Identification of children with mathematics learning disabilities (MLDs) using latent class growth analysis.
Latent growth modeling can split math-learning-disabled kids into two groups—those with core number deficits and those slowed by low SES—so tailor interventions accordingly.
01Research in Context
What this study did
The team used latent class growth analysis on math scores. This method groups kids whose math skills grow at the same pace.
They looked at a Chinese elementary sample. The goal was to see if hidden sub-types of math learning disability exist.
What they found
Two low-achieving classes appeared. One class had weak number sense across tasks. The other class had low family income but normal thinking skills.
The finding shows math trouble can stem from different roots: core numbers or life context.
How this fits with other research
MacFarland et al. (2025) extend this work backward. They show kindergarten symbolic comparison and working memory predict second-grade MLD. You can spot risk even earlier.
Brugnaro et al. (2024) extend forward. Their online screener adds arithmetic-fluency probes. It sharpens MLD identification in grades 3-9.
Ceulemans et al. (2014) seem to clash. They found no enumeration deficit in adolescents with MLD. The gap is age. Early number problems may fade or hide as kids grow and learn tricks.
Desoete et al. (2013) foreshadow the SES link. Siblings of MLD children already show weak number-line skills, hinting that family risk shapes one group.
Why it matters
You now have a quick way to sort math strugglers. Give symbolic comparison and working-memory probes in K-1. If scores are low, check number sense next. If number sense is fine, look at home context and provide broader support. Match the intervention to the class: number drills for the core-deficit group, and family or language support for the SES group.
Want CEUs on This Topic?
The ABA Clubhouse has 60+ free CEUs — live every Wednesday. Ethics, supervision & clinical topics.
Join Free →Add a one-minute symbolic comparison probe to your math screening tool and track which low scorers also fail number-line tasks.
02At a glance
03Original abstract
The traditional way of identifying children with mathematics learning disabilities (MLDs) using the low-achievement method with one-off assessment suffers from several limitations (e.g., arbitrary cutoff, measurement error, lacking consideration of growth). The present study attempted to identify children with MLD using the latent growth modelling approach, which minimizes the above potential problems. Two hundred and ten Chinese-speaking children were classified into five classes based on their arithmetic performance over 3 years. Their performance on various number-related cognitive measures was also assessed. A potential MLD class was identified, which demonstrated poor achievement over the 3 years and showed smaller improvement over time compared with the average-achieving class. This class had deficits in all number-related cognitive skills, hence supporting the number sense deficit hypothesis. On the other hand, another low-achieving class, which showed little improvement in arithmetic skills over time, was also identified. This class had an average cognitive profile but a low SES. Interventions should be provided to both low-achieving classes according to their needs.
Research in developmental disabilities, 2014 · doi:10.1016/j.ridd.2014.07.015