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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 prediction model including the constant and learning components of fluidintelligence as predictor variables and math and verbal achievements aspredicted variables. All completely standardized path coefficients reachedthe level of significance (** p < .01). The pathcoefficient from the learning component to each predicted variable wasstatistically larger than the one from the constant component.
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Figure 4: The prediction model including the constant and learning components of fluidintelligence as predictor variables and math and verbal achievements aspredicted variables. All completely standardized path coefficients reachedthe level of significance (** p < .01). The pathcoefficient from the learning component to each predicted variable wasstatistically larger than the one from the constant component.

Mentions: The representation of the constant and learning components of fluid intelligence bythe second-order CFA model made it possible to relate the components to the academicscores. This was achieved by a means of a full structural equation modeladditionally including two criterion variables representing the math and verbalperformance. The fit statistics of this model indicate a good fit,χ2(1018) = 5, 204.97, RMSEA = .043 [CI90: .041 .044], SRMR =.063, CFI = .915. Figure 4 provides anillustration of the structure of this prediction model.


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 prediction model including the constant and learning components of fluidintelligence as predictor variables and math and verbal achievements aspredicted variables. All completely standardized path coefficients reachedthe level of significance (** p < .01). The pathcoefficient from the learning component to each predicted variable wasstatistically larger than the one from the constant component.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: The prediction model including the constant and learning components of fluidintelligence as predictor variables and math and verbal achievements aspredicted variables. All completely standardized path coefficients reachedthe level of significance (** p < .01). The pathcoefficient from the learning component to each predicted variable wasstatistically larger than the one from the constant component.
Mentions: The representation of the constant and learning components of fluid intelligence bythe second-order CFA model made it possible to relate the components to the academicscores. This was achieved by a means of a full structural equation modeladditionally including two criterion variables representing the math and verbalperformance. The fit statistics of this model indicate a good fit,χ2(1018) = 5, 204.97, RMSEA = .043 [CI90: .041 .044], SRMR =.063, CFI = .915. Figure 4 provides anillustration of the structure of this prediction model.

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.