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在教育资源匮乏的背景下识别发育性计算障碍和数字认知的神经发育模型

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

作者信息

Santos Flavia H, Ribeiro Fabiana S, Dias-Piovezana Ana Luiza, Primi Caterina, Dowker Ann, von Aster Michael

机构信息

Affective, Behavioural and Cognitive Neuroscience, School of Psychology, University College Dublin, D04 V1W8 Dublin, Ireland.

Department of Social Sciences, Faculty of Humanities, Education and Social Sciences, University of Luxembourg, L-4366 Esch-Sur-Alzette, Luxembourg.

出版信息

Brain Sci. 2022 May 16;12(5):653. doi: 10.3390/brainsci12050653.

Abstract

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.

摘要

发育性计算障碍(DD)意味着在数量表征方面存在缺陷,这会损害儿童时期基本数学运算的表现和学业成绩。评估方法缺乏特异性以及相应的临界值是识别患有DD的儿童最具挑战性的因素,尤其是在教育条件较差的环境中。本研究聚焦于一种针对儿童的数字认知测试组合,旨在通过12个分测验诊断DD。本研究的目的有两个:通过比较z分数和百分位数,考察一个教育水平普遍较低的国家中DD的患病率,并基于该测试组合的表现检验三种数字认知的神经发育模型。参与者为304名7至12岁的巴西在校儿童,男女皆有(143名女孩),通过Zareki - R进行评估。分测验成绩和总分随年龄增长而提高,且无性别差异。使用第五百分位数时,DD的患病率为4.6%,通过z分数计算则增至7.4%(304名儿童中共有22名被诊断为DD)。我们建议,在临床或教育背景下,Zareki - R总分减去1.5个标准差是一个有用的标准。然而,百分位数≤5似乎更适合研究目的,特别是在发展中国家,因为社会经济环境或/和教育背景是诊断的强大混杂因素。基于冯·阿斯特和沙莱夫数字认知模型(数字感、数字理解、数字生成和计算)的四因素结构是最佳模型,在0.001水平上显著相关性范围为0.89至0.97。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf7e/9139865/e65522ab33f1/brainsci-12-00653-g001.jpg

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