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Volume 24, Issue 150 (8-2025)
Journal of Psychological Science 2025, 24(150): 185-208 |
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hanifi vahed M, Tale pasand S, Moradi S. (2025). Clustering of Cognitive subtypes of Mathematics Disorders in Elementary School Students with Math Disorders. Journal of Psychological Science. 24(150), 185-208.
URL: http://psychologicalscience.ir/article-1-2659-en.html
URL: http://psychologicalscience.ir/article-1-2659-en.html
Professor, Department of Education, Faculty of Education and Psychology, Semnan University, Semnan, Iran. , stalepasand@semnan.ac.ir
Abstract: (48 Views)
Background: Mathematics disorder is a learning disability that makes understanding and performing mathematical calculations challenging for children. Furthermore, numerous gaps exist in domestic studies regarding the identification and classification of the cognitive subtypes of this disorder. This study aims to evaluate general and specific cognitive processing types in children with mathematics disorder.
Aims: The study aimed to identify the cognitive subtypes of mathematics disorder in elementary school students.
Methods: This descriptive-correlational study was conducted with two target populations, the first consisted of all students referred to learning disability centers in Alborz Province diagnosed with poor mathematical performance, and the second comprised all third- to sixth-grade elementary students in Alborz Province during the 2023-2024 academic year. The participants included 96 students diagnosed with mathematics disorder selected through random sampling and 180 typically developing students selected through stratified random sampling. Participants completed twelve cognitive tasks designed using Psychopy software, the Wechsler Intelligence Scale for Children-IV, the Frostig visual perception test, the Wepman auditory perception test, the Iran-Key math test, and the Rutter Behavioral Rating Scale. The data were analyzed using t-test, exploratory factor analysis and cluster analysis spss 24 software was used for data analysis.
Results: The exploratory factor analysis identified six factors: arithmetic cognitive performance, cognitive comparison performance, numerical cognitive processing, cognitive processing speed, arithmetic processing speed, and response accuracy, which together explained 74% of the variance. The cluster analysis revealed six distinct cognitive clusters among children with math disorders: cognitive processing divergence, computation and accuracy with delayed processing, impaired numerical cognition, numerical and accuracy deficits, processing and accuracy deficits, and arithmetic and comparison deficits.
Conclusion: The findings suggest that mathematics disorder is a heterogeneous condition. Therapists and educational systems can use these results to design targeted interventions tailored to the specific subtype of mathematics disorder in children with mathematical difficulties.
Aims: The study aimed to identify the cognitive subtypes of mathematics disorder in elementary school students.
Methods: This descriptive-correlational study was conducted with two target populations, the first consisted of all students referred to learning disability centers in Alborz Province diagnosed with poor mathematical performance, and the second comprised all third- to sixth-grade elementary students in Alborz Province during the 2023-2024 academic year. The participants included 96 students diagnosed with mathematics disorder selected through random sampling and 180 typically developing students selected through stratified random sampling. Participants completed twelve cognitive tasks designed using Psychopy software, the Wechsler Intelligence Scale for Children-IV, the Frostig visual perception test, the Wepman auditory perception test, the Iran-Key math test, and the Rutter Behavioral Rating Scale. The data were analyzed using t-test, exploratory factor analysis and cluster analysis spss 24 software was used for data analysis.
Results: The exploratory factor analysis identified six factors: arithmetic cognitive performance, cognitive comparison performance, numerical cognitive processing, cognitive processing speed, arithmetic processing speed, and response accuracy, which together explained 74% of the variance. The cluster analysis revealed six distinct cognitive clusters among children with math disorders: cognitive processing divergence, computation and accuracy with delayed processing, impaired numerical cognition, numerical and accuracy deficits, processing and accuracy deficits, and arithmetic and comparison deficits.
Conclusion: The findings suggest that mathematics disorder is a heterogeneous condition. Therapists and educational systems can use these results to design targeted interventions tailored to the specific subtype of mathematics disorder in children with mathematical difficulties.
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This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. |
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)