Wechsler Individual Achievement Test–Third Edition

Dumont, Ron; Willis, John O.; Veizel, Kathleen; Zibulsky, Jamie

doi:10.1002/9781118660584.ese2521

Cited by 27 publications

(4 citation statements)

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“…The 25th percentile was selected as the outcome variable for two reasons. First, from a normative perspective, the 25th percentile typically represents the division between typical (i.e., average) performance and below average performance on norm-referenced measures of academic achievement (e.g., Wechsler Individual Achievement Test, Third Edition [29]).…”

Section: Discussionmentioning

confidence: 99%

Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach

2022

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Traditional screening approaches identify students who might be at risk for academic problems based on how they perform on a single screening measure. However, using multiple screening measures may improve accuracy when identifying at-risk students. The advent of machine learning algorithms has allowed researchers to consider using advanced predictive models to identify at-risk students. The purpose of this study is to investigate if machine learning algorithms can strengthen the accuracy of predictions made from progress monitoring data to classify students as at risk for low mathematics performance. This study used a sample of first-grade students who completed a series of computerized formative assessments (Star Math, Star Reading, and Star Early Literacy) during the 2016–2017 (n = 45,478) and 2017–2018 (n = 45,501) school years. Predictive models using two machine learning algorithms (i.e., Random Forest and LogitBoost) were constructed to identify students at risk for low mathematics performance. The classification results were evaluated using evaluation metrics of accuracy, sensitivity, specificity, F1, and Matthews correlation coefficient. Across the five metrics, a multi-measure screening procedure involving mathematics, reading, and early literacy scores generally outperformed single-measure approaches relying solely on mathematics scores. These findings suggest that educators may be able to use a cluster of measures administered once at the beginning of the school year to screen their first grade for at-risk math performance.

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Section: Discussionmentioning

confidence: 99%

Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach

2022

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“…Average split-half reliability coefficients for subtests equaled or exceeded 0.83 across ages except for alphabet writing fluency, which was 0.69 in the standardization sample. Average split-half reliability coefficients for composites ranged from 0.91 to 0.98 across ages (Pearson, 2009). The WIAT-III composites have been shown to correlate strongly with the composite scores from the second edition of the test, suggesting that the composites are measuring similar constructs.…”

Section: Wiat-iiimentioning

confidence: 96%

“…Achievement scores were agestandardized with a mean of 100 and a standard deviation of 15, resulting in standard scores that could be compared across tests. For participants who originally met inclusion criteria (N = 89), most reports included achievement scores from the Wechsler Individual Achievement Test (WIAT-III; Pearson, 2009) Records from four students (4.5%) did not contain information about the achievement test used. Proportions were similar for participants who were used to address the research questions (N = 63), with 78.69% of the participants reporting WIAT-III data (N = 48), 4.92% reporting WJ-IV data (N = 3), 6.56% reporting KTEA-3 data (N = 4), and 9.84% reporting multiple tests (N = 6).…”

Section: Academic Achievementmentioning

confidence: 99%

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Specific Learning Disability Identification in an RtI Method: Do Measures of Cognitive Ability Matter?

Hajovsky¹,

Maki

Chesnut

et al. 2022

Learn Disabil Res Pract

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This study examined the extent to which cognitive ability index scores predicted multidisciplinary teams’ (MDT) SLD identification within a response‐to‐intervention (RtI) method after accounting for RtI slope and norm‐referenced achievement scores. Results showed that four achievement composite scores (i.e., basic reading, reading comprehension, math computation, and math problem solving) and two cognitive ability index scores (i.e., crystallized ability, working memory) predicted MDT‐determined SLD, explaining 81% of the variance. The inclusion of academic achievement and cognitive ability index scores predicted MDT‐determined SLD with 90% accuracy; cognitive ability index scores only increased specificity (sensitivity = 95%; specificity = 79%). RtI slope did not predict MDT‐determined SLD, which was a required component of the evaluation.

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Predictive ability of the General Ability Index (GAI) versus the Full Scale IQ among gifted referrals.

Rowe

Kingsley

Thompson

2010

School Psychology Quarterly

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The General Ability Index (GAI) is a composite ability score for the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) that minimizes the impact of tasks involving working memory and processing speed. The goal of the current study was to compare the degree to which the Full Scale IQ (FSIQ) and the GAI predict academic achievement in math and reading among a group of 88 children tested for gifted programming. All students had significant variability among their index scores on the WISC-IV. Whereas both the FSIQ and GAI significantly predicted standardized achievement test scores in reading and math, the FSIQ explained more of the variance. In sequential regression analyses, both working memory and verbal comprehension scores explained significant, unique variance in reading and math scores. However, measures of processing speed and perceptual reasoning did not account for significant amounts of variance in achievement scores over and above measures of working memory and verbal comprehension. The inclusion of working memory scores in calculation of the FSIQ appears to account for the difference in prediction between the FSIQ and the GAI.

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Wechsler Individual Achievement Test–Third Edition

Cited by 27 publications

References 0 publications

Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach

Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach

Specific Learning Disability Identification in an RtI Method: Do Measures of Cognitive Ability Matter?

Predictive ability of the General Ability Index (GAI) versus the Full Scale IQ among gifted referrals.

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