Meta-analyses from the 1990s have previously established a significant, small-tomoderate, and negative correlation between math achievement and math anxiety. Since these publications, research has continued to investigate this relation with more diverse samples and measures. Thus, the goal of the present meta-analysis was to provide an update of the math anxiety-math achievement relation and its moderators. Analyzing 747 effect sizes accumulated from research conducted between 1992 and 2018, we found a small-to-moderate, negative, and statistically significant correlation (r =-.28) between math anxiety and math achievement. The relation was significant for all moderator subgroups, with the exception of the relation between math anxiety and assessments measuring the approximate number system. Grade level, math ability level, adolescent/adult math anxiety scales, math topic of anxiety scale, and math assessments were significant moderators of this relation. There is also a tendency for published studies to report significantly stronger correlations than unpublished studies but, overall, large, negative effect sizes are under-reported. Our results are consistent with previous findings of a significant relation between math anxiety and math achievement. This association starts in childhood, remains significant through adulthood, is smaller for students in grades 3 through 5 and postsecondary school, is larger for math anxiety than for statistics anxiety and for certain math anxiety scales, and is smaller for math exam grades and samples selected for low math ability. This work supports future research efforts to determine effective math achievement and math anxiety interventions, which may be most helpful to implement during childhood.

Mathematical thinking is in high demand in the global market, but approximately 6 percent of school-age children across the globe experience math difficulties (Shalev et al., 2000). The home math environment (HME), which includes all math-related activities, attitudes, beliefs, expectations, and utterances in the home, may be associated with children’s math development. To examine the relation between the HME and children’s math abilities, a preregistered meta-analysis was conducted to estimate the average weighted correlation coefficient (r) between the HME and children’s math achievement and how potential moderators (i.e., assessment, study, and sample features) might contribute to study heterogeneity. A multilevel correlated effects model using 631 effect sizes from 64 quantitative studies comprising 68 independent samples found a positive, statistically significant average weighted correlation of r = .13 (SE = .02, p < .001). Our combined sensitivity analyses showed that the present findings were robust and that the sample of studies has evidential value. A number of assessment, study, and sample characteristics contributed to study heterogeneity, showing that no single feature of HME research was driving the large between-study differences found for the association between the HME and children’s math achievement. These findings indicate that children’s environments and interactions related to their learning are supported in the specific context of math learning. Our results also show that the HME represents a setting in which children learn about math through social interactions with their caregivers (Vygotsky, 1978) and what they learn depends on the influence of many levels of environmental input (Bronfenbrenner, 1979) and the specificity of input children receive (Bornstein, 2002).

According to the Multiple Deficit Model, comorbidity results when the genetic and environmental risk factors that increase the liability for a disorder are domain-general. In order to explore the role of domain-general etiological risk factors in the co-occurrence of learning-related difficulties, the current meta-analysis compiled 38 studies of third through ninth-grade children to estimate the average genetic, shared environmental, and nonshared environmental correlations between reading and attention-deficit/hyperactivity disorder (ADHD) symptoms, and reading and math, as well as their potential moderators. Results revealed average genetic, shared and nonshared environmental correlations between reading and ADHD symptoms of .42, .64, and .20, and reading and math of .71, .90, and .56, suggesting that reading and math may have more domain-general risk factors than reading and ADHD symptoms. A number of significant sources of heterogeneity were also found and discussed. These results have important implications for both intervention and classification of learning disabilities.

Recent achievement research suggests that executive function (EF), a set of regulatory processes that control both thought and action necessary for goal-directed behavior, is related to typical and atypical reading performance. This project examines the relation of EF, as measured by its components, Inhibition, Updating Working Memory, and Shifting, with a hybrid model of reading disability (RD). Our sample included 420 children who participated in a broader intervention project when they were in KG-third grade (age M = 6.63 years, SD = 1.04 years, range = 4.79–10.40 years). At the time their EF was assessed, using a parent-report Behavior Rating Inventory of Executive Function (BRIEF), they had a mean age of 13.21 years (SD = 1.54 years; range = 10.47–16.63 years). The hybrid model of RD was operationalized as a composite consisting of four symptoms, and set so that any child could have any one, any two, any three, any four, or none of the symptoms included in the hybrid model. The four symptoms include low word reading achievement, unexpected low word reading achievement, poorer reading comprehension compared to listening comprehension, and dual-discrepancy response-to-intervention, requiring both low achievement and low growth in word reading. The results of our multilevel ordinal logistic regression analyses showed a significant relation between all three components of EF (Inhibition, Updating Working Memory, and Shifting) and the hybrid model of RD, and that the strength of EF’s predictive power for RD classification was the highest when RD was modeled as having at least one or more symptoms. Importantly, the chances of being classified as having RD increased as EF performance worsened and decreased as EF performance improved. The question of whether any one EF component would emerge as a superior predictor was also examined and results showed that Inhibition, Updating Working Memory, and Shifting were equally valuable as predictors of the hybrid model of RD. In total, all EF components were significant and equally effective predictors of RD when RD was operationalized using the hybrid model.

ABSTRACT. In this study, we explore student achievement in a semester-long flipped Calculus II course, combining various predictor measures related to student attitudes (math anxiety, math confidence, math interest, math importance) and cognitive skills (spatial skills, approximate number system), as well as student engagement with the online system (discussion forum interaction, time to submission of workshop assignments, quiz attempts), in predicting final grades. Data from 85 students enrolled in a flipped Calculus II course was used in dominance analysis to determine which predictors emerged as the most important for predicting final grades. Results indicated that feelings of math importance, approximate number system (ANS) ability, total amount of discussion forum posting, and time grading peer workshop submissions was the best combination of predictors of final grade, accounting for 17% of variance in a student's final grade. The point of this work was to determine which predictors are the most important in predicting student grade, with the end goal of building a recommendation system that could be implemented to help students in this traditionally difficult class. The methods used here could be used for any class.Keywords: Math performance, calculus, flipped classroom, math attitudes, cognitive performance, student engagement INTRODUCTIONOver the past few years, the attrition of STEM-focused undergraduates in the United States has become a critical national concern, with "a substantial number of undergraduate students initially enrolled in STEM degree programs [dropping] out in the first two years" (PCAST, 2012). In order to maintain highquality instructional practices in the face of STEM undergraduate attrition, growing demand for large (2017 130 numbers of graduates, and diminishing financial and human resources, many colleges and universities are turning to technologically aided teaching practices. This technological shift aims to integrate the traditional classroom environment with online course resources to enhance, replace, and supplement face-to-face instruction to reach more students in a cost-effective way (Garrison & Kanuka, 2004).To accommodate this shift toward technologically aided teaching methods, schools have implemented campus-wide Learning Management Systems (LMS), such as Moodle and Blackboard. These web-based management systems provide data related to the "user," with every action of the student tracked and recorded online. The underlying advantage of these LMS data is that they unobtrusively record individual student activity and interaction with course materials in real-time, providing a lens into traditionally unobservable learning-related behaviours and silently tracking individual students' learning progression (Macfadyen & Dawson, 2010; Gašević, Dawson, & Siemens, 2015). In traditional college courses, many performance measures, for example midterm or final exams (Lee, Speglia, Ha, Finch, & Nehm, 2015;Macfadyen & Dawson, 2010), are taken too late in the semester to identify st...