Two Dyscalculia Subtypes With Similar, Low Comorbidity Profiles: A Mixture Model Analysis

Arithmetic fluency and number processing skills in identifying students with mathematical learning disabilities

BACKGROUND

Students with mathematical learning disabilities (MLD) struggle with number processing skills (e.g., enumeration and number comparison) and arithmetic fluency. Traditionally, MLD is identified based on arithmetic fluency. However, number processing skills are suggested to differentiate low achievement (LA) from MLD.

AIMS

This study investigated the accuracy of number processing skills in identifying students with MLD and LA, based on arithmetic fluency, and whether the classification ability of number processing skills varied as a function of grade level.

METHODS AND PROCEDURES

The participants were 18,405 students (girls = 9080) from Grades 3-9 (ages 9-15). Students' basic numerical skills were assessed with an online dyscalculia screener (Functional Numeracy Assessment -Dyscalculia Battery, FUNA-DB), which included number processing and arithmetic fluency as two factors.

OUTCOMES AND RESULTS

Confirmatory factor analyses supported a two-factor structure of FUNA-DB. The two-factor structure was invariant across language groups, gender, and grade levels. Receiver operating characteristics curve analyses indicated that number processing skills are a fair classifier of MLD and LA status across grade levels. The classification accuracy of number processing skills was better when predicting MLD (cut-off < 5 %) compared to LA (cut-off < 25 %).

CONCLUSIONS AND IMPLICATIONS

Results highlight the need to measure both number processing and arithmetic fluency when identifying students with MLD.

AI-Enhanced Dyscalculia Screening: A Survey of Methods and Applications for Children

New forms of interaction made possible by developments in special educational technologies can now help students with dyscalculia. Artificial intelligence (AI) has emerged as a promising tool in recent decades, particularly between 2001 and 2010, offering avenues to enhance the quality of education for individuals with dyscalculia. Therefore, the implementation of AI becomes crucial in addressing the needs of students with dyscalculia. Content analysis techniques were used to examine the literature covering the influence of AI on dyscalculia and its potential to assist instructors in promoting education for individuals with dyscalculia. The study sought to create a foundation for a more inclusive dyscalculia education in the future through in-depth studies. AI integration has had a big impact on educational institutions as well as people who struggle with dyscalculia. This paper highlights the importance of AI in improving the educational outcomes of students affected by dyscalculia.

Heterogeneity in children at risk of math learning difficulties

This study recruited 428 Singaporean children at risk of math learning difficulties (MLD; M_age  = 83.9 months, SD_age  = 4.35 months; 41% female). Using a factor mixture model that considered both quantitative and qualitative differences in math ability, two qualitatively different groups were identified: one with generalized difficulties across different math skills and the other with more focal difficulties in arithmetic fluency. Reading, working memory capacity, and numeracy (number line estimation skills and numerical discrimination) uniquely explained group membership. Children within each group differed in the extent of difficulties they exhibited, with numeracy variables differentially contributing to math ability in each group. Findings speak against a dimensional view of MLD and underscore the conceptual limitations of using basic numeracy performance to profile learning difficulties.

Cognitive Profiles of Children with Isolated and Comorbid Learning Difficulties in Reading and Math: a Meta-analysis

The causes underlying comorbid learning difficulties in reading (RD) and math (MD) are still a matter of debate. Based on current research, two models for the relation of the cognitive profile of isolated and combined learning difficulties (RDMD) are discussed. Regarding the “multi-deficit model”, the profile of RDMD is characterized by the sum of domain-specific core deficits of RD and MD (additivity) as well as shared domain-general risk factors of RD and MD resulting in less severe deficits than expected under additivity (under-additivity). The “three independent disorders model” explains RDMD as a distinct learning disorder, showing a separate cognitive profile with distinct and/or more severe deficits, compared to the sum of RD’s and MD’s profiles (over-additivity). To evaluate these approaches, a meta-analysis including 74 studies, examining children aged 6–12, was conducted. Separate group comparisons for the three subcomponents in the cognitive profiles—reading, math, executive functions (EF)—were considered. Linear hypothesis testing revealed different results regarding the three subcomponents of the cognitive profiles of children with isolated vs. combined learning difficulties: Whereas RDMDs’ deficits in reading and math represented the sum of the deficits in the isolated groups (additivity), there was some evidence that RDMDs’ deficits in EF skills corresponded to under-additivity. Furthermore, group differences in math skills were more pronounced in symbolic than in non-symbolic math tasks, whereas in reading, group differences were larger in phonological processing and reading than in rapid automatized naming and language skills. Results are discussed in terms of intervention options for RDMD.

[Cognitive disorders in children with dyscalculia]

OBJECTIVE

This study was to study the features of cognitive disorders in children with dyscalculia.

MATERIAL AND METHODS

The main study group included 48 children aged 8 to 10 years with manifestations of dyscalculia. The control group consisted of 30 children aged 8 to 10 years without manifestations of learning disabilities and other neuropsychiatric disorders. The following research methods were used in the work: the SNAP-IY scale for assessing concomitant manifestations of attention deficit hyperactivity disorder, the L.D. Malkova, «Working memory» technique for the quantitative assessment of working memory, TOVA computer test for the quantitative assessment of attention disorders and impulsiveness.

RESULTS

The study showed that only in 4 cases (8.3%) dyscalculia was of an isolated nature, without concomitant neuropsychiatric disorders. Most often, manifestations of attention deficit hyperactivity disorder (ADHD) were recorded in children with dyscalculia - 33 (68.8%) children and manifestations of other learning disorders (dyslexia - 27 (56.3%) children, dysgraphia - 22 (45.8%) children). In 20 (41.7%) cases, children in the study group had asthenic symptoms. When comparing the results of working memory testing in the study group, the number of correct answers was significantly lower than in the control group. Indicators of the TOVA psychophysiological test in children with dyscalculia showed a statistically significant increase in the number of inattention errors both in the first and second half of the test, compared with children from the control group.

CONCLUSION

Thus, dyscalculia should be considered not only as a disorder of arithmetic skills, but also as a disorder based on multiple cognitive dysfunctions, such as working memory dysfunction, dysfunction of attention.

Math difficulties in attention deficit hyperactivity disorder do not originate from the visual number sense

There is ample evidence from literature and clinical practice indicating mathematical difficulties in individuals with ADHD, even when there is no concomitant diagnosis of developmental dyscalculia. What factors underlie these difficulties is still an open question. Research on dyscalculia and neurotypical development suggests visual perception of numerosity (the number sense) as a building block for math learning. Participants with lower numerosity estimation thresholds (higher precision) are often those with higher math capabilities. Strangely, the role of numerosity perception in math skills in ADHD has been neglected, leaving open the question whether math difficulties in ADHD also originate from a deficitary visual number sense. In the current study we psychophysically measured numerosity thresholds and accuracy in a sample of children/adolescents with ADHD, but not concomitant dyscalculia (N = 20, 8-16 years). Math abilities were also measured by tasks indexing different mathematical competences. Numerosity performance and math scores were then compared to those obtained from an age-matched control group (N = 20). Bayesian statistics indicated no difference between ADHD and controls on numerosity perception, despite many of the symbolic math tasks being impaired in participants with ADHD. Moreover, the math deficits showed by the group with ADHD remained substantial even when numerosity thresholds were statistically regressed out. Overall, these results indicate that math difficulties in ADHD are unlikely to originate from an impaired visual number sense.

Heterogeneity of Dyscalculia Risk Dependent on the Type of Number Line Estimation Task and the Number Magnitude

An ability that is impaired in developmental dyscalculia (DD) is related to number line estimation (NLE). However, due to variability in NLE task performance, group differences do not exemplify the real difficulty level observed in the DD population. Thirty-two of the fifty-two participants posing dyscalculia risk (DR) (mean age = 9.88) experienced difficulties in mathematics. All the children performed two number-to-position tasks and two tasks requiring a verbal estimation of a number indicated on a line, utilizing the ranges 0–100 and 0–1000. The results showed that the estimation error in the verbal task was greater in the DR group than in the typically developed (TD) group for the 0–1000 range. In the number-to-position task, group differences were found for both ranges and the variability within both groups was smaller than it was in the verbal tasks. Analyses of each of the 26 numerical magnitudes revealed a more comprehensive pattern. The majority of the group effects were related to the 0–1000 line. Therefore, considerable data variability, especially in the DD group, suggests this issue must be analyzed carefully in the case of other mathematical capacities. It also critically questions some well-established phenomena and norms in experimental and diagnostic practices.

Discerning Developmental Dyscalculia and Neurodevelopmental Models of Numerical Cognition in a Disadvantaged Educational Context

Developmental Dyscalculia (DD) signifies a failure in representing quantities, which impairs the performance of basic math operations and schooling achievement during childhood. The lack of specificity in assessment measures and respective cut-offs are the most challenging factors to identify children with DD, particularly in disadvantaged educational contexts. This research is focused on a numerical cognition battery for children, designed to diagnose DD through 12 subtests. The aims of the present study were twofold: to examine the prevalence of DD in a country with generally low educational attainment, by comparing z-scores and percentiles, and to test three neurodevelopmental models of numerical cognition based on performance in this battery. Participants were 304 Brazilian school children aged 7–12 years of both sexes (143 girls), assessed by the Zareki-R. Performances on subtests and the total score increase with age without gender differences. The prevalence of DD was 4.6% using the fifth percentile and increased to 7.4% via z-score (in total 22 out of 304 children were diagnosed with DD). We suggest that a minus 1.5 standard deviation in the total score of the Zareki-R is a useful criterion in the clinical or educational context. Nevertheless, a percentile ≤ 5 seems more suitable for research purposes, especially in developing countries because the socioeconomic environment or/and educational background are strong confounder factors to diagnosis. The four-factor structure, based on von Aster and Shalev’s model of numerical cognition (Number Sense, Number Comprehension, Number Production and Calculation), was the best model, with significant correlations ranging from 0.89 to 0.97 at the 0.001 level.