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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.


The latent structure of the second-order CFA model with the constant andlearning components of fluid intelligence as higher-order factors which werederived from the four components of the reasoning tests. Completelystandardized factor loadings and completely standardized error variances ofthe latent variables are also presented (** p < .01).The correlations between the constant and the position components were fixedto zero.
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Figure 3: The latent structure of the second-order CFA model with the constant andlearning components of fluid intelligence as higher-order factors which werederived from the four components of the reasoning tests. Completelystandardized factor loadings and completely standardized error variances ofthe latent variables are also presented (** p < .01).The correlations between the constant and the position components were fixedto zero.

Mentions: In a following step, a second-order CFA model that included two higher-order factorsrepresenting the constant and the learning components of fluid intelligence wasinspected. This second-order model, compared to the comprehensive CFA model,additionally included two higher-order factors addressed as the constant andlearning components of fluid intelligence. Figure3 presents the latent structure of this second-order model.Unfortunately, some of the estimated parameters could not be identified in thismodel. Therefore, we fixed the residuals of the first-order latent variablesaccording to the estimated values from the comprehensive CFA model (i.e., thefirst-order model) so that a stable switch was achieved from the first- to thesecond-order models. The fit statistics of the second-order model were acceptable,χ2(816) = 4,743.26, RMSEA = .046 [CI90: .045 .047], SRMR =.065, CFI = .862. The relationships of the first-order latent variables and thesecond-order latent variables were rather close, as it was obvious from thestandardized loadings varying between .80 and .89.


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)

The latent structure of the second-order CFA model with the constant andlearning components of fluid intelligence as higher-order factors which werederived from the four components of the reasoning tests. Completelystandardized factor loadings and completely standardized error variances ofthe latent variables are also presented (** p < .01).The correlations between the constant and the position components were fixedto zero.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: The latent structure of the second-order CFA model with the constant andlearning components of fluid intelligence as higher-order factors which werederived from the four components of the reasoning tests. Completelystandardized factor loadings and completely standardized error variances ofthe latent variables are also presented (** p < .01).The correlations between the constant and the position components were fixedto zero.
Mentions: In a following step, a second-order CFA model that included two higher-order factorsrepresenting the constant and the learning components of fluid intelligence wasinspected. This second-order model, compared to the comprehensive CFA model,additionally included two higher-order factors addressed as the constant andlearning components of fluid intelligence. Figure3 presents the latent structure of this second-order model.Unfortunately, some of the estimated parameters could not be identified in thismodel. Therefore, we fixed the residuals of the first-order latent variablesaccording to the estimated values from the comprehensive CFA model (i.e., thefirst-order model) so that a stable switch was achieved from the first- to thesecond-order models. The fit statistics of the second-order model were acceptable,χ2(816) = 4,743.26, RMSEA = .046 [CI90: .045 .047], SRMR =.065, CFI = .862. The relationships of the first-order latent variables and thesecond-order latent variables were rather close, as it was obvious from thestandardized loadings varying between .80 and .89.

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.