Suppr超能文献

使用基本计算能力测试(FCAT)预测数学学习困难

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

作者信息

Ohba Sawako, Koeda Tatsuya, Oguri Masayoshi, Okanishi Tohru, Maegaki Yoshihiro

机构信息

Work-Life Balance Support Center, Tottori University Hospital, Yonago 683-8504, Japan.

Department of Psychosocial Medicine, National Center for Child Health and Development, Setagaya, Tokyo 157-8535, Japan.

出版信息

Yonago Acta Med. 2022 Aug 29;65(3):238-243. doi: 10.33160/yam.2022.08.010. eCollection 2022 Aug.

Abstract

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, < 0.001) and MCBT-2 scores (regression coefficient: 0.50, < 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.

摘要

背景

学龄期的数学学习困难(MLD)由多种因素导致,包括计算障碍。传统的计算障碍诊断测试耗时较长,且需要专业技能人员。这项前瞻性队列研究旨在表明,一年级时进行的耗时较短的基本计算能力测试(FCAT)能够预测二年级(FCAT测试后1.2年)基于课程的数学测试所衡量的数学学业成绩。

方法

共有362名日本一、二年级儿童参与。对一年级学生(平均年龄:7.1岁)进行了一项测量基本计算能力的新快速测试,即FCAT,包括序数、基数、加法和减法。在一、二年级学生的测试期间[鸟取县基于数学课程的测试(MCBT)]测量数学学业成绩,一年级为(MCBT - 1,平均年龄:7.3岁),二年级为(MCBT - 2,平均年龄:8.3岁)。我们使用单变量回归分析分析了FCAT与MCBT - 1和2之间的关联,并使用评分操作曲线和尤登指数确定了MCBT - 2时数学学习困难(MLD)的临界值。MLD被设定为MCBT得分低于20%。

结果

FCAT得分与MCBT - 1(回归系数:0.67,<0.001)和MCBT - 2得分(回归系数:0.50,<0.001)显著相关。FCAT得分的临界值为47分(偏差得分:47)时可预测MCBT - 2时的MLD(敏感性:0.77,特异性:0.73)。对于62名MCBT - 1得分存在MLD的参与者,FCAT得分低于40分(偏差得分:35)的临界值时,在MCBT - 2时存在MLD的风险较高(优势比:6.2)。

结论

FCAT可在常规学校短时间内轻松进行,并能预测数学学业成绩。它可用于早期诊断有数学问题的儿童。

相似文献

2
Building Knowledge Structures by Testing Helps Children With Mathematical Learning Difficulty.
J Learn Disabil. 2016 Mar-Apr;49(2):166-75. doi: 10.1177/0022219414538515. Epub 2014 Jun 19.

本文引用的文献

2
The Diagnosis and Treatment of Dyscalculia.计算障碍的诊断与治疗。
Dtsch Arztebl Int. 2019 Feb 15;116(7):107-114. doi: 10.3238/arztebl.2019.0107.
4
Dyscalculia from a developmental and differential perspective.从发展和差异视角看计算障碍
Front Psychol. 2013 Aug 21;4:516. doi: 10.3389/fpsyg.2013.00516. eCollection 2013.
5
Pathways to mathematics: longitudinal predictors of performance.通向数学之路:表现的纵向预测因素。
Child Dev. 2010 Nov-Dec;81(6):1753-67. doi: 10.1111/j.1467-8624.2010.01508.x.
7
Mathematics and learning disabilities.数学与学习障碍。
J Learn Disabil. 2004 Jan-Feb;37(1):4-15. doi: 10.1177/00222194040370010201.
9
Theory-based assessment of acquired dyscalculia.
Brain Cogn. 1991 Nov;17(2):285-308. doi: 10.1016/0278-2626(91)90078-m.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验