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1. Cognition and math and reading overlap in community college students

   Cirino, PT; Salentine, C; Boada, C; Wall, J (February 2024, Poster presented at the 52nd annual meeting of the International Neuropsychological Society, New York, NY.)

   Objective Math and reading are related at the disability level and along the continuum of skill (Cirino, 2022). Cognitive correlates of math and reading in children are well-known separately, with a recent focus on the reason for their overlap. However, less is known about these issues in community college (CC) students despite more than half of post-secondary education occurring at this level. Here we assess cognitive predictors of math and reading (language, working memory, processing speed, nonverbal reasoning, attention) in CC students, outcome overlap, and the extent that predictors account for overlap. We expect all predictors to relate to achievement, with language and working memory as the strongest predictors, and accounting for the most overlap. We also expect more overlap and stronger prediction for complex outcomes (reading comprehension and math applications) relative to foundational skills (word reading and computations). Participants and Methods Participants were 94 CC students enrolled in their first math class. Approximately half the students were taking developmental coursework. Participants were administered four KTEA-3 measures: Letter-Word Reading, Reading Comprehension, Math Computation, and Math Concepts and Application. Language consisted of Vocabulary (K-BIT-2), and Elision and Rapid Naming subtests of the CTOPP-2. Working memory was assessed with two complex span measures (Symmetry Span and Reading Span). Processing speed was measured with the WAIS-IV, and nonverbal reasoning with the K-BIT-2. Attention was assessed via a researcher-designed continuous performance task and a self-rating scale. Multiple regression assessed cognitive prediction for each achievement measure; and partial correlation evaluated overlap. Results For computations, all predictors accounted for R2=53% variance; nonverbal reasoning and elision were unique predictors (p<.05). For math applications, R2=58%, with unique prediction for nonverbal reasoning, vocabulary, elision, and symmetry span. For word reading, R2=50%, with unique prediction for vocabulary, elision, and reading span. Finally, for reading comprehension, R2=47%, with unique prediction for vocabulary and nonverbal reasoning. Regarding overlap, computations and word reading correlated r=.50, and math applications and reading comprehension r=.57, which is higher than a recent meta-analysis (Unal et al., 2023). Language was the strongest contributor of overlap; these variables reduced the correlation for foundational achievement by 50%, and for complex achievement, by 32%. Other domains accounted for little overlap, despite significant zero-order correlations. Substantive results were generally similar when covariates were considered. Conclusions Individual prediction was dominated by language, nonverbal reasoning, and working memory variables. Math and reading performances were strongly related, and language was the strongest predictor of this overlap, which is only partially consistent with extant literature but adds context and generalization for CC students. Attention and processing speed were only weakly related to performance, which may reflect the overlearned nature of these skills at this level. Future work might need to include more malleable factors (e.g., motivation), as well as broader views of achievement (e.g., course grades). 

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2. Attention check questions in self-report measurement of math and its non-cognitive factors in college students

   Boada, C; Salentine, C; Cirino, PT (February 2024, Poster presented at the 52nd annual meeting of the International Neuropsychological Society, New York, NY.)

   Objective Online surveys are a common method of data collection. The use of “attention-check” questions are an effective method of identifying careless responding in surveys (Liu & Wronski, 2018; Meade & Craig 2012; Ward & Meade, 2023), which occurs in 10-12% of undergraduate samples (Meade & Craig, 2012). Instructed response type attention checks are straightforward and the most recommended (Meade & Craig, 2012; Ward & Meade, 2023). This study evaluated the effect of instructed response attention check questions on the measurement of math ability and non-cognitive factors commonly related to math (self-efficacy and math anxiety). We evaluated both level differences as well as whether check questions alter the relationship of non-cognitive factors to math. We expected that incorrect responding to check questions would lower math performance but were unable to make hypotheses about level of self-report non-cognitive factors. We predicted that incorrect responding to check questions would moderate the relationship between both math anxiety and self-efficacy to math performance. Participants and Methods Participants were 424 undergraduates (age 20.4, SD=2.7) at a large southwestern university. The sample was majority female (74%) but diverse socioeconomically and in race/ethnicity. The non-cognitive measures were researcher developed Math Anxiety (MA) and Math Self-Efficacy (MSE; Betz & Hackett, 1993) scales, with items selected directly targeting the use/manipulation of math in everyday life; both showed good reliability (α=.95). The two math scales were also researcher developed; one was a pure symbolic computational measure (EM-A) and the other consisted of word problems in an everyday context (EM-B). These measures had good reliability (α=.80 and α=.73). The four check questions were embedded in the surveys and two groupings were formed – one consisting of those who provided the correct answer for all items versus those who did not, and a second consisting of those who got all correct or only one answer incorrect versus those with more items incorrect. Correlational, ANOVA, and ANCOVA models were utilized. Results Descriptively, check questions were skewed – 75% participants answered all check questions correctly, and 8% missed only one. Relations of both MA and MSE with EM-A and EM-B were modest though significant (|r|=.22 to .37) and in the expected directions (all p<.001). Check questions were related to level of all tasks (p<.001), with incorrect responses resulting in lower math performance, lower MSE, and higher MA. Check questions did not moderate the relation of MA or MSE to either math performance, with some suggestion that MA was more strongly related to EM-B in those who missed check questions, though only when failing several. Conclusions Check questions showed a clear relation to both self-report and math performance measures. However, check questions did not alter the relation of MA or MSE to math performance in general. These results affirm extant relations of key self-perceptions to math using novel measures and highlight the need to evaluate the validity of self-report measures, even outside of objective performance indicators. Future work could examine the effect of attention checks in domains other than math and investigate other types of attention checks. 

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3. Non-linear relationships between math anxiety and performance in digital learning

   Egorova, A (May 2025, https://digital.wpi.edu/show/2f75rd59b)

   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. 

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4. A multidimensional examination of math anxiety and engagement on math achievement

   https://doi.org/10.1111/bjep.12482  

   Quintero, Michaela; Hasty, Leslie; Li, Tianyu; Song, Seowon; Wang, Zhe (December 2021, British Journal of Educational Psychology)

   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. 

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5. Comorbid learning difficulties in reading and mathematics: The role of intelligence and in-class attentive behavior

   https://doi.org/doi/10.3389/fpsyg.2020.572099  

   Geary, D. C. (October 2020, Frontiers in psychology)

   null (Ed.)

   The goal was to identify the domain-general cognitive abilities and academic attitudes that are common and unique to reading and mathematics learning difficulties that in turn will have implications for intervention development. Across seventh and eighth grade, 315 (155 boys) adolescents (M age = 12.75 years) were administered intelligence, verbal short-term and working memory, and visuospatial memory, attention, and ability measures, along with measures of English and mathematics attitudes and mathematics anxiety. Teachers reported on students’ in-class attentive behavior. A combination of Bayesian and multi-level models revealed that intelligence and in-class attentive behavior were common predictors of reading accuracy, reading fluency, and mathematics achievement. Verbal short-term memory was more critical for reading accuracy and fluency, whereas spatial ability and mathematics self-efficacy were more critical for mathematics achievement. The combination of intelligence and in-class attentive behavior discriminated typically-achieving students from students with comorbid (D = 2.44) or mathematics (D = 1.59) learning difficulties, whereas intelligence, visuospatial attention, and verbal short-term memory discriminated typically-achieving students from students with reading disability (D = 1.08). The combination of in-class attentive behavior, verbal short-term memory, and mathematics self-efficacy discriminated students with mathematics difficulties from their peers with reading difficulties (D = 1.16). Given the consistent importance of in-class attentive behavior, we conducted post hoc follow-up analyses. The results suggested that students with poor in-class attentive behavior were disengaging from academic learning which in turn contributed to their risk of learning difficulties. 

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