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The Prediction of Students’ Academic Performance With FluidIntelligence in Giving Special Consideration to the Contribution ofLearning

机译:流畅的学生学习成绩预测特殊考虑智力的贡献学习

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摘要

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.
机译:本研究为流体智力如何影响学术表现提供了新的解释。因此,提出了流体智力测验的一个复杂的学习组成部分,在预测学术表现中起主要作用。在2个277名中学生的样本中,完成了两个推理测试,这些测试被认为代表流体智力,并通过标准化的数学和口头测试来评估学习成绩。流体智能数据被分解为与智能项目的位置效应相关的学习组件和与位置效应无关的恒定组件。结果表明,学习成分对数学和言语成绩预测的贡献远大于恒定成分。从学习组件到数学成绩的联系特别牢固。这些结果表明,迄今为止被认为是同质的流体智力可以通过分解的方式,使得所得的成分表现出不同的性质,并且对学习成绩的预测有不同的贡献。此外,结果符合预期,即学习是学校绩效的预测指标。

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