Mathematical Ability of Autistic Youth: What Best Explains the Heterogeneity?

• Author / Creator

  Hoveling, Léonie Antoinette Maria

• Background: Autistic individuals are often stereotyped as gifted mathematicians and, as a result, autistic traits and behaviours are generally believed to lead to exceptional mathematical ability. Much of society’s collective understanding of autism is based on media representations, which often foster unrealistic expectations of their skills by families and teachers, leading to additional barriers for autistic students (Draaisma, 2009). This is concerning given that autistic students are more likely to have a mathematics learning disability (LD) compared to mathematical giftedness (Oswald et al., 2016). Autistic students may struggle in the upper grades when mathematics relies more heavily upon abstract reasoning and becomes cognitively complex (Barnett & Cleary, 2019). In fact, studies have found several variables that influence the mathematics achievement of autistic students such as language and executive function (EF) skills (Bullen et al., 2020; Chen et al., 2019; Polo-Blanco et al., 2022; St. John et al., 2018).
  Objectives: Across two mathematics composites (Basic Concepts and Applications), I explored the relative proportions of low– and high–achieving (± 1 SD of the norm) autistic youth compared to the expected rates in the general population. Second, I investigated the relative contributions of fluid reasoning (Gf), expressive language, and EF on the Basic Concepts composite.
  
  Methods & Results: 33 autistic youth (aged 5–16 years) completed measures of mathematical ability (KeyMath-3 DACDN), fluid reasoning (Gf; Raven’s Progressive Matrices or Leiter-3), expressive language (CELF-5), EF (BRIEF2), and autistic traits (SRS-2). Chi–squared tests for Basic Concepts (p < .01, φ = .54) and Applications (p < .05, φ = .47) revealed a significantly higher proportion of autistic youth who were low–achieving (21.21% and 25%, respectively) and/or high–achieving (33.33% and 28.57%, respectively) in my sample compared to the expected rates in the general population (approximately 15%). Using the Basic Concepts composite, a hierarchical regression analysis revealed that Gf was the strongest predictor of mathematical ability accounting for 53% of the model variance (β = .54, p < .001). Furthermore, expressive language accounted for 8% of the model variance (β = .33, p < .05) and EF accounted for 5% of the model variance (β = -.23, p < .05).
  Conclusions: My findings challenge prevailing stereotypes of an autistic advantage in mathematics and emphasize the need to understand the unique strengths and needs of each autistic student. My study highlights the importance of promoting more accurate representations of the distribution of mathematical ability among autistic youth. While studies have consistently reported that Gf is associated with mathematics achievement in non–autistic individuals (Cormier et al., 2017), this study found that Gf is a large and important predictor of autistic students’ mathematical ability. My findings regarding the importance of expressive language and EF skills were less clear but both were significant predictors of mathematical ability. Future researchers should consider a) exploring the relationships among these predictors with a larger sample; b) using a holistic measure of language ability to quantify how specific language subskills may be related to different mathematical abilities; and c) incorporating a direct measure of EF that will provide an objective and reliable way to quantify EF. In sum, this study highlights the importance of promoting more accurate representations of the broad range of mathematical ability across autistic youth. Understanding which variables are most strongly related to mathematics achievement among autistic students can help researchers, clinicians, and educators develop effective interventions and strategies to ensure autistic students receive the necessary resources and support to succeed in mathematics.

• Subjects / Keywords

  • autism
  • mathematics
  • pedagogy
  • fluid reasoning
  • language
  • executive functions
  • learning

• Graduation date

  Fall 2023

• Type of Item

  Thesis

• Degree

  Master of Education

• DOI

  https://doi.org/10.7939/r3-2vsq-et94

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.