A growing body of research has examined how children's self-regulation during early and middle childhood mediates SES disparities in academic achievement. Evidence suggests that these self -regulation skills begin developing even earlier, during the toddler years, but more work is needed examining how different measures of self-regulation relate to key constructs such as so-cioeconomic status (SES) and toddlers' pre-academic skills. In this online study, we examine multiple approaches to measuring self-regulation using confirmatory factor analyses and assess the extent to which self-regulatory skills help explain SES differences in early math and language skills among a sample of 158 two-and three-year-old children. Self-regulation was assessed through a battery of parent-and examiner-ratings. Children's counting, cardinality, and vocab-ulary skills were measured online through direct assessments and parent surveys. Two self -regulation factors emerged representing parent-reported and observational measures, and only observational measures of self-regulation mediated associations between SES and children's math and language skills. Parent-reported self-regulation was not uniquely related to SES or children's pre-academic skills, underscoring the need for careful consideration of how self-regulation is measured among toddlers when examining its associations with pre-academic skills.

Purpose of the study Previous literature has examined the relationship between high school students' postsecondary STEM major choices and their prior interest and perceived ability in mathematics. Yet, we have limited understanding of whether and how perceived ability and interest in science and mathematics jointly affect students' STEM major choices. Results Using the most recent nationally representative longitudinal cohort of U.S. secondary school students, we examine the degree to which students' perceived mathematical and scientific abilities and interests predict their STEM major choices, employing logistic regression and a series of interaction analyses. We find that while both mathematics and science perceived ability positively influence STEM major selection, academic interest in these subjects is a weaker predictor. Moreover, across a series of analyses, we observe a significant gender gap-whereby women are less than half as likely to select STEM majors-as well as nuanced distinctions by self-identified race. The relationships among perceived ability, interest, and STEM major choice are not found to meaningfully vary by race nor consistently by gender. However, perceived ability has a more positive effect for men than women who are pursuing Computing/Engineering majors and a more positive effect for women than men who are pursuing other STEM majors, including less applied Social/Behavioral, Natural, and Other Sciences. Implications These findings suggest potential opportunities to enhance their perceived mathematical and scientific abilities in high school, positioning them to potentially enter STEM fields. School sites with more resources to support the ambitions of STEM students of all backgrounds may be better positioned to reduce postsecondary disparities in STEM fields. Given existing opportunity gaps and resource differentials among schools, corresponding recommendations are suggested.

Systematic reviews and meta-analyses are important techniques because they synthesize results from multiple primary studies on a similar topic. To influence policy, practice, and research, however, synthesis researchers must translate the results for various audiences. Ideally, the translation drives future research agendas, informs policymaking, or assists in practical decision-making. An Evidence Gap Map (EGM), a graphical or tabular visualization of systematic review and meta-analysis results, is one ideal translation technique because it provides a structured framework to assess contexts for which primary evidence is available or to determine whether the effectiveness of an intervention or a program differs across populations, conditions, and settings. To bolster the field and promote the use of EGMs, we provide an overview of what constitutes an informative EGM, detail multiple examples of EGMs using extant meta-analytic results, and present a free R Shiny application we created to easily generate EGMs from typical meta-analytic datasets. We conclude by reviewing education-based systematic reviews that included an EGM to describe the current state of the field. [This paper will be published in "Journal of Research on Educational Effectiveness."]

Although early causal reasoning has been studied extensively, inconsistency in the tasks used to assess it has clouded our under-standing of its structure, development, and relevance to broader developmental outcomes. The current research attempted to bring clarity to these questions by exploring patterns of performance across several commonly used measures of causal reasoning, and their relation to scientific literacy, in a sample of 3-to 5-year-old children from diverse backgrounds (N = 153). A longitudinal confir-matory factor analysis revealed that some measures of causal rea-soning (counterfactual reasoning, causal learning, and causal inference), but not all of them (tracking cause-effect associations and resolving confounded evidence), assess a unidimensional fac-tor and that this resulting factor was relatively stable across time. A cross-lagged panel model analysis revealed associations between causal reasoning and scientific literacy across each age tested. Causal reasoning and scientific literacy related to each other con-currently, and each predicted the other in subsequent years. These relations could not be accounted for by children's broader cognitive skills. Implications for early STEM (science, technology, engineering, and math) engagement and success are discussed.(c) 2022 Elsevier Inc. All rights reserved.

The articles in this special issue provide state-of-the-art reviews of the brain and cognitive systems that are engaged during some aspects of mathematical learning, as well as the self-beliefs, anxiety, and social factors that influence engagement with mathematics, along with discussion of any associated sex differences. These issues are integrated into an evolutionary perspective that includes discussion of how evolved brain and cognitive systems might be co-opted for learning in the evolutionarily novel domain of mathematics. Attitudes and beliefs about mathematics are considered in the context of the evolution of self-awareness that in turn explains why many students do not value mathematics, despites its importance in the modern world, as highly as many other personal traits, such as their physical appearance. The overall argument is that reflecting on academic learning and attitudes from an evolutionary perspective provides insights into student learning and self-beliefs about learning that might otherwise elude explanation.

Attending to the whole unit that a number refers to in a mathematical problem situation and showing flexibility in coordinating different units are foundational for mathematical understanding. In this study, we explored teachers' attention to and flexibility with referent units in situations involving fractions and fraction multiplication. Using data collected across the USA from 246 mathematics teachers in Grades 3-7 where fractions are taught, we found that teachers' attention to and flexibility with referent units were related to each other as well as to teachers' overall knowledge of fractions.

Motion remains a significant technical hurdle in fMRI studies of young children. Our aim was to develop a straightforward and effective method for obtaining and preprocessing resting state data from a high-motion pediatric cohort. This approach combines real-time monitoring of head motion with a preprocessing pipeline that uses volume censoring and concatenation alongside independent component analysis based denoising. We evaluated this method using a sample of 108 first grade children (age 6-8) enrolled in a longitudinal study of math development. Data quality was assessed by analyzing the correlation between participant head motion and two key metrics for resting state data, temporal signal-to-noise and functional connectivity. These correlations should be minimal in the absence of noise-related artifacts. We compared these data quality indicators using several censoring thresholds to determine the necessary degree of censoring. Volume censoring was highly effective at removing motion-corrupted volumes and ICA denoising removed much of the remaining motion artifact. With the censoring threshold set to exclude volumes that exceeded a framewise displacement of 0.3 mm, preprocessed data met rigorous standards for data quality while retaining a large majority of subjects (83 % of participants). Overall, results show it is possible to obtain usable resting-state data despite extreme motion in a group of young, untrained subjects.

Clickstream data have been used increasingly to present students in online courses with analytics about their learning process to support self-regulation. Drawing on self-regulated learning theory and attribution theory, we hypothesize that providing students with analytics on their own effort along with the effort and performance of relevant peers will help students attribute their performance to factors under their control and thus positively influence their subsequent behavior and performance. To test the effect of the analytics and verify the proposed mechanism, we conducted an experiment in an online undergraduate course in which students were randomly assigned to receive theoretically inert questions (control condition), attribution questions (active control condition), and the analytics with attribution questions (treatment condition). The intervention significantly increased effort attribution, reduced ability attribution, and improved subsequent effort for a subgroup of students who self-reported low performance, although there was no significant impact on their performance.

The COVID-19 pandemic has necessitated disease surveillance using group testing. Novel Bayesian methods using lattice models were proposed, which offer substantial improvements in group testing efficiency by precisely quantifying uncertainty in diagnoses, acknowledging varying individual risk and dilution effects, and guiding optimally convergent sequential pooled test selections. Computationally, however, Bayesian group testing poses considerable challenges as computational complexity grows exponentially with sample size. HPC and big data stacks are needed for assessing computational and statistical performance across fluctuating prevalence levels at large scales. Here, we study how to design and optimize critical computational components of Bayesian group testing, including lattice model representation, test selection algorithms, and statistical analysis schemes, under the context of parallel computing. To realize this, we propose a high-performance Bayesian group testing framework named HiBGT, based on Apache Spark, which systematically explores the design space of Bayesian group testing and provides comprehensive heuristics on how to achieve highperformance, highly scalable Bayesian group testing. We show that HiBGT can perform large-scale test selections (> 250 state iterations) and accelerate statistical analyzes up to 15.9x (up to 363x with little trade-offs) through a varied selection of sophisticated parallel computing techniques while achieving near linear scalability using up to 924 CPU cores.

This article explores three attributes of teachers' understanding of fraction magnitude: the accuracy and reasonableness of teachers' estimations in response to fraction arithmetic tasks as well as the alignment of the estimation strategies they used with the concept of fraction magnitude. The data were collected from a national sample of mathematics teachers in grades 3-7 in which fraction concepts were taught (N = 603). The results indicated the teachers' estimations were only partially accurate and reasonable, particularly when fraction division was involved. Furthermore, teachers' credentials and the grade level at which they taught mathematics were significantly related to teachers' understanding of fraction magnitude.