials, and open access with the research she was conducting now, she had some questions about whether and how she could make her work open.

Open Data and Code

Dr. Thompson’s departmental mentor has told her that it is important for her to not publish with her doctoral advisor, so that she can show her own independent line of research for promotion and tenure. Dr. Thompson is concerned, because she did not have startup funds to collect her own data and funding rates are low, especially for early-career scholars. After seeing a conference presentation on open data, Dr. Thompson realized there were publicly shared datasets posted to data repositories she might be able to use to answer her own research questions. However, most of the data she found were quantitative, and for some of her key research questions she needs qualitative data. Remembering her doctoral committee member who advocated for open science, Dr. Thompson would also like to deposit the data she collected for her dissertation research into a data repository for others to use. However, she is concerned about how to ensure that her participants will not be identifiable to other researchers, particularly for her qualitative data.

What Are Open Data and Code?

Data are considered open when an investigator makes their cleaned (including fully deidentified), raw dataset available on a public repository or website, such as the Open Science Framework (OSF; https://osf.io/ ), the Inter-University Consortium for Political and Social Research (ICPSR; https://www.icpsr.umich.edu/web/pages/ ), and LDBase ( https://ldbase.org ). Often, researchers share data limited to the analyses reported in a specific paper, allowing others to re-analyze and reproduce those analyses. However, open data can also include the full data of a project, which we suggest is the gold standard to open the full power of data reuse and allow for the investigation of new research topics and questions. It is essential that researchers include detailed metadata accompanying open data. In the context of education research, metadata typically includes data documentation, such as codebooks, as well as a description of the data collection and the participants. Metadata allows others to understand one’s data and determine whether and how the data can be used for their needs ( Day, 2005 ). Preferably, open data are posted in a non-commercial format that allows for free and open access (e.g., .csv). We recommend Logan et al. (in press) for an in-depth description of what data sharing is and a step-by-step on how to do it, and Meyer (2018) for a general review of best practices.

Open code typically involves posting the code, or syntax, used to run all the analyses presented in a published paper on a public repository or website. However, open code can also include the code used to prepare final cleaned datasets, which would likely include code to check the variable properties (e.g., checking for out-of-range values that sometimes occur in data entry), recoding variables, and the creation of new variables (e.g., the mean score of a questionnaire). Preferably, the code is fully annotated in a non-commercial format (e.g., .txt) to allow easy and broad reuse.

Open data and code provide benefits to (a) education research and subsequently to the students, educators, and other stakeholders impacted by education research; (b) the data user; and (c) to the data depositor. The loftiest benefit is to the broad research and stakeholder communities. Open data and code can enhance the credibility of research by facilitating re-analysis and replication of reported study results, which helps the education community know what findings are credible and should be considered for translation into policy and practice. That is, other researchers can confirm the accuracy of reported findings by independently conducting the same analyses with the same data. Indeed, a recent Pew Research Center survey reported that among a sample of 3,627 U.S. adults, 57% said that they trust scientific research findings more when data are openly available ( Funk, 2020 ).

Additionally, open data allow independent researchers to conduct robustness analyses that examine the same research questions posited in the original study using the same data set but different analyses. Open data and code also allow for testing totally new research questions, which facilitates creative solutions to difficult problems ( Schofield et al., 2009 ; Vision, 2010 ). Take for example the Child Language Data Exchange System (CHILDES), which was a pioneer when it started in 1984 as an open-data repository of language acquisition corpora data. By 2000, the 230 corpora datasets within CHILDES had been used to publish over 5000 papers ( MacWhinney, 2000 ). The contribution of CHILDES to the science of the language-acquisition community, and consequent benefits for educators and children, is substantial and beyond what could have been achieved by just the original research team. In this way, open data also enhance the impact and efficiency of research.

The second benefit of open data and code is to the data user. Given tight funding lines and diminishing research resources in an era of increasing research expectations, more education researchers find themselves unable to collect high-quality data to test their research questions. Open data provide education researchers data to support their research and careers, contributing to a more equitable research environment ( Mangul et al., 2019 ). Indeed, open data democratize who can participate in science and, as such, diversifies the perspectives and approaches to posing and examining research questions in our field ( Hall et al., 2008 ). Open data also benefits researchers conducting meta-analyses by allowing them to access raw data instead having to rely on summary statistics typically reported in published articles (see Patall, 2021 /this issues for an extended discussion of open science and research syntheses).

The third benefit is to the original data creator themselves. Considerable effort goes into collecting high-quality education data. By releasing their data, researchers efficiently expand the potential application and impact of that hard-collected data. Open data and code are also typically assigned their own digital object identifier (DOI) and are therefore citable and reportable as scholarly products. Research suggests that shared data are associated with increased citations for publications ( Piwowar & Vision, 2013 ), a commonly used metric for evaluating researchers. Finally, given the movement towards funding agencies requiring data being made open (e.g., Institute for Education Sciences [IES], 2020 ; National Institutes of Health, 2020 ), open data and code are becoming required for many funded investigators.

Limitations and Challenges

For most education researchers, achieving open data and code is not done by simply dragging and dropping a dataset from their computer onto a data repository. Depending on one’s data-cleaning pipeline, there may be multiple steps to get a fully clean and deidentified dataset ready. In addition, detailed data documentation will need to be produced. Finally, to achieve FAIR (Findable, Accessible, Interoperable, and Reusable; Wilkinson et al., 2016 ) data standards, most data repositories require multiple steps to upload and release one’s data and code (e.g., provide metadata, assign release restrictions, assign copyright). These steps take time, resources, and expertise (see van Dijk et al., 2021 , for suggestions on where to start; see Gilmore et al., 2018 for guidance on sharing audio and video data).

An additional challenge for education researchers is navigating the ethical regulations in place to protect research participants, including what can happen with their data. The New Common Rule has actually loosened many of these regulations (see Electronic Code of Federal Regulations, 2018 ), but not all institutional review boards (IRB) are following the new guidelines. As such, depending on their IRB’s practices, it can be difficult for some investigators to fully share their data and code. We recommend that, rather than assuming what their IRB requires, researchers talk with their IRB about open data. IRBs should be aware of the changing norms and funding body requirements concerning open data. For investigators with old datasets with restrictive informed consents, IRBs have the power to override old consent forms if the risk to participants would not change (and posting deidentified data should not change risk) by issuing a waiver of informed consent.

Researchers may be hesitant about sharing for other reasons, such as increased scrutiny and the possibility of inflating Type-1 error. Although it is possible that errors in one’s data or code could be discovered through sharing, identification and subsequent correction of errors can be considered a valuable opportunity to improve the validity and credibility of education research. Interesting discussions have also emerged about whether analyzing the same dataset repeatedly, as occurs with open data, will inflate Type-1 error (see Logan et al., in press ; Maxwell et al., 2017 ). If inflation of Type-1 error is a concern, researchers can share metadata only or provide only limited access to others upon request (e.g., storing the data privately on a data repository with access control options). These options provide greater benefit to the field, and are more secure, than simply storing data on one’s computer.

Thorny Issues

Guidelines and community support regarding data sharing have been slow to develop, leaving education researchers to grapple with some thorny issues regarding open data and code. The lack of established norms and incentives for data sharing creates real trade-offs for early-career scholars like Dr. Thompson—should she invest time in still evolving practices or stay focused on more traditional publishing (without shared data and code)? Here we focus on two thorny issues, related to (a) issues in sharing qualitative data ( Feldman & Shaw, 2019 ) and (b) data deidentification. Like quantitative researchers, qualitative researchers are experiencing increased expectations for data sharing from funders and journals ( Mannheimer et al., 2019 ). However, there are unique concerns related to sharing qualitative data. Some concerns are epistemological; for instance, that shared data removed from the original context of the initial data collection (e.g., sharing data absent the context of the researcher who collected it, removing the personal details from the data) are incomplete or illegitimate ( Walters, 2009 ). Other concerns exist related to ethics. For example, some qualitative researchers argue that data are generated by (not collected from) study participants and ownership is shared with the participants themselves, meaning the researcher alone cannot give permission for sharing ( Broom et al., 2009 ; Moore, 2007 ). Additional concerns exist related to difficulties fully deidentifying qualitative data ( Mannheimer et al., 2019 ).

Despite these concerns, there is a growing belief that qualitative data can be shared and a growing number of resources available. Some have pointed out that even primary investigators cannot know the full context of the data they generate, and so holding secondary data users to this standard is not appropriate ( Fielding, 2004 ). Indeed, there is a rich tradition of data sharing in some qualitative fields, such as history, where data archiving is well established ( Fielding, 2004 ). Qualitative educational researchers could turn to these fields for examples of how their epistemologies might support data sharing ( Broom et al., 2009 ). Decontextualization of anonymized secondary data sets will likely be a concern for some qualitative researchers, who will, ultimately, need to determine whether the benefits of data sharing outweigh their epistemological concerns ( Mannheimer et al., 2019 ). Related to ethics concerns, scholars have begun to develop guidelines for how libraries and data repositories can help support qualitative researchers at the start of their research process ( Mannheimer et al., 2019 ; see https://qdr.syr.edu/guidance/managing for additional resources).

Another thorny issue related to open data is whether and how researchers can adequately deidentify data, which can often be more difficult than it may appear at first for both quantitative and qualitative data. Most researchers realize that names, addresses, and the like are identifiable and should be removed from a dataset. However, extreme values, rare characteristics (e.g., certain disabilities), or certain combination of variables (e.g., being male and a kindergarten teacher) can also make a person identifiable in a dataset. It is difficult to provide a general rule of thumb to ensure data deidentification, as it is project dependent (e.g., in some geographical regions, a participant characteristic might be identifiable, but in other regions it would not). Also, techniques to deidentify data are still being developed and tested. However, there are resources that are available to help (see Edwards & Schatschneider, 2020a , 2020b , for less technical resources, and US Department of Health and Human Services, 2012 , for a more technical resource).

Data deidentification is paramount when sharing sensitive data. We encourage researchers with highly sensitive data that cannot be fully deidentified to consider (a) sharing nonsensitive data from the data set and providing restricted access to more sensitive data (see Meyer, 2018 ), and (b) posting summary data (e.g., means, standard deviations, sample sizes and covariance matrices, broken into subgroups if possible) and project meta-data to a data repository. The latter enables other researchers to know about the data, use the summary data for meta-analyses and other statistical methods, and contact the original researchers about possible use of some or all of the data set.

Open Materials

During the development of her dissertation proposal, Dr. Thompson’s committee members had encouraged her to adapt an interview protocol that another researcher had shared as supplemental material to a published article. The protocol had been developed for use with special education teachers, but Dr. Thompson was able to modify the questions to apply to secondary students. After her dissertation research had been accepted for publication, Dr. Thompson wanted to share the survey she developed for the quantitative portion of her dissertation, which she hoped would also increase the impact of the measure and save time for others conducting similar research. As the survey was her original work, she was the copyright holder and was able to apply the copyright license of her choosing. Dr. Thompson wants to continue to use and share open materials as she develops her own line of research, but she is wary about potential copyright issues associated with open materials. Specifically, in her next study, she is planning to modify an intervention protocol developed by other researchers, but the copyright license of the protocol has been difficult to find. She is not sure whether or how she can share the adapted version of the intervention.

What Are Open Materials?

Open materials make researcher-created or -adapted study materials freely and publicly available to other researchers and research consumers. This is accomplished through sharing the materials on a data repository, such as OSF or ICPSR, or by including them in a journal’s online supplemental repository published in conjunction with a journal article. Although policies regarding the openness of online supplemental materials may vary by publisher and journal, many publishers currently make supplemental materials openly available, even if the user does not have access to the article (see SAGE, 2021 ; Springer, 2020 ). Potentially shareable materials include, but are not limited to, intervention materials and implementation procedures, researcher-created dependent measures, fidelity and treatment integrity checklists, survey instruments, data collection forms, training procedures and manuals, interview or observational protocols, positionality or reflexivity statements, data integration methods, and deductive or inductive codebooks.

Open materials involve shifting some or all of the rights of the creator and copyright holder to others. Once created and shared in a fixed and tangible form, one’s work becomes automatically copyrighted with the creator holding copyright ownership, even without registration with the copyright office ( US Copyright Office, n.d. ). In holding copyright ownership, one has the right to reproduce, alter, and distribute their own work exclusively ( US Copyright Office, 2019 ). To engage in open materials, one must transfer some or all of their copyright permissions to others through the addition of a copyright license. A license grants the public some permissions, but—depending on the license—can retain some exclusive rights for the copyright owner. Creative Commons (CC) is a frequently used license provider that works in tandem with the provisions of copyright and allows for different levels of attribution, editing, and reuse by others ( Creative Commons, n.d.a ). See Figure 1 for the different CC licenses available, and the rights afforded to others by each license. Once chosen, the license is clearly and prominently appended to the materials in a machine-readable format. When others use the licensed materials, they must attribute and cite the original authors, reuse, edit, and redistribute according to the license restrictions ( Creative Commons, n.d.b ).

Note . Figure is licensed by Sarah Emily Wilson, 2020, under a Creative Commons Attribution-Noncommercial 4.0 International License (CC BY-NC). Publicly available at https://osf.io/n35zy/ .

In the same vein as open data, materials can be made open and accessible by researchers across paradigms and methodologies, benefitting many stakeholders, including researchers who use shared materials; researchers who share their materials; and educational policy makers and practitioners, as well as students. In sharing study materials, authors enable others to adapt and reuse materials in their own research ( Miguel et al., 2014 ; Molloy, 2011 ) for a wide range of purposes, rather than have to create their own original materials. Sharing materials may particularly benefit early-career researchers, like Dr. Thompson, who have limited access to resources and funding, as well as limited experience developing rigorous materials. Additionally, making materials accessible allows researcher consumers to assess a study’s methods more thoroughly and transparently, thus increasing the study’s credibility — a critical aim for qualitative and quantitative researchers alike. Similarly, researchers using the same shared measure across studies can reduce a source of variance in research syntheses. Thus, using shared materials can enhance the equity, efficiency, and credibility of research. Openly sharing materials also benefits the researcher who shares. Once a researcher has published their materials openly through a data repository or as a supplement to a journal article, the materials are assigned a DOI, and can be listed on the author’s vitae. The open materials can then be cited by others.

Finally, increased accessibility of materials benefits the students and practitioners who are the end users of education research. By sharing research materials freely, practitioners can access high-quality and validated materials more easily, thereby facilitating improved student outcomes. Indeed, the inaccessibility of many research-validated programs may contribute to the research-to-practice gap seen in education. Making materials open and accessible to others facilitates practitioners’ ability to apply education research with fidelity in schools. This benefit also increases the equity and impact of education research.

Although open materials have clear benefits and can be straightforward, there are several limitations and challenges to consider. The first challenge is navigating copyright issues for sharing original materials and adapted materials (the latter is discussed in more detail in the subsequent section). Researchers should not share materials that they do not own, such as norm-referenced assessments, but they can share researcher-created materials as the copyright holder. Before sharing their original materials, we encourage authors to consult with their funding agencies, employers, and copyright lawyers that many universities employ through their library system to check for possible copyright or intellectual property restrictions that would prevent authors from legally sharing their own materials. Most original, non-commercially distributed work that a researcher created for the purpose of a study will not be restricted.

Once it is determined that the materials can be shared, the next challenge is formatting. Researchers should create, format, and share materials in ways that facilitate others accessing, editing, and using them. We recommend shared materials be formatted and shared in an editable file format that provides users with source-file access. In other words, materials should be saved and shared in a format (e.g., .txt, .html) that does not require specific software to access, thereby allowing users to open and interact with materials in the original, editable file. Similarly, if possible, materials should be shared in formats that allow for editing using common or freely accessible software that does not require a high level of expertise. Finally, we suggest that researchers include instructions, as needed, with shared material to allow others to meaningfully reuse the materials. For example, researchers might include an implementation guide alongside a shared intervention protocol, or administration and scoring guidelines alongside a shared assessment. Education researchers should carefully review shared materials before uploading, as they are not typically reviewed or vetted before being posted.

The next challenge is selecting the appropriate copyright license for one’s work and digitally indicating the license on the shared materials. Although creators’ work is automatically copyrighted when published online, clearly indicating the license of the work is helpful and supports open and transparent communication between the authors and those who might use their shared materials. Care should be taken to match the copyright license with the author’s intended permission level of use by others because, once published, licensing options cannot be changed. CC provides material creators with an easy-to-use, step-by-step tool for matching intended use with the appropriate license ( Creative Commons, n.d.a ). Once selected, the license should then be indicated clearly and prominently in a machine-readable format on the shared electronic materials (an example can be seen in the licensing of Figure 1 ). It is also important to note that copyright restrictions and permissions are not internationally held ( US Copyright Office, 2019 ). In most instances, copyright is determined by the publication’s country of origin and is bound by that nation’s copyright laws. Although international copyright treaties have simplified how users in other countries can maintain a creator’s copyright license, this is not always the case. When sharing or using materials created outside of one’s own country, we recommend consulting the International Copyright Relations of the United States circular (2020) . To address these potential challenges proactively, van Dijk et al. (2021) recommended that plans for sharing be instigated at the start of a project, rather than after the fact.

Open materials have received considerably less attention than open data and open access, with relatively little guidance available for researchers who encounter thorny issues. In this section, we discuss two such issues: (a) understanding restrictions for using and adapting materials that someone else has licensed for sharing, and (b) making aspects of researcher-adapted materials open when one does not own the copyright and the license for the original materials does not allow for sharing and redistribution.

To address both issues, the first step is to determine the copyright status and type of license for material one wishes to share. This is critical to determine what rights the creator holds exclusively, and what rights have been given to the public. Although creators typically hold the copyright for their own materials, it is not always straightforward to determine the copyright and licensure status of others’ work. If not clearly displayed, we recommend contacting the original author and publisher regarding copyright licensing and permissions for reuse, as the copyright relationship between authors and publishers can vary.

Once copyright license status is determined, the first thorny issue arises when authors want to use materials previously shared by another author as allowed by the license of the material. In preparing to use shared material, it is important to understand the rights still held by the copyright holder and the specific permissions the license grants. If the original material is within the public domain or has a CC0 license, the materials can be reused and shared without attribution. If the material has a CC By Attribution (BY) license, it can be adapted and shared as long as the author is properly attributed and cited. When assessing the restrictions that a CC license communicates, there are three additional terms to attend to that can be combined in various ways. First, licenses containing No Derivatives (ND) terms cannot be modified, but can be copied and distributed with attribution to the original author. Licenses with Non-Commercial (NC) terms limit distribution to non-commercial products. Finally, licenses with a Share-Alike (SA) term must be licensed and shared under the same license restrictions as the original work. See Figure 1 for a description of possible CC licenses.

The second thorny issue arises when a researcher wants to share their adaptations to materials that do not have a license permitting sharing or redistribution and the copyright of the materials is fully retained by the original creator or a publisher. In these cases, we suggest that the researcher share a written, detailed description of the adaptation without sharing the original material, as this would infringe on the rights of the copyright holder. For example, a researcher might have investigated the efficacy of a packaged reading fluency intervention that they adapted, with permission, for a specialized population of learners. The packaged intervention is not licensed for sharing. When sharing their adaptation to the intervention, the researchers should properly cite and describe generally the original work, describe in specific detail the changes made to the intervention, and comply with the copyright restrictions by not sharing the original intervention protocol. Although this does not allow for the same level of openness as sharing researcher-created materials or researcher-adapted materials that allow for sharing, we posit that this degree of openness realizes many of the benefits of shared materials (e.g., improved equity, impact, and efficiency).

Open Access

Dr. Thompson posted the study from her dissertation research as a preprint before she submitted it for publication. She was surprised at how easy it was to do—she just uploaded a .pdf version of her manuscript to a repository, provided some basic information about the manuscript, and it was assigned a DOI and became searchable and freely available the next day. She was pleased that the preprint had been downloaded many times, likely by many educators who would not otherwise have access to the manuscript. Additionally, she had received comments on the preprint from other researchers that helped her improve the paper before submitting it for publication. She recommended open-access prints to the administrators and teachers she worked with, because they could not typically access published journal articles without paying for them. One of the teachers asked her whether she could trust the research in the prints. Dr. Thompson hadn’t fully considered the consequences of prints not being peer reviewed: some of the prints could, indeed, report flawed and misleading research. Additionally, Dr. Thompson had decided not to post a preprint for the study she had just finished writing up. She thought that colleagues who worked in her specific sub-field might see the preprint, and she hoped that they would be able to serve as blind peer reviewers when she submitted the paper for publication. She continued to see the advantages of open access but wondered if the problems of lack of peer review and the possibility of identifying authors before peer review for a journal might outweigh their benefits.

What is Open Access?

Although evidence-based reforms in education are premised on the notion of educators using research evidence to inform decisions related to policy and practice, most published research lies behind publisher paywalls ( Piwowar et al., 2018 ), inaccessible to many education stakeholders. Access to scholarship behind publisher paywalls is often costly, and multiple institutions of higher education have taken steps to cut ties with major publishers to reduce cost and increase accessibility and equity ( Taylor, 2020 ). The many forms of open access (OA; gold, hybrid, bronze, and green OA) help to democratize research evidence by removing paywalls and making scholarship openly accessible to anyone with internet access.

Gold OA journals are entirely open and accessible online. To cover publishing costs, most gold-OA journals charge an article-processing charge (APC) to authors, typically about $3,000 ( Fleming & Cook, 2021 ). There are some gold-OA journals in education, such as AERA Open , Education Policy Analysis Archives , and Journal of Educational Technology & Society , but closed journals remain the norm in the field. Hybrid OA involves authors having the option to pay an APC to make their specific article OA in an otherwise closed journal. Most education journals published by large publishers provide hybrid-OA options. With bronze OA, the most common type of OA ( Piwowar et al., 2018 ), publishers make specific articles of an otherwise closed journal open, at least temporarily, but without an OA license. For example, a journal may make the lead article of a special issue bronze OA to increase interest in the issue. Because bronze-OA articles are not licensed as OA, readers are restricted from sharing the content and articles can become closed at any time.

Green OA, which includes preprints and postprints, involves researchers posting author-formatted versions of manuscripts in open, online repositories. Preprints generally refer to manuscripts posted prior to undergoing peer review; whereas postprints are posted after peer review ( Tennant et al., 2018 ). Green OA can also include working papers posted on an institutional or personal website. For example, EdPolicyWorks (n.d.) at the University of Virginia has a series of working papers on their website. Key differences between working papers and prints in the field of education include permanence and licensing. Generally, prints are permanent publications with assigned DOIs and are licensed to be OA, whereas working papers typically do not have a DOI, may be removed or moved, and are not licensed as OA.

Journal and publisher policies on green OA vary, and researchers should be familiar with the policies of targeted journals before posting their work publicly or on print repositories ( Fleming & Cook, 2021 ). Print repositories where education research can be posted include the Social Science Research Network ( www.ssrn.com ), EdArXiv ( www.edarxiv.org ), and Advance ( www.advance.sagepub.com ). Journal and publisher green-OA policies can be found on the SHERPA/RoMEO website ( http://sherpa.ac.uk/romeo/index.php ), on journal websites, and by contacting journal editors.

Most fundamentally, OA publishing democratizes access to scholarship to anyone with internet access, regardless of resources, career status, institution, or country of residence ( Bahlai et al., 2019 ; Syed & Kathawalla, 2020 ). Accessibility is a critical component of democratization and equity, and OA publishing removes barriers of prestige and power associated with journal subscriptions. In addition to providing practitioners and families access to scholarship, OA also ensures that a study will not be excluded from meta-analyses and research syntheses because other researchers could not access it (see Patall, 2021 /this issue). Additional benefits of OA publishing include increased efficiency, impact, and credibility of research and scholarship. Although important, peer review delays dissemination of research and scholarship. Depending on the number of rounds of review (potentially at multiple journals) before a manuscript is accepted, peer review can delay the availability of scholarship by many months or even years. Green OA allows education researchers to immediately disseminate their scholarship. This may be especially important for scholarship on time-sensitive matters (e.g., providing effective education during a pandemic).

Likely because of enhanced accessibility, there is a well-documented citation advantage for studies first posted as prints ( Piwowar et al., 2018 ). Additionally, journal articles with corresponding prints have been shown to have more downloads and greater social media presence ( Abdill & Blekhman, 2019 ; Fu & Hughey, 2019 ). Gold, hybrid, and bronze OA also provide for broad dissemination and can increase impact. For example, Gershenson and colleagues (2020) found that when a paywall was temporarily removed for six high-impact education journals (bronze OA), downloads increased 60 to 80% over a two-month period.

Lastly, prints have the potential to increase credibility of the education research base by helping to curb publication bias. There is a well-documented “file-drawer” problem (i.e., publication bias) in which studies with null or negative results are published less frequently than studies with positive findings ( Franco et al., 2014 ; Polanin et al., 2016 ). For applied fields, such as education, publication bias can have important consequences for policy and practice that is informed by syntheses of the published research base that disproportionately excludes studies with null findings. Print repositories provide a dissemination outlet for all research, including studies that may be relatively unlikely to be published, such as those with null findings.

Limitations and challenges associated with OA publishing include expense, limited awareness and use, and concerns about being scooped. The expense of APCs is a major limitation of gold and hybrid OA. We recommend posting prints to online repositories as a cost-free OA alternative. Another challenge is limited awareness and use among education researchers. Prints and OA publishing are relatively new to education research, whereas in fields such as physics, mathematics, and computer science posting prints to online repositories became common decades ago ( Tennant et al., 2018 ). In fields where prints are better established, print repositories report over 2,000 uploads each month ( Abdill & Blekhman, 2019 ). EdArXiv has 673 prints (as of 2/20/2021), which suggests that prints may be underutilized in education. Additional supports and incentives may increase the volume of print submissions in education. For example, guidance on how preprints fit into the publishing timeline and steps for posting prints ( Fleming, 2020 ; Fleming & Cook, 2021 ; Kathawalla et al., 2020 ; Roehrig et al., 2018 ; Syed, 2020 ) can help increase awareness and promote greater use. Finally, researchers may also be concerned about their preprinted work being “scooped” (i.e., another researcher taking their idea and publishing it first). However, because preprints are timestamped, they actually serve to establish precedence and should be cited by others, just as a published article would.

In this section, we discuss two thorny issues related to OA: (a) preprints are made publicly available without being peer reviewed, and (b) posting preprints may compromise subsequent blind peer review. Peer review is a well-established and strongly supported practice for vetting the rigor of published research ( Tennant, 2018 ). Thus, absence of peer review in preprints raises important concerns about the dissemination of potentially flawed and misleading research. It is important to recognize that peer review is “prone to bias and abuse in numerous dimensions, frequently unreliable, and can fail to detect even fraudulent research” ( Tennant et al., 2017 , p. 2). Indeed, many hundreds of published, peer-reviewed papers are retracted each year ( Brainard & You, 2018 ). Thus, we urge research consumers to take a skeptical, caveat emptor approach when consuming research, whether or not it is peer reviewed. Moreover, we note that Klein et al. (2019) found few differences between preprints and corresponding journal articles. As such, if one can locate the preprint version of a published article, it is likely to contain highly similar content and be of similar quality. Moreover, many journals and publishers allow researchers to post manuscripts as postprints after acceptance. In this way, researchers can update preprints by posting the author-formatted version of the peer-reviewed manuscript. However, when not updated with a peer-reviewed postprint, the absence of peer review in preprints is an important concern, perhaps especially so because preprints can be freely accessed by practitioners, parents, and others who typically do not have advanced training in research to critically evaluate research on their own. Many print repositories enable comments to be made on posted prints, so that peers can engage in ongoing peer review. That is, other researchers can point out strengths and weaknesses of study design and methods (i.e., review), to which the authors can respond (e.g., agree with, clarify, counter). Non-researchers can also leave questions they have about interpreting study findings, to which authors and other researchers can respond.

A second thorny issue is that preprints potentially jeopardize the pool of blind peer reviewers if and when authors submit a manuscript that has been posted as a preprint. In our experience most education journals use double-blind peer review, in which the identities of both the reviewers and authors are unknown to one another. If authors post a preprint before submitting a manuscript for publication, potential reviewers see the preprint, discover the identity of the authors, and therefore not be able to serve as a blind reviewer. This may be especially problematic for researchers working in small subfields in which there are relatively few expert reviewers available. It is important to recognize that the issue of reviewers identifying authors is not unique to preprints. It is not uncommon for reviewers to discern the identities of the authors of supposedly blinded manuscripts (e.g., Baggs et al., 2008 ; Justice et al., 1998 ). For example, in our roles as journal editors, two of the authors have experienced multiple instances of invited reviewers declining to review blinded manuscripts because they identified the author (e.g., because of the topic or works cited). Indeed, we suspect that, given the information available on the internet, a motivated reviewer could likely identify the authors of many if not most manuscripts they are asked to review. As such, we encourage editors to ask reviewers to confirm they do not know the identity of the authors and will/did not actively attempt to identify the authors when accepting and submitting their reviews. If journals commit to using blind peer review, editors should verify whether the review was, indeed, blind, regardless of whether the manuscript had been posted as a preprint. We suspect that if reviewers respect an editor’s request not to research authors’ identities, the availability of a preprint should not significantly affect the availability of blind peer reviewers for most manuscripts.

Nonetheless, if authors wish to post a preprint without unmasking their identity, they can post preprints under a pseudonym, indicating in an author’s note that the preprint will be updated with a postprint with the author’s real name after peer review is complete. This would involve creating a pseudonymous account with the print repository. Alternatively, for this special issue, one of the guest editors posted preprints of manuscripts planned for the special issue under their name and requested in an author’s note that the papers not be cited until after postprints with the authors’ names are posted. These approaches allow for posting preprints while retaining anonymity of authors, though that work cannot be cited until after peer review is complete and a postprint is posted.

Conclusion: A Final Thorny Issue

Although there are challenges and thorny issues that complicate implementation of open science, Dr. Thompson is well positioned to implement the core practices described in this manuscript. Open-science practices were taught and valued at her doctoral granting institution and she is motivated to enact open-science reforms. Despite her motivation, it is unclear whether Dr. Thompson will be rewarded institutionally for her effort. Her university provides no funds for her to publish in gold-OA journals and senior faculty in her department do not engage in or value sharing data, sharing material, or OA publishing. Moreover, neither the annual merit review or the promotion and tenure guidelines used at her university mention open-science practices. As is the case in many institutions of higher education, the fate of Dr. Thompson’s promotion and tenure case will primarily rest on her publishing first- and sole-authored articles that report studies in high-impact journals. Given faculty members’ limited time and resources, the disregard of open practices in review, promotion, and tenure policies may encourage Dr. Thompson to devote her scarce time and resources to other, more traditional forms of scholarship that are rewarded explicitly. Thus, although Dr. Thompson remains motivated to implement open-science practices, she does so outside of the incentive structure of her university.

Unfortunately, Dr. Thompson’s experience is similar to many other early-career education researchers. Indeed, Alperin et al.’s (2019) review of faculty review, promotion, and tenure policies from 129 universities in the US and Canada, found that almost all policies mentioned traditional outputs such as journal articles and grants, whereas only 5% of institutions mentioned open access—and most of the mentions cautioned against publishing in OA venues! Thus, consideration of how to support education researchers, especially early-career researchers, to devote scarce time and resources to engage in open practices without harming their career advancement can be considered a final thorny issue. Although we are aware of many education researchers who, similar to Dr. Thompson, are innovators and early adopters of open science, we suspect that broad diffusion and long-term adoption of open-science reforms will require multiple levels of supports and incentives (see Mellor, 2021 /this issue; Rogers, 2003 ).

Mellor (2021 /this issue) proposed a 5-level pyramid for achieving research culture change that can serve as a framework for supporting broader adoption of open science in education research. The model posits that infrastructure, such as data repositories and print archives, is needed as a base to make open science possible. Then, at progressively higher levels of the pyramid, user experience is needed to make engaging in open science easy, communities to make it normative, incentives to make it rewarding, and—finally—policies to make it required. Education researchers can readily find infrastructure with suitable user-experience interfaces to post prints (e.g., EdArXiv), make their data and code publicly available (e.g., ICPSR, LDbase), and share their study materials (e.g., OSF). Some progress is occurring at the higher levels of the pyramid as well. For example, more education researchers are gaining experience in implementing different open-science practices ( Makel et al., in press ), communities of education researchers focused on open science are being formed (e.g., STEM Education Hub; Center for Open Science, n.d. ), and funding agencies are encouraging and in some cases requiring open practices in funded research ( IES, 2020 ). However, much work remains to support open practices being broadly adopted and sustained across the diverse research cultures in education. In particular, we see few institutional incentives for engaging in open science, which will be critical. Thus, we recommend that colleges of education and other research organizations explore ways to recognize the value of open access in their review, promotion, and tenure guidelines (see Moher et al., 2018 ).

Strategies and support for open science will also need to be multifaceted to address the unique needs of different members of the education research community. For instance, as open practices have primarily been designed for quantitative research, additional attention to all levels of the pyramid is needed to encourage qualitative and mixed-method researchers to make their research open. Given the broad application of multiple research methodologies in the field, education researchers are well positioned to contribute to expanding open science. Incentives must also be in place for researchers across the career continuum, from doctoral students to full professors, to address the varied demands and contingencies at each career stage (see Allen & Mehler, 2019 ). For example, as full professors have been found to be less favorable toward preprints than their early career counterparts ( Soderberg et al., 2020 ), researchers should identify the concerns of this influential group of scholars and develop strategies for addressing them. Finally, multiple stakeholders in education research (e.g., institutions of higher education, journals, professional organizations, funding agencies) should develop strategies supporting open practices to provide a multipronged, comprehensive approach ( Adelson et al., 2019 ).

In this paper, we explored three open-science practices targeting open access of the tools and products of research—open data and code, open materials, and OA—and how they can increase equity, credibility, impact, and efficiency in education research. Despite potential benefits, each practice has challenges and limitations associated with it, and education researchers are likely to face thorny issues that are not easily resolved as they apply the practices. To realize the benefits of and surmount the obstacles related to open-science practices, stakeholders will need to develop and implement strategies to shift the culture of education research from a field that not only makes open science possible to one that normalizes, rewards, and—eventually—requires open science.

Acknowledgments

This work is supported by Eunice Kennedy Shrive r National Institute of Child Health & Human Development Grants R01HD095193 and P50HD052120, and a grant from the Institute of Education Sciences (R324U190001). Views expressed herein are those of the authors and have neither been reviewed nor approved by the granting agencies.

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Vanderbilt University may submit one Letter of Inquiry as lead institution to the 2024 Creating Equitable Pathways to STEM Graduate Education program from the Alfred P. Sloan Foundation.

The Higher Education Program at the Alfred P. Sloan Foundation is continuing its investment in Minority Serving Institutions (MSIs) and in the establishment of partnerships between MSIs and graduate programs at other colleges and universities. Sloan’s Creating Equitable Pathways to STEM Graduate Education grants will engage the expertise of MSIs—and the unique experiences of their faculty and students—to model effective systems and practices that remove barriers and create opportunities for equitable learning environments in STEM graduate education so all students can thrive. Grant awards will support sharing MSIs’ institutional know-how on equitable undergraduate and graduate education, as well as modeling that know-how to create systemic changes that enhance pathways from MSIs to master’s and doctoral degree programs in astronomy, biology, chemistry, computer science, data science, Earth sciences, economics, engineering, marine science, mathematics, physics and statistics at partner institutions.

Awards will also open pathways to students and faculty at partnering institutions to learn at and from MSIs and collaborate on systemic changes that have the potential for even broader impacts. While Equitable Pathways grants will directly fund activities that eliminate barriers to pathways between MSIs and their partnering institutions, Sloan is particularly interested in supporting partners that are committed to widening pathways to master’s and doctoral degree programs in the covered fields for students from MSIs and from other colleges and universities that currently have weak, if any, pathways to STEM graduate education.

The foundation thus invites letters of inquiry (LOIs) for both new and the continuation of previously Sloan-funded projects that seek to dismantle systemic barriers and create sustainable MSI pathways to graduate education in the stated disciplines. Compelling LOIs will result in the invitation of a full proposal. 

Proposed projects may take multiple forms, including, for example, planning activities on MSI campuses that set the stage for new pathways between MSIs and graduate programs at partner institutions, which may be other MSIs or institutions with graduate programs in the covered STEM fields. Another example could be projects that establish or scale existing, mutually beneficial partnerships between undergraduate and graduate programs at two or more institutions.

In addition to establishing seamless pathways, projects need to address policies, processes, and practices that reinforce existing systems that are barriers to student access and success in graduate education. These projects could include efforts to examine or redesign graduate recruitment, admission policies and processes, mentoring practices, departmental climate, or other gatekeeping (or gateway) structures to and through STEM graduate education. Since the barriers to equitable pathways do not end once students are admitted to graduate programs, the Foundation is looking for evidence that projects will promote and enhance existing efforts to reduce and eliminate policies, procedures, and institutional climates and cultures that prevent students from successfully attaining a graduate degree.

All projects must have at least one MSI partner . When two or more institutions are the proposed grantees, it is preferred that the primary PI be housed at the MSI to create a direct connection between MSI expertise and project leadership.

Three types of grants will be funded:

• Planning grants to support two or more institutions to conduct internal reviews of existing barriers to student success and for analysis and planning for future partnership(s) (up to $75,000 for up to 1 year);
• Seed grants to two or more institutions that seek to formalize an existing partnership(s) and launch one or more pilot initiatives (up to $250,000 over 1-2 years); and
• Implementation grants to two or more institutions that allow for the augmentation or scaling of existing partnerships/collaborations (up to $500,000 over 2-3 years).

Eligibility

Lead investigators from submitting and partner institutions should be at the full, associate, or assistant professor level, a department chair, or in an administrative role with high connectivity to academic positions. Such individuals should come from nonprofit two- or four-year institutions, or organizations that serve higher education professionals or institutions.

The selected nominee will submit the LOI to the sponsor by July 1, 2024 . If invited by the Sloan Foundation to submit a full proposal, the due date will be October 4, 2024.

See the full program page for more information.

Internal Application Instructions

Interested faculty should visit https://vanderbilt.infoready4.com/#competitionDetail/1935697 to submit an application for the internal LSO competition and to find additional information about the opportunity.  The deadline for the internal competition is April 18, 2024 .

Any questions about this opportunity or the LSO process may be directed to [email protected] .

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Contents of Volume 377, Number 4 HTML articles powered by AMS MathViewer View front and back matter from the print issue

Talking politics with strangers isn't as awful as you'd expect, research suggests

Many of us avoid discussing politics with someone who holds an opposing viewpoint, assuming the exchange will turn nasty or awkward. But having those conversations is far more gratifying than we expect, a new research paper suggests.

Across a series of experiments involving hundreds of U.S. adults, a team of scientists found that individuals underestimate the social connection they can make with a stranger who disagrees with them. The findings are published in Psychological Science, a journal of the Association for Psychological Science.

These low expectations may help to explain why people think those on the opposite side of the political spectrum have more extreme views than they actually do, behavioral scientists Kristina A. Wald (University of Pennsylvania), Michael Kardas (Oklahoma State University), and Nicholas Epley (University of Chicago) wrote in an article about their research.

"Mistakenly fearing a negative interaction may create misplaced partisan divides," they wrote, "not only keeping people from connecting with each other but also keeping people from learning about each other and from each other."

The experimenters found evidence, through experiments conducted online and in person, that people prefer to avoid hot-button issues, especially with people who disagree with them. People also tend to advise their friends and relatives to avoid such conversations.

But Wald, Kardas, and Epley believed people would find discussing their political differences to be a more positive experience than expected, at least partly because people fail to appreciate the extent to which conversations are informative and draw people closer together.

To test their theory, they asked nearly 200 participants in one experiment for their opinions on divisive political and religious topics, such as abortion and climate change. The researchers then divided the participants into pairs and assigned them to discuss one of these topics. Some participants were told in advance whether their partners agreed with them or not, but others entered the discussions unaware of their partners' views.

All the participants reported how positively or negatively they expected the conversation to be, then engaged in the discussion while being video recorded. Afterward, the participants rated their sentiments about the dialogue. Research assistants also viewed the videos of the conversations and evaluated them across several dimensions.

As predicted, the participants underestimated how positive their conversation experience would be, but this tendency was largest when they disagreed with their partner. Participants in this disagreement condition also underestimated the similarities in their opinions. Coders who watched the videos of these conversations confirmed that participants tended to stay on topic, and that the conversations were consistently positive whether the participants agreed or disagreed.

In another experiment, the researchers tested their hypothesis that people underestimate how the process of conversation itself -- actual back-and-forth dialogue -- connects people. To do so, they randomly assigned participants to discuss a divisive topic they agreed or disagreed on, but they also randomly assigned participants to either have a conversation about the topic in a dialogue format or to simply learn of their partners' beliefs on the topic in a monologue format. In the monologue format, each person separately recorded themselves talking about their opinion and then watched the other person's recording.

Overall, the participants underestimated how positive their interactions would be, especially when they disagreed with their partner, the researchers noted. But this tendency was especially strong when people actually had a conversation with their partner rather than simply learning of their beliefs in a monologue. The social forces in conversation that draw people together through back-and-forth dialogue are not only powerful, but they appear to be even more powerful than people expect.

The researchers cautioned that their experiments involved participants talking with strangers; the experiments did not reveal how disagreements unfold among family and friends. Still, they said their findings illustrate the benefits of talking and listening to others rather than typing and broadcasting in debates on social media.

Our reluctance to discuss our differences denies us some positive social interactions, the authors concluded.

"Misunderstanding the outcomes of a conversation," they wrote, "could lead people to avoid discussing disagreements more often, creating a misplaced barrier to learning, social connection, free inquiry, and free expression."

• Relationships
• Racial Issues
• Social Psychology
• STEM Education
• Political Science
• Surveillance
• Poverty and Learning
• Social movement
• Intellectual giftedness
• Social science
• Social psychology
• Double blind
• Social cognition
• Social inclusion

Story Source:

Materials provided by Association for Psychological Science . Note: Content may be edited for style and length.

Journal Reference :

• Kristina A. Wald, Michael Kardas, Nicholas Epley. Misplaced Divides? Discussing Political Disagreement With Strangers Can Be Unexpectedly Positive . Psychological Science , 2024; DOI: 10.1177/09567976241230005

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