Have Gender Gaps in Math Closed? Achievement, Teacher Perceptions, and Learning Behaviors Across Two ECLS-K Cohorts

Studies using data from the Early Childhood Longitudinal Study–Kindergarten Class of 1998–1999 (ECLS-K:1999) revealed gender gaps in mathematics achievement and teacher perceptions. However, recent evidence suggests that gender gaps have closed on state tests, raising the question of whether such gaps are absent in the ECLS-K:2011 cohort. Extending earlier analyses, this study compares the two ECLS-K cohorts, exploring gaps throughout the achievement distribution and examining whether learning behaviors might differentially explain gaps more at the bottom than the top of the distribution. Overall, this study reveals remarkable consistency across both ECLS-K cohorts, with the gender gap developing early among high achievers and spreading quickly throughout the distribution. Teachers consistently rate girls’ mathematical proficiency lower than that of boys with similar achievement and learning behaviors. Gender differences in learning approaches appear to be fairly consistent across the achievement distribution, but girls’ more studious approaches appear to have more payoff at the bottom of the distribution than at the top. Questions remain regarding why boys outperform girls at the top of the distribution, and several hypotheses are discussed. Overall, the persistent ECLS-K patterns make clear that girls’ early mathematics learning experiences merit further attention

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

Teachers’ race and gender biases and the moderating effects of their beliefs and dispositions

Abstract Background Women and people of color continue to be underrepresented in many STEM fields and careers. Many studies have linked societal biases against the mathematical abilities of women and people of color to this underrepresentation, as well as to earlier measures of mathematical confidence and performance. Recent studies have shown that teachers may unintentionally have biases that reflect those in broader society. Yet, many studies on teachers’ reports of students’ abilities use data in the field—not experimental data—and thus often cannot say if the findings reflect bias or actual differences. The few experimental studies conducted suggest bias against the abilities of girls and students of color, but the prior work has limitations, which we seek to address (e.g., local samples, no exploration of moderators, no preregistration). Methods In this preregistered experiment of 458 teachers across the U.S., we randomly assigned gender- and race-specific names to solutions to math problems, then asked teachers to rate the correctness of the solution, as well as the student’s math ability and effort. Teachers also completed scales reflecting their own beliefs and dispositions, which we then assessed how those beliefs/dispositions moderated their biases. We used multilevel modeling to account for the nested data structure. Results Consistent with our preregistered hypotheses, when the solution was not fully correct, findings suggest teachers thought boys had higher ability, even though the same teachers did not report differences in the correctness of the solution or perceived effort. Moreover, teachers who reported that gender disparities no longer exist in society were particularly likely to underestimate girls’ abilities. Although findings revealed no evidence of racial bias on average, teachers’ math anxiety moderated their ability judgments of students from different races, albeit with only marginal significance; teachers with high math anxiety tended to assume that White students had higher math ability than students of color. Conclusions The present research identifies teachers’ beliefs and dispositions that moderate their gender and racial biases. This experimental evidence sheds new light on why even low-performing boys consistently report higher math confidence and pursue STEM—namely, their teachers believe they have higher mathematical ability

Have Gender Gaps in Math Closed? Achievement, Teacher Perceptions, and Learning Behaviors Across Two ECLS-K Cohorts

Studies using data from the Early Childhood Longitudinal Study–Kindergarten Class of 1998–1999 (ECLS-K:1999) revealed gender gaps in mathematics achievement and teacher perceptions. However, recent evidence suggests that gender gaps have closed on state tests, raising the question of whether such gaps are absent in the ECLS-K:2011 cohort. Extending earlier analyses, this study compares the two ECLS-K cohorts, exploring gaps throughout the achievement distribution and examining whether learning behaviors might differentially explain gaps more at the bottom than the top of the distribution. Overall, this study reveals remarkable consistency across both ECLS-K cohorts, with the gender gap developing early among high achievers and spreading quickly throughout the distribution. Teachers consistently rate girls’ mathematical proficiency lower than that of boys with similar achievement and learning behaviors. Gender differences in learning approaches appear to be fairly consistent across the achievement distribution, but girls’ more studious approaches appear to have more payoff at the bottom of the distribution than at the top. Questions remain regarding why boys outperform girls at the top of the distribution, and several hypotheses are discussed. Overall, the persistent ECLS-K patterns make clear that girls’ early mathematics learning experiences merit further attention

Crowdsourcing hypothesis tests: making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim