PASS theory of intelligence and academic achievement: A meta-analytic review

Georgiou, George K.; Guo, Kan; Naveenkumar, Nithya; Vieira, Ana Paula Alves; Das, J. P.

doi:10.1016/j.intell.2020.101431

Cited by 20 publications

(13 citation statements)

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“…This theory of brain function provided a blueprint for test development, which excluded traditional, academically-laden subtests that demand knowledge (e.g., Vocabulary, Arithmetic) and allowed more equitable measurement. Although the evidence of validity and reliability as well as clinical utility of PASS scores is beyond the scope of this study, Naglieri and Otero (2017) have reported that PASS scores (a) are more predictive of achievement test scores than any other ability test (see also Georgiou et al, 2020, for recent evidence from a meta-analysis); (b) show distinctive profiles for different children with different disabilities; (c) can be used for specific learning disabilities eligibility determination consistent with Federal Law using the discrepancy consistency method of analyzing a pattern of strengths and weaknesses in PASS and achievement scores; (d) can be readily used for instructional planning and interventions and, perhaps most importantly for gifted students, (e) offer the most equitable way to measure diverse populations (Naglieri & Otero, 2017). GEORGIOU ET AL.…”

Section: The Pass Theory Of Intelligencementioning

confidence: 94%

“…Several studies have established that planning, attention, simultaneous, and successive (PASS) neurocognitive processes as measured by the cognitive assessment system (CAS; Naglieri & Das, 1997) are significant predictors of academic achievement across languages (e.g., English: Das et al, 2008;Greek: Papadopoulos, 2001;Chinese: Wang et al, 2012), ethnicities (e.g., Naglieri et al, 2007), and developmental levels (e.g., Naglieri & Rojahn, 2004). Importantly, Georgiou et al (2020) reported meta-analytic results showing correlations between PASS scores and reading or math that were significantly stronger than correlations reported in previous meta-analyses for other measures of intelligence. They concluded that "there are significant benefits if we conceptualize intelligence as a constellation of cognitive processes that are linked to the functional organization of the brain" (p. 10).…”

Section: Introductionmentioning

confidence: 99%

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Examining the relation between PASS cognitive processes and superior reading and mathematics performance

Georgiou

Bulut

Dunn

et al. 2020

Psychology in the Schools

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Although several studies have shown that planning, attention, simultaneous, and successive (PASS) cognitive processes-operationalized with the cognitive assessment system (CAS; Naglieri & Das, 1997)-are significant predictors of academic performance in the general population, little is known about their role among children with superior academic skills. Thus, the purpose of this study was to examine whether PASS processes can predict superior performance in reading and mathematics. We used the standardization sample of CAS (n = 1210) and further identified children with superior reading (n = 62) and mathematics (n = 73) performance on Woodcock-Johnson Tests of Achievement-Revised (Woodcock & Johnson, 1989). Results of the initial regression analyses showed that the PASS processes were significant predictors of superior reading and mathematics performance. Next, a classification and regression tree approach showed that the PASS scores could classify superior or not-superior readers and mathematicians with 89% and 82% accuracy, respectively. Theoretical and practical implications of our results are discussed.

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Section: The Pass Theory Of Intelligencementioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Examining the relation between PASS cognitive processes and superior reading and mathematics performance

Georgiou

Bulut

Dunn

et al. 2020

Psychology in the Schools

Self Cite

View full text Add to dashboard Cite

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“…A thorough assessment of the conditional background induced by those three analytical axes is of utmost importance when explaining students' AA. Cognitive ability has long been considered the most essential determinant of AA [11,12]. Not surprisingly, students' scores can be anticipated accurately from their Intelligence Quotient [13], despite the significant role that is left for other important factors [14].…”

Section: -Literature Reviewmentioning

confidence: 99%

Deep Learning in Predicting High School Grades: A Quantum Space of Representation

et al. 2022

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This paper applies deep learning to the prediction of Portuguese high school grades. A deep multilayer perceptron and a multiple linear regression implementation are undertaken. The objective is to demonstrate the adequacy of deep learning as a quantitative explanatory paradigm when compared with the classical econometrics approach. The results encompass point predictions, prediction intervals, variable gradients, and the impact of an increase in the class size on grades. Deep learning’s generalization error is lower in the student grade prediction, and its prediction intervals are more accurate. The deep multilayer perceptron gradient empirical distributions largely align with the regression coefficient estimates, indicating a satisfactory regression fit. Based on gradient discrepancies, a student’s mother being an employer does not seem to be a positive factor. A benign paradigm shift concerning the balance between home and career affairs for both genders should be reinforced. The deep multilayer perceptron broadens the spectrum of possibilities, providing a quantum solution hinged on a universal approximator. In the case of an academic achievement-critical factor such as class size, where the literature is neither unanimous on its importance nor its direction, the multilayer perceptron formed three distinct clusters per the individual gradient signals. Doi: 10.28991/ESJ-2022-SIED-012 Full Text: PDF

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“…The literature has extensively confirmed the student's cognitive ability as the main determinant of AA [10,11]. However, on average, it leaves unexplained 51-75% of the total variance [12].…”

Section: -Literature Reviewmentioning

confidence: 99%

Machine Learning Bias in Predicting High School Grades: A Knowledge Perspective

et al. 2021

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This study focuses on the machine learning bias when predicting teacher grades. The experimental phase consists of predicting the student grades of 11th and 12thgrade Portuguese high school grades and computing the bias and variance decomposition. In the base implementation, only the academic achievement critical factors are considered. In the second implementation, the preceding year’s grade is appended as an input variable. The machine learning algorithms in use are random forest, support vector machine, and extreme boosting machine. The reasons behind the poor performance of the machine learning algorithms are either the input space poor preciseness or the lack of a sound record of student performance. We introduce the new concept of knowledge bias and a new predictive model classification. Precision education would reduce bias by providing low-bias intensive-knowledge models. To avoid bias, it is not necessary to add knowledge to the input space. Low-bias extensive-knowledge models are achievable simply by appending the student’s earlier performance record to the model. The low-bias intensive-knowledge learning models promoted by precision education are suited to designing new policies and actions toward academic attainments. If the aim is solely prediction, deciding for a low bias knowledge-extensive model can be appropriate and correct. Doi: 10.28991/esj-2021-01298 Full Text: PDF

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PASS theory of intelligence and academic achievement: A meta-analytic review

Cited by 20 publications

References 56 publications

Examining the relation between PASS cognitive processes and superior reading and mathematics performance

Examining the relation between PASS cognitive processes and superior reading and mathematics performance

Deep Learning in Predicting High School Grades: A Quantum Space of Representation

Machine Learning Bias in Predicting High School Grades: A Knowledge Perspective

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