Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Psychology, Evaluation, and Technology in Educational Research
Sari, Nuraeni Ratna
Unknown Affiliation
Computer Science & IT Education Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
WISC-V cognitive indicators of mathematical learning disabilities in elementary school students: A case-control study Wang, Fangping; Sari, Nuraeni Ratna; Wang, Bingli
Psychology, Evaluation, and Technology in Educational Research Vol. 8 No. 2 (2026): Article in Press
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/petier.v8i2.317

Abstract

Mathematical learning disabilities (MLD) involve persistent and heterogeneous difficulties in acquiring age-appropriate mathematical skills, requiring assessment through both cognitive and classroom-based indicators. This study examined whether WISC-V profiles differentiate elementary students with MLD from typically developing peers and support an integrated classification model. A case-control design involved 120 students aged 8–12 years, comprising 60 students formally identified with MLD and 60 age-matched controls. Participants completed the WISC-V Chinese Edition and a Mathematical Achievement Test, while teachers completed the Teacher Questionnaire on Mathematical Performance. Data were analyzed using t tests, effect sizes, correlations, discriminant analysis, and ROC analysis. Students with MLD scored significantly lower on all WISC-V primary indices, particularly Working Memory (d = 2.10) and Processing Speed (d = 1.60). Quantitative Reasoning showed the strongest ancillary-index separation (d = 3.03). Arithmetic, Digit Span, and Figure Weights were the most discriminating subtests. MAT deficits were greatest in word problems (d = 3.29) and mental computation (d = 3.03). QRI correlated most strongly with MAT total score (r = .84, p < .001), and the combined model achieved 89.2% accuracy and AUC = .986. QRI, WMI, Arithmetic, Digit Span, word problems, and mental computation are promising screening indicators for MLD, pending external validation.
Co-Authors Wang, Bingli Wang, Fangping