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Predicting Mathematical Learning Difficulties Using Fundamental Calculative Ability Test (FCAT)

Ohba, Sawako 小枝 達也 大栗 聖由 岡西 徹 前垣 義弘 鳥取大学 DOI:10.33160/yam.2022.08.010

2022.08.29

概要

Background: Mathematical learning difficulty (MLD) during school years results from several factors, including dyscalculia. Traditional diagnostic tests for dyscalculia are time intensive and require skilled specialists. This prospective cohort study aimed to reveal that the less time intensive Fundamental Calculative Ability Test (FCAT), administered in first grade, can predict the outcome of mathematical school achievement, which was measured with the curriculum-based mathematical test for second grade (1.2 years after FCAT). Methods: A total of 362 Japanese first- and second-grade children participated. A new quick test measuring fundamental calculative abilities, the FCAT, ordinal, radix, addition, and subtraction, was conducted for the first graders (mean age: 7.1 years). Mathematical school achievement was measured during the tests [mathematics curriculum-based test in Tottori Prefecture (MCBT)] for first (MCBT-1, mean age: 7.3 years) and second graders (MCBT-2, mean age: 8.3 years). We analyzed the associations between FCAT and MCBT-1 and 2 using univariate regression analysis, and cutoff values for mathematical learning difficulty (MLD) at MCBT-2 using the rating operation curve and Youden index. MLD was set as a score of lower than 20% on the MCBT. Results: The FCAT score was significantly associated with the MCBT-1 (regression coefficient: 0.67, P < 0.001) and MCBT-2 scores (regression coefficient: 0.50, P < 0.001). A cutoff value of 47 points (deviation score: 47) at the FCAT score predicted MLD at MCBT-2 (sensitivity: 0.77, specificity: 0.73). For 62 participants with MLD at MCBT-1 score, FCAT scores below the cutoff value of 40 points (deviation score: 35) were at high risk of MLD at MCBT-2 (odds ratio: 6.2). Conclusion: The FCAT is easily conducted in a short time during regular schools and can predict mathematical school achievement. It can be used for the early diagnosis of children with mathematical problems.

参考文献

1 .American Psychiatric Association. Diagnostic and statisti- cal manual of mental disorders. 5th ed. DOI: 10.1176/appi. books.9780890425596.

2 Sella F, Tressoldi P, Lucangeli D, Zorzi M. Training numerical skills with the adaptive videogame “The Number Race”: A randomized controlled trial on preschoolers. Trends Neurosci Educ. 2016;5:20-9. DOI: 10.1016/j.tine.2016.02.002

3 Schwenk C, Sasanguie D, Kuhn JT, Kempe S, Doebler P, Holling H. (Non-)symbolic magnitude processing in children with mathematical difficulties: A meta-analysis. Res Dev Disabil. 2017;64:152-67. DOI: 10.1016/j.ridd.2017.03.003, PMID: 28432933

4 .Haberstroh S, Schulte-Körne G. The Diagnosis and Treat- ment of Dyscalculia. tsch Arztebl Int. 2019;116:107-14. DOI: 10.3238/arztebl.2019.0107

5 Butterworth B. Dyscalculia screener: highlighting children with specific learning difficulties in maths. London: NFER- Nelson Publishing Company Limited; 2003.

6 Von Aster M, Weinhold Zulauf M, Horn R. Neuropsycholo- gische Testbatterie für Zahlenverarbeitung und Rechnen bei Kindern (ZAREKI-R) [Neuropsychological test battery of number processing and calculation in children]. Frankfurt am Main, Germany: Harcourt Publishers Test Services; 2006. German.

7 Jordan NC, Glutting JJ, Dyson N. Number sense screener™(NSS™) User’s guide, k-1, research edition. Baltimore: Brookes Publishing; 2012.

8 Peters L, Ansari D. Are specific learning disorders truly specific, and are they disorders? Trends Neurosci Educ. 2019;17:100115. DOI: 10.1016/j.tine.2019.100115, PMID: 31685130

9 Inagaki M, Yoneda R. Specific learning disorder: from a viewpoint of medicine. J Child Adolesc Psychiatry. 2017;58:205-16. DOI: 10.20615/jscap.58.2_205 Japanese with English abstract.

10 LeFevre JA, Fast L, Skwarchuk SL, Smith-Chant BL, Bisanz J, Kamawar D, et al. Pathways to mathematics: longitudinal predictors of performance. Child Dev. 2010;81:1753-67. DOI: 10.1111/j.1467-8624.2010.01508.x, PMID: 21077862

11 Krajewski K, Schneider W. Exploring the impact of pho- nological awareness, visual–spatial working memory, and preschool quantity–number competencies on mathematics achievement in elementary school: findings from a 3-year longitudinal study. J Exp Child Psychol. 2009;103:516-31. DOI: 10.1016/j.jecp.2009.03.009, PMID: 19427646

12 Ohba S, Koeda T, Maegaki Y. A Numerical Fundamentals Test for the early detection of dyscalculia. Psychiatr Neruro- logia Paediatr Jpn. 2019–2020;59:199-206. DOI: 10.24782/ jsppn.59.2_199 Japanese.

13 Geary DC. Mathematics and learning disabilities. J Learn Disabil. 2004;37:4-15. DOI: 10.1177/00222194040370010201, PMID: 15493463

14 McCloskey M, Caramazza A, Basili A. Cognitive mecha- nisms in number processing and calculation: evidence from dyscalculia. Brain Cogn. 1985;4:171-96. DOI: 10.1016/0278- 2626(85)90069-7, PMID: 2409994

15 McCloskey M, Aliminosa D, Macaruso P. Theory-based assessment of acquired dyscalculia. Brain Cogn. 1991;17:285- 308. DOI: 10.1016/0278-2626(91)90078-M, PMID: 1799455

16 Räsänen P, Aunio P, Laine A, Hakkarainen A, Väisänen E, Finell J, et al. Effects of gender on basic numerical and arith- metic skills: pilot data from third to ninth grade for a large- scale online dyscalculia screener. Frontiers in Education. 2021;6:683672. DOI: 10.3389/feduc.2021.683672

17 Price G, Ansari D. Dyscalculia: characteristics, causes, and treatments. Numeracy. 2013;6:2. DOI: 10.5038/1936- 4660.6.1.2

18 Kaufmann L, Mazzocco MM, Dowker A, von Aster M, Göbel SM, Grabner RH, et al. Dyscalculia from a develop- mental and differential perspective. Front Psychol. 2013;4:516. DOI: 10.3389/fpsyg.2013.00516, PMID: 23970870

19 Grigore M. Towards a standard diagnostic tool for dyscalculia in school children. Core Proceedings. 2020;1-16.

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