An official website of the United States government
Objective Historically, numerous studies have supported a male advantage in math. While more recent literature has shown that the gender gap is either decreasing or non-significant, a gender difference remains for higher level math (high school and college) (Hyde et. al. 1990; Casey et. al. 1995). It is known that both cognitive and non-cognitive factors influence math performance. There is little evidence for gender differences in working memory (Miller & Bichsel, 2004), which is a key predictor for mathematics. There is, however, evidence for gender differences in the non-cognitive domain, including math anxiety, with females having higher levels (Miller & Bichsel, 2004; Goetz, et. al. 2013). This study evaluates gender differences in both standardized and everyday math performances, and the way that cognitive and non-cognitive factors impact math. The study is focused on a very understudied group with high levels of math difficulty, namely community college students. We expected to find gender differences in math, and expect these to be in part accounted for by gender differences in strong mathematical predictors, particularly non-cognitive factors. Participants and Methods Participants included 94 community college students enrolled in their first math class (60 female; 34 male). Participants were administered the Kaufman Test of Educational Achievement – 3rd edition (KTEA3): Math Computation (MC) and Math Concepts Application (MCA) subtests, as well as an original Everyday Math (EM) measure which assessed their math ability in the context of common uses for math (e.g., financial and health numeracy). Additional measures included math anxiety, self-efficacy, and confidence. Finally, measures of complex span working memory tasks were administered to assess verbal and spatial working memory. Analyses were performed using correlation and regression to examine relationships between the cognitive and non-cognitive variables and standardized and everyday math measures. Results Correlations showed that all cognitive and non-cognitive variables are significantly correlated with all three math measures (all p < .05). There were no significant gender differences for any of the math measures, nor the working memory, or non-cognitive measures. Regression showed that across all three math outcomes, math anxiety and verbal working memory are significantly predictive of math performance. Overall R2 values were significant (range 27% to 37%, all p < .001). Working memory and math anxiety were unique predictors in all three regressions (all p < .05), but other non-cognitive variables such as self-efficacy did not show unique prediction (all p > .05). Conclusions There was no evidence for gender differences on any studied variable. This stands in contrast to prior studies, although few studies have included community college students. On the other hand, both cognitive and non-cognitive factors were complimentary in the prediction of math outcomes, which is consistent with prior work. Among non-cognitive predictors, math anxiety was particularly prominent. This study clarifies prior conflicting work regarding gender differences, and highlights the role of both math anxiety and working memory as relevant for multiple math outcomes.
more »
« less
This content will become publicly available on May 13, 2026
Non-linear relationships between math anxiety and performance in digital learning
Mathematics anxiety is a phenomenon characterized by feelings of tension and nervousness towards math (Ashcraft, 2002). Unsurprisingly, it has been extensively documented to be negatively associated with mathematical performance (Ramirez et al., 2018). Research consistently shows that math anxiety impacts cognitive processing abilities and diminishes working memory resources, leading to poorer problem-solving skills and lower achievement in mathematical tasks (Ashcraft & Krause, 2007; Ramirez et al., 2013). It is important to consider students with different math anxiety levels and patterns when creating new math learning interventions, as math anxiety affects how students perceive and learn from mathematical interventions. Recognizing and accommodating the diverse math interventions to math anxiety can help create more effective learning environments. In this dissertation, I will present three studies that examine how math anxiety interplays with math performance and manifests in learning.
more »
« less
- Award ID(s):
- 2320053
- PAR ID:
- 10599824
- Publisher / Repository:
- https://digital.wpi.edu/show/2f75rd59b
- Date Published:
- Format(s):
- Medium: X
- Institution:
- Worcester Polytechnic Institute
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
BackgroundMath anxiety (MA) and math achievement are generally negatively associated. AimsThis study investigated whether and how classroom engagement behaviors mediate the negative association between MA and math achievement. SampleData were drawn from an ongoing longitudinal study that examines the roles of affective factors in math learning. Participants consisted of 207 students from 4th through 6th grade (50% female). MethodsMath anxiety was measured by self‐report using the Mathematics Anxiety Scale for Children (Chiu & Henry, 1990,Measurement and valuation in Counseling and Development, 23, 121). Students self‐reported their engagement in math classrooms using a modified version of the Math and Science Engagement Scale (Wang et al., 2016,Learning and Instruction, 43, 16). Math achievement was assessed using the Applied Problem, Calculations, and Number Matrices subtests from the Woodcock‐Johnson IV Tests of Achievement (Schrank et al., 2014,Woodcock‐Johnson IV Tests of Achievement. Riverside). Mediation analyses were conducted to examine the mediating role of classroom engagement in the association between MA and math achievement. ResultsStudents with higher MA demonstrated less cognitive‐behavioral and emotional engagement compared to students with lower MA. Achievement differences among students with various levels of MA were partly accounted for by their cognitive‐behavioral engagement in the math classroom. ConclusionsOverall, students with high MA exhibit avoidance patterns in everyday learning, which may act as a potential mechanism for explaining why high MA students underperform their low MA peers.more » « less
-
This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students’ (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2) what types of in-game features (i.e. student in-game behaviors, math anxiety, mathematical strategies) were associated with student math knowledge scores. The results indicated that the Random Forest algorithm showed the best performance (i.e. the accuracy of models, error measures) in predicting posttest math knowledge scores among the seven algorithms employed. Out of 37 features included in the model, the validity of the students’ first mathematical transformation was the most predictive of their posttest math knowledge scores. Implications for game learning analytics and supporting students’ algebraic learning are discussed based on the findings.more » « less
-
Research Findings: Two hundred and sixty-seven Chilean children from grades 1–3, their fathers and their mothers completed measures of implicit and explicit math-related beliefs (math–gender stereotypes, math selfconcepts) and feelings (math anxiety), as well as tests of mathematical achievement. Children, fathers, and mothers exhibited stereotypes that link math with males. More specifically, mothers identified more with language than with math, while fathers and children identified more with math than with language. Path analyses models revealed that children’s explicit math self-concepts significantly predicted their actual math achievement. Children’s explicit self-concept was, in turn, explained marginally by the mathematical anxiety of their mothers. Practice or Policy: These results contribute to our understanding of the relation between parental and children’s beliefs and children’s math achievement during early elementary school years. In countries such as Chile, with a significant gender gap in math achievement, these findings may highlight relevant aspects to consider when designing interventions aimed at educational equity and providing equal mathematical learning opportunities to boys and girls.more » « less
-
There is a large gap between the ability of experts and students in grasping spatial concepts and representations. Engineering and the geosciences require the highest expertise in spatial thinking, and weak spatial skills are a significant barrier to success for many students [1]. Spatial skills are also highly malleable [2]; therefore, a current challenge is to identify how to promote students’ spatial thinking. Interdisciplinary research on how students think about spatially-demanding problems in the geosciences has identified several major barriers for students and interventions to help scaffold learning at a variety of levels from high school through upper level undergraduate majors. The Geoscience Education Transdisciplinary Spatial Learning Network (GET-Spatial; http://serc.carleton.edu/getspatial/) is an NSF-funded collaboration between geoscientists, cognitive psychologists, and education researchers. Our goal is to help students overcome initial hurdles in reasoning about spatial problems in an effort to diversify the geoscience workforce. Examples of spatial problems in the fields of geochemistry include scaling, both in size and time; penetrative thinking to make inferences about internal structures from surface properties; and graph-reading, especially ternary diagrams. Understanding scales outside of direct human experience, both very large (e.g. cosmochemistry, deep time) and very small (e.g. mineralogy, nanoparticles) can be acutely difficult for students. However, interventions have successfully resulted in improvements to scale estimations and improve exam performance [3]. We will discuss best practices for developing effective interdisciplinary teams, and how to overcome challenges of working across disciplines and across grade levels. We will provide examples of spatial interventions in scaling and penetrative thinking. [1] Hegarty et al. (2010) in Spatial Cognition VII 6222, 85- 94. [2] Uttal et al. (2012) Psychology of Learning and Motivation 57, 147-181. [3] Resnick et al. (2016) Educational Psychology Review, 1-15.more » « less
