Limits...
The Prediction of Students' Academic Performance With Fluid Intelligence in Giving Special Consideration to the Contribution of Learning.

Ren X, Schweizer K, Wang T, Xu F - Adv Cogn Psychol (2015)

Bottom Line: The fluid intelligence data were decomposed into a learning component that was associated with the position effect of intelligence items and a constant component that was independent of the position effect.Results showed that the learning component contributed significantly more to the prediction of math and verbal performance than the constant component.Furthermore, the results were in line with the expectation that learning was a predictor of performance in school.

View Article: PubMed Central - PubMed

Affiliation: School of Education, Huazhong University of Science & Technology, Wuhan 430074, China ; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing 100875, China.

ABSTRACT
The present study provides a new account of how fluid intelligence influences academic performance. In this account a complex learning component of fluid intelligence tests is proposed to play a major role in predicting academic performance. A sample of 2, 277 secondary school students completed two reasoning tests that were assumed to represent fluid intelligence and standardized math and verbal tests assessing academic performance. The fluid intelligence data were decomposed into a learning component that was associated with the position effect of intelligence items and a constant component that was independent of the position effect. Results showed that the learning component contributed significantly more to the prediction of math and verbal performance than the constant component. The link from the learning component to math performance was especially strong. These results indicated that fluid intelligence, which has so far been considered as homogeneous, could be decomposed in such a way that the resulting components showed different properties and contributed differently to the prediction of academic performance. Furthermore, the results were in line with the expectation that learning was a predictor of performance in school.

No MeSH data available.


An illustration of the measurement model including the constant and positioncomponents of reasoning as two independent latent variables and theindividual items of each reasoning test as manifest variables (the model ofthe figure reasoning includes 19 manifest variables, and the model of thenumerical reasoning includes 22 manifest variables).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4591514&req=5

Figure 2: An illustration of the measurement model including the constant and positioncomponents of reasoning as two independent latent variables and theindividual items of each reasoning test as manifest variables (the model ofthe figure reasoning includes 19 manifest variables, and the model of thenumerical reasoning includes 22 manifest variables).

Mentions: The representation of the position effect for each reasoning test required afixed-links model including two latent variables: the constant component and theposition component. Figure 2 illustrates themeasurement model including the constant and position components of reasoning andthe individual items of each reasoning test serving as manifest variables. Theloadings of the constant component were kept constant since this component wasindependent of item positions and contributed almost equally to all individualitems. The loadings of the position component were determined by a quadraticfunction (e.g., 1, 4, 9…) that described the influence of complex learning onthe position effect—that is, a small increase may occur at the first fewpositions whereas a steep slope is achieved as one progresses through the test. Asimple linear function was also considered to represent the position effect for acomparison. This linear function simply means that learning increases linearly astesting continues from the first to last items. These two fixed-links models wereaddressed as Linear- and Quadratic models. Since there was the necessity to relatethe binomial distributions of the binary reasoning items to the normal distributionsof the latent scores, a link transformation for eliminating effects due to such adiscrepancy was adopted (cf. McCullagh & Nelder,1985). This transformation was accomplished by weights serving asmultiplier to each true component of the measurement models.


The Prediction of Students' Academic Performance With Fluid Intelligence in Giving Special Consideration to the Contribution of Learning.

Ren X, Schweizer K, Wang T, Xu F - Adv Cogn Psychol (2015)

An illustration of the measurement model including the constant and positioncomponents of reasoning as two independent latent variables and theindividual items of each reasoning test as manifest variables (the model ofthe figure reasoning includes 19 manifest variables, and the model of thenumerical reasoning includes 22 manifest variables).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4591514&req=5

Figure 2: An illustration of the measurement model including the constant and positioncomponents of reasoning as two independent latent variables and theindividual items of each reasoning test as manifest variables (the model ofthe figure reasoning includes 19 manifest variables, and the model of thenumerical reasoning includes 22 manifest variables).
Mentions: The representation of the position effect for each reasoning test required afixed-links model including two latent variables: the constant component and theposition component. Figure 2 illustrates themeasurement model including the constant and position components of reasoning andthe individual items of each reasoning test serving as manifest variables. Theloadings of the constant component were kept constant since this component wasindependent of item positions and contributed almost equally to all individualitems. The loadings of the position component were determined by a quadraticfunction (e.g., 1, 4, 9…) that described the influence of complex learning onthe position effect—that is, a small increase may occur at the first fewpositions whereas a steep slope is achieved as one progresses through the test. Asimple linear function was also considered to represent the position effect for acomparison. This linear function simply means that learning increases linearly astesting continues from the first to last items. These two fixed-links models wereaddressed as Linear- and Quadratic models. Since there was the necessity to relatethe binomial distributions of the binary reasoning items to the normal distributionsof the latent scores, a link transformation for eliminating effects due to such adiscrepancy was adopted (cf. McCullagh & Nelder,1985). This transformation was accomplished by weights serving asmultiplier to each true component of the measurement models.

Bottom Line: The fluid intelligence data were decomposed into a learning component that was associated with the position effect of intelligence items and a constant component that was independent of the position effect.Results showed that the learning component contributed significantly more to the prediction of math and verbal performance than the constant component.Furthermore, the results were in line with the expectation that learning was a predictor of performance in school.

View Article: PubMed Central - PubMed

Affiliation: School of Education, Huazhong University of Science & Technology, Wuhan 430074, China ; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing 100875, China.

ABSTRACT
The present study provides a new account of how fluid intelligence influences academic performance. In this account a complex learning component of fluid intelligence tests is proposed to play a major role in predicting academic performance. A sample of 2, 277 secondary school students completed two reasoning tests that were assumed to represent fluid intelligence and standardized math and verbal tests assessing academic performance. The fluid intelligence data were decomposed into a learning component that was associated with the position effect of intelligence items and a constant component that was independent of the position effect. Results showed that the learning component contributed significantly more to the prediction of math and verbal performance than the constant component. The link from the learning component to math performance was especially strong. These results indicated that fluid intelligence, which has so far been considered as homogeneous, could be decomposed in such a way that the resulting components showed different properties and contributed differently to the prediction of academic performance. Furthermore, the results were in line with the expectation that learning was a predictor of performance in school.

No MeSH data available.