Academic achievement over a decade: A neuropsychological prediction study

Clarren, Sandra Bernstein; Martin, Donald C.; Townes, Brenda D.

doi:10.1080/87565649309540550

Cited by 8 publications

(10 citation statements)

References 23 publications

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“…Neuropsychological variables have been found to predict later math achievement at least as well, if not more accurately, than IQ scores alone; and when added to IQ scores, they contribute to the accuracy of such predictions (Clarren, Martin, & Townes, 1993). Neuropsychological features associated with poor math performance include (but are not limited to) poor reading (Badian, 1983;Donlan & Hutt, 1991;) and attentional or executive function skills (Geary, 1990).…”

Section: Core Deficits and MD Subtypesmentioning

confidence: 97%

Complexities in identifying and defining mathematics learning disability in the primary school-age years

2003

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This paper is a descriptive report of findings from a prospective longitudinal study of math disability (MD). The study was designed to address the incidence of MD during primary school, the utility of different MD definitions, and evidence of MD subtypes. The results illustrate the dynamic properties of psychometrically derived definitions of MD. Different groups of children meet criteria for MD depending on which measure(s) are used for identification. Over time, a given individual may not continue to meet MD criteria, even when using the same assessments. Thus, the findings lead to cautions regarding the single-tool/ one-time assessment for a clinical diagnosis of MD. Twenty-two of 209 participants demonstrated "persistent MD" (MD-p), or MD for more than one school grade. Reading disability was relatively more frequent in this MD-p subgroup than in the remaining participants (25 percent vs. 7 percent). Reading-related skills were correlated with math achievement, as were select visual spatial skills. There was minimal overlap between groups who met either a "poor achievement" criteria or an "IQ-achievement discrepancy," and the latter was far less stable a measure over time than the former. The results highlight the complexities of defining MD and illustrate the need for more research in this area.

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Section: Core Deficits and MD Subtypesmentioning

confidence: 97%

Complexities in identifying and defining mathematics learning disability in the primary school-age years

2003

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“…It is unclear to what extent cognitive correlates will inform us of the deficits that underlie MLD, but we believe that it is important to pursue this line of work in view of the replication of these findings across studies with different criteria for MLD and different study populations. Clarren, Martin, and Townes (1993) demonstrated that different combinations of items from a neuropsychological battery were accurate at predicting math versus reading achievement over the longer term. It remains to be seen how well our identified skills predict math achievement in middle school or beyond.…”

Section: Discussionmentioning

confidence: 99%

“…Clarren, Martin, and Townes (1993) demonstrated that different combinations of items from a neuropsychological battery were accurate at predicting math versus reading achievement over the longer term. It remains to be seen how well our identified skills predict math achievement in middle school or beyond.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, the ability to read numerals at age 6 years was more strongly correlated with performance on a draw-a-person task than with the neuropsychological screen, although correlations with both measures were statistically significant. Looking at long-term predictors, Clarren, Martin, and Townes (1993) reported that kindergarten performance can successfully predict academic achievement outcome 10 years later, and that prediction accuracy is enhanced by a comprehensive IQ-neuropsychological testing combination approach. Although their findings support the notion that early prediction of MLD is possible, the question that remains is how to make early prediction feasible.…”

Section: Predicting Poor Math Achievementmentioning

confidence: 99%

“…Even young children do show an association between reading and math skills, in terms of brain activation patterns (Key et al, 2004), and as seen in correlations between RAN and TEMA-2 composite scores (Mazzocco & Myers, 2003) and phonological processing and mathematical computation (Hecht et al, 2001). It is unclear to what extent cognitive correlates will inform us of the deficits that underlie MLD, but we believe that it is important to pursue this line of work in view of the replication of these findings across studies with different criteria for MLD and different study populations.Clarren, Martin, and Townes (1993) demonstrated that different combinations of items from a neuropsychological battery were accurate at predicting math versus reading achievement over the longer term. It remains to be seen how well our identified skills predict math achievement in middle school or beyond.…”

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confidence: 99%

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Kindergarten Predictors of Math Learning Disability

Mazzocco

Thompson

2005

Learning Disabil Res Pract

228

169

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The aim of the present study was to address how to effectively predict mathematics learning disability (MLD). Specifically, we addressed whether cognitive data obtained during kindergarten can effectively predict which children will have MLD in third grade, whether an abbreviated test battery could be as effective as a standard psychoeducational assessment at predicting MLD, and whether the abbreviated battery corresponded to the literature on MLD characteristics. Participants were 226 children who enrolled in a 4-year prospective longitudinal study during kindergarten. We administered measures of mathematics achievement, formal and informal mathematics ability, visual-spatial reasoning, and rapid automatized naming and examined which test scores and test items from kindergarten best predicted MLD at grades 2 and 3. Statistical models using standardized scores from the entire test battery correctly classified ~80-83 percent of the participants as having, or not having, MLD. Regression models using scores from only individual test items were less predictive than models containing the standard scores, except for models using a specific subset of test items that dealt with reading numerals, number constancy, magnitude judgments of one-digit numbers, or mental addition of one-digit numbers. These models were as accurate in predicting MLD as was the model including the entire set of standard scores from the battery of tests examined. Our findings indicate that it is possible to effectively predict which kindergartners are at risk for MLD, and thus the findings have implications for early screening of MLD.The primary aim of the present study was to examine whether cognitive data obtained from psychoeducational or neuropsychological assessments of kindergarteners can effectively predict math achievement outcome in third grade. Although this study of predictors was not a screening study per se, the findings from the present study may have direct implications for early screening of risk for math learning disability (MLD). The findings may also contribute toward efforts to identify core deficits that underlie MLD. Relative to knowledge about reading disability (RD), there is less known about the core deficit(s) of mathematics disability, and there are few evidence-based screening tools available for detecting risk for poor achievement in mathematics. The proposed project was designed to help address these gaps in researchbased practice. The Need for Early Identification of MLDIt is important to identify risk for MLD, because-like poor reading achievement-poor math achievement is a risk factor for negative outcomes in both childhood and adulthood. Education level achieved in adulthood is associated with math performance level (Delazer, Girelli, Grana, Requests for reprints should be sent to Michèle Mazzocco, KKI West Campus, 3825 Greenspring Avenue, Painter Building, Top Floor, Baltimore, MD 21211. Electronic inquiries may be send to mazzocco@kennedykrieger.org. NIH Public Access Author ManuscriptLearn Disabil Res ...

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