Using Machine Learning for Identification: Development of the Cognitive Assessment Battery for Twice Exceptionality

IF 4 3区教育学 Q1 EDUCATION, SPECIAL

Gifted Child Quarterly Pub Date : 2025-12-18 DOI:10.1177/00169862251394277

Furkan Atmaca, Mustafa Baloğlu

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引用次数: 0

Abstract

The identification of twice-exceptional (2e) students is a complex challenge, primarily due to cognitive masking. In this study, we developed the Cognitive Assessment Battery for Twice Exceptionality (2eCAB) and evaluated the classification performance of a machine learning algorithm, specifically Classification and Regression Trees (CART). Grounded in prior literature, the 2eCAB was designed to assess nonverbal ability, memory, rapid automatized naming, and pseudoword reading. The sample included 565 Turkish-speaking elementary students: typically developing (TD, n = 468), gifted ( n = 44), 2e ( n = 15), and students with specific learning disabilities (SLD, n = 38). The results indicated that the 2eCAB is a valid and reliable tool. Internal consistency of the battery was high (α = .95, ω = .95). Test–retest reliability for total scores was .92, while individual task scores ranged between .77 and .92. Significant relationships were found between 2eCAB scores, and results from hierarchical confirmatory factor analysis showed a good model fit. Scores from four external assessments measuring nonverbal ability, working memory, naming speed, and reading were significantly correlated with 2eCAB scores. The trained CART algorithm achieved an acceptable overall classification accuracy for identifying 2e, gifted, TD, and SLD students. Thus, artificial intelligence technologies show promise for the identification of students with special needs.

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使用机器学习进行识别：两次异常的认知评估电池的发展

双重例外（2e）学生的识别是一项复杂的挑战，主要是由于认知掩蔽。在本研究中，我们开发了两次异常性认知评估电池（2eCAB），并评估了机器学习算法的分类性能，特别是分类和回归树（CART）。在先前文献的基础上，2eCAB被设计用来评估非语言能力、记忆、快速自动命名和假词阅读。样本包括565名说土耳其语的小学生：典型发展（TD, n = 468），天才（n = 44），天才（n = 15）和有特殊学习障碍（SLD, n = 38）的学生。结果表明，2eCAB是一种有效、可靠的检测工具。电池内部一致性高（α = 0.95, ω = 0.95）。总分重测信度为。92分，而单项任务得分在。77和。92。2eCAB评分之间存在显著关系，分层验证性因子分析结果显示模型拟合良好。非语言能力、工作记忆、命名速度和阅读四项外部评估的得分与2eCAB得分显著相关。经过训练的CART算法在识别2e、gifted、TD和SLD学生方面取得了可接受的总体分类精度。因此，人工智能技术有望用于识别有特殊需求的学生。

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来源期刊

Gifted Child Quarterly Multiple-

CiteScore

6.30

自引率

29.00%

发文量

期刊介绍： Gifted Child Quarterly (GCQ) is the official journal of the National Association for Gifted Children. As a leading journal in the field, GCQ publishes original scholarly reviews of the literature and quantitative or qualitative research studies. GCQ welcomes manuscripts offering new or creative insights about giftedness and talent development in the context of the school, the home, and the wider society. Manuscripts that explore policy and policy implications are also welcome. Additionally, GCQ reviews selected books relevant to the field, with an emphasis on scholarly texts or text with policy implications, and publishes reviews, essay reviews, and critiques.