The COVID-19 pandemic exposed the limits of big data to guide decision-making in times of crisis. As people navigated daily life, they were confronted with the reality that data were often not yet material but rather in-the-making. Drawing upon critical and feminist lenses and participatory methodologies, this study investigates the data stories of nine people of Asian descent living in the United States. Findings illustrate how participants navigated within and across time, space, activity, media, epistemology, race, and politics to produce lively data assemblages. These data stories guided social-distancing and mask-wearing weeks before official US policy even as participants lived in constant fear of dehumanizing racist and xenophobic violence. This study advances theorizing about data practices for human knowing and learning with media, racial and epistemic (in)justice, and community action. It also advances participatory research as a site of epistemic resistance and activism.

The public acceptance of evolution remains a contentious issue in the United States. Numerous investigations have used national cross-sectional studies to examine the factors associated with the acceptance or rejection of evolution. This analysis uses a 33-year longitudinal study that followed the same 5000 public-school students from grade 7 through midlife (ages 45-48) and is the first to do so in regard to evolution. A set of structural equation models demonstrate the complexity and changing nature of influences over these three decades. Parents and local influences are strong during the high school years. The combination of post-secondary education and occupational and family choices demonstrate that the 15 years after high school are the switchyards of life.

This pilot study examined the feasibility of using worked examples as a mechanism to improve verbal explanations of fractions concepts among Grade 5 students with mathematics difficulties in a small-scale randomized controlled trial (RCT) before scaling up to a large-scale RCT. Students (N = 49) were randomly assigned to a business-as-usual (BAU) comparison group and to two variants of intervention. One intervention condition received both correct and incorrect worked example solutions, the other received correct solutions only. On a measure of verbal explanations, intervention students significantly outperformed students in BAU. Furthermore, students who received both correct and incorrect solutions significantly outperformed students who received correct solutions only on verbal explanations. On fractions proficiency outcomes, results were not significant between intervention and BAU or between the two intervention groups; however, positive findings demonstrate promise for using worked examples to elicit and develop students' verbal explanations of fractions concepts.

Chatbots represent a promising technology for engaging students in math learning. Guided by Jerome Bruner's constructivism and Lev Vygotsky's Zone of Proximal Development, we designed and developed a chatbot that incorporates scaffolding strategies and social-emotional considerations, and we integrated it into ASSISTments, an online math learning platform. We conducted an experimental study to examine the influence of learning math with the chatbot compared to traditional learning with hints. This study involved 85 middle and high school students from three diverse school settings in the United States. The results revealed no significant differences in students' math learning performance and perceived helpfulness and interest between the chatbot and traditional hints conditions. However, students in the chatbot condition displayed significantly lower confidence in solving a similar problem after the intervention, likely due to the removal of the high level of support provided by the chatbot. Despite this, students' open responses indicated that a significantly higher number of students had positive attitudes towards chatbots. They appreciated the chatting feature, breaking down a problem into steps, and real-time support. The study concludes with a discussion of the findings and implications for chatbot designers and developers and presents avenues for future research and practice in chatbot-assisted learning. In support of Open Science, this study has been preregistered and both the data and the analysis code used in this study are publicly available at https://osf.io/am3p8/.

Impacts of rational number interventions among U.S. students in Grades 3 through 9 with mathematics difficulties are examined using a systematic review and meta-analysis. The primary goal of the meta-analysis was to identify instructional practices that are key drivers of student impacts. From approximately 1,200 published and unpublished study records, we identified 28 studies that met our inclusion criteria and coded the interventions for their instructional practices, intervention characteristics, and study design features. The random-effects mean effect size across all 28 studies (90 effect sizes) was 0.68 (SE = 0.08, p < .001, 95% confidence interval [CI]: [0.51, 0.85]). The 95% prediction interval was -0.36 to 1.8. Using meta-regression techniques, we found the teaching of mathematical language (beta = 0.50) and the use of the number line (beta = 0.47) during intervention to be significantly associated with positive impacts when adjusted for controls. We discuss implications for intervention practice and study limitations along with the challenges of examining complex, multifaceted interventions.

This study contributes to research exploring social factors shaping gender identification. Informed by structural symbolic interactionism, social identity theory, and Levitt's psychosocial theory of gender, we explore how a key aspect of external social structure-adolescent family socioeconomic status-is associated with gender identification in emerging adulthood. We examine whether correlates of family socioeconomic status, including adolescent family and educational experiences and friend and high school characteristics, are associated with a cisgender, binary transgender, nonbinary, or gender unsure identification. Using data from High School Longitudinal Study of 2009 (HSLS:09), we find a positive association between adolescent family socioeconomic status and a nonbinary gender identification. Analyses indicate that educational and family experiences account for the largest percentage of the association between adolescent family socioeconomic status and nonbinary gender identification, potentially representing higher SES youths' heightened access to middle- and upper-class cultural schemas and resources.

The literature linking adulthood criminality to cumulative disadvantage and early school misbehavior demonstrates that understanding the mechanisms underlying student behavior and the responses of teachers and administrators is crucial in comprehending racial/ethnic disparities in actual or perceived school misbehavior. We use data on 19,160 ninth graders from the nationally representative High School Longitudinal Study of 2009 to show that boys' and girls' negative achievement and negative experiences with teachers relate more closely to school misbehavior than the contextual measures (e.g., negative peer climate, proportion Black) that have often been emphasized as most salient for misbehavior. Differences in negative achievement and experiences completely explain Black boys', Latinx boys', and Black girls' heightened levels of school misbehavior relative to White youth, and Asian boys' and girls' lower levels of school misbehavior. In contrast, differences in negative achievement and experiences only partially explain Latinx girls' higher levels of school misbehavior relative to White girls.

Increasing diversity in science, technology, engineering, and math (STEM) and STEM-related degrees and professions is a national priority. Research on students’ pathways in STEM may contribute to our understanding of how to change institutions to achieve diversity; however, until recently, the dominant narrative invoked a “pipeline” metaphor. In this work, we challenge the pipeline metaphor by interrogating what is meant by a “STEM” pathway, measuring constructs not typically measured in STEM pipeline research, endeavoring to make our measures intersectional, and imagining alternative outcomes in addition to “staying in STEM.” We have been following students who completed an out-of-school mentored science research program since 2017. Three hundred fifty-eight participants responded to an alumni survey designed to collect data about their location along their pathway, constructs related to the pursuit of a pathway, and demographic information. Here, we describe the characteristics of this sample and initial findings about the new constructs we measured. By measuring constructs not typically measured in pathways research and designing items and scales using an intersectional approach, we challenge the problematic pipeline metaphor that dominates the STEM persistence literature.

Accurate item parameters and standard errors (SEs) are crucial for many multidimensional item response theory (MIRT) applications. A recent study proposed the Gaussian Variational Expectation Maximization (GVEM) algorithm to improve computational efficiency and estimation accuracy (Cho et al., 2021). However, the SE estimation procedure has yet to be fully addressed. To tackle this issue, the present study proposed an updated supplemented expectation maximization (USEM) method and a bootstrap method for SE estimation. These two methods were compared in terms of SE recovery accuracy. The simulation results demonstrated that the GVEM algorithm with bootstrap and item priors (GVEM-BSP) outperformed the other methods, exhibiting less bias and relative bias for SE estimates under most conditions. Although the GVEM with USEM (GVEM-USEM) was the most computationally efficient method, it yielded an upward bias for SE estimates.

Using data from 15 countries, this article investigates whether descriptive and prescriptive gender norms concerning housework and child care (domestic work) changed after the onset of the COVID-19 pandemic. Results of a total of 8,343 participants ( M = 19.95, SD = 1.68) from two comparable student samples suggest that descriptive norms about unpaid domestic work have been affected by the pandemic, with individuals seeing mothers’ relative to fathers’ share of housework and child care as even larger. Moderation analyses revealed that the effect of the pandemic on descriptive norms about child care decreased with countries’ increasing levels of gender equality; countries with stronger gender inequality showed a larger difference between pre- and post-pandemic. This study documents a shift in descriptive norms and discusses implications for gender equality—emphasizing the importance of addressing the additional challenges that mothers face during health-related crises.