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 example of the item of the figural reasoning test with the correctanswer.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: An example of the item of the figural reasoning test with the correctanswer.

Mentions: Fluid intelligence was assessed using analogy tasks combining different contents. Thefigural reasoning (FR) test consisted of 19 items each presented in the form ofanalogy patterns composed of geometric figures (see Figure 1 for an example). To complete each item, participants had toinfer the rule underlying the first pattern and to apply the rule to complete thesecond pattern by choosing a correct figure out of four alternatives. The 19 itemsof this test were presented in an ascending order of difficulty. The numericalreasoning (NR) test was the numerical equivalent of the FR test. The elements of thepatterns were simple numbers composed according to underlying rules. This testconsisted of 22 items presented also in an ascending order of difficulty.Participants had 8 min to complete each test. The time limit was chosen on the basisof the results of several pilot testing sessions to make sure that participants hadsufficient time to try to complete each item of each test. The response to each itemof the tests was recorded as binary data. According to the technical report of thesetests (Dong & Lin, 2011), internalconsistency indexed by Cronbach’s s was computed based on a national norm of12,000 junior middle school students. The internal consistencies were .77 for the FRand .86 for the NR. Criterion validity of the reasoning test was established on thebasis of 120 students. The Matrix Reasoning subtest of the Wechsler IntelligenceScale for Children (WISC-IV) served as an external criterion for the reasoning test.Correlations of the FR and NR tests with WISC-IV Matrix Reasoning were .66(p < .01) and .64 (p < .01)respectively.


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 example of the item of the figural reasoning test with the correctanswer.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: An example of the item of the figural reasoning test with the correctanswer.
Mentions: Fluid intelligence was assessed using analogy tasks combining different contents. Thefigural reasoning (FR) test consisted of 19 items each presented in the form ofanalogy patterns composed of geometric figures (see Figure 1 for an example). To complete each item, participants had toinfer the rule underlying the first pattern and to apply the rule to complete thesecond pattern by choosing a correct figure out of four alternatives. The 19 itemsof this test were presented in an ascending order of difficulty. The numericalreasoning (NR) test was the numerical equivalent of the FR test. The elements of thepatterns were simple numbers composed according to underlying rules. This testconsisted of 22 items presented also in an ascending order of difficulty.Participants had 8 min to complete each test. The time limit was chosen on the basisof the results of several pilot testing sessions to make sure that participants hadsufficient time to try to complete each item of each test. The response to each itemof the tests was recorded as binary data. According to the technical report of thesetests (Dong & Lin, 2011), internalconsistency indexed by Cronbach’s s was computed based on a national norm of12,000 junior middle school students. The internal consistencies were .77 for the FRand .86 for the NR. Criterion validity of the reasoning test was established on thebasis of 120 students. The Matrix Reasoning subtest of the Wechsler IntelligenceScale for Children (WISC-IV) served as an external criterion for the reasoning test.Correlations of the FR and NR tests with WISC-IV Matrix Reasoning were .66(p < .01) and .64 (p < .01)respectively.

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.