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

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 (alpha = .95, omega = .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.