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The ReadFree tool for the identification of poor readers: a validation study based on a machine learning approach in monolingual and minority-language children
Annals of Dyslexia ( IF 2.275 ) Pub Date : 2023-08-07 , DOI: 10.1007/s11881-023-00287-3
Desiré Carioti 1, 2 , Natale Adolfo Stucchi 2 , Carlo Toneatto 2 , Marta Franca Masia 1 , Milena Del Monte 1, 3 , Silvia Stefanelli 1, 4 , Simona Travellini 1, 3 , Antonella Marcelli 3 , Marco Tettamanti 2 , Mirta Vernice 1 , Maria Teresa Guasti 2 , Manuela Berlingeri 1, 3, 5
Affiliation  

In this study, we validated the “ReadFree tool”, a computerised battery of 12 visual and auditory tasks developed to identify poor readers also in minority-language children (MLC). We tested the task-specific discriminant power on 142 Italian-monolingual participants (8–13 years old) divided into monolingual poor readers (N = 37) and good readers (N = 105) according to standardised Italian reading tests. The performances at the discriminant tasks of the “ReadFree tool” were entered into a classification and regression tree (CART) model to identify monolingual poor and good readers. The set of classification rules extracted from the CART model were applied to the MLC’s performance and the ensuing classification was compared to the one based on standardised Italian reading tests. According to the CART model, auditory go-no/go (regular), RAN and Entrainment100bpm were the most discriminant tasks. When compared with the clinical classification, the CART model accuracy was 86% for the monolinguals and 76% for the MLC. Executive functions and timing skills turned out to have a relevant role in reading. Results of the CART model on MLC support the idea that ad hoc standardised tasks that go beyond reading are needed.



中文翻译:

用于识别阅读能力较差的 ReadFree 工具:基于机器学习方法对单语和少数民族语言儿童进行的验证研究

在这项研究中,我们验证了“ ReadFree 工具”,这是一个包含 12 项视觉和听觉任务的计算机化工具,旨在识别少数民族语言儿童 (MLC) 的阅读能力较差的人。我们根据标准化意大利语阅读测试,将 142 名意大利语单语参与者(8-13 岁)分为单语阅读能力较差(N = 37)和良好阅读能力(N = 105),测试了任务特定判别力。“ReadFree 工具”在判别任务中的表现被输入到分类和回归树(CART)模型中,以识别单语较差和良好的读者。从 CART 模型中提取的一组分类规则应用于 MLC 的性能,并将随后的分类与基于标准化意大利语阅读测试的分类进行比较。根据 CART 模型,听觉 go-no/go(常规)、RAN 和 Entrainment 100bpm是最具辨别力的任务。与临床分类相比,单语者的 CART 模型准确率为 86%,MLC 为 76%。事实证明,执行功能和计时技巧在阅读中发挥着重要作用。MLC 上的 CART 模型的结果支持这样的想法:需要超出阅读范围的临时标准化任务。

更新日期:2023-08-07
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