Journal ArticleDOI
The Hungry Mind: Intellectual Curiosity Is the Third Pillar of Academic Performance
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This article proposes expanding on the traditional set of predictors of academic performance by adding a third agency: intellectual curiosity, highlighting that a “hungry mind” is a core determinant of individual differences in academic achievement.Abstract:
Over the past century, academic performance has become the gatekeeper to institutions of higher education, shaping career paths and individual life trajectories. Accordingly, much psychological research has focused on identifying predictors of academic performance, with intelligence and effort emerging as core determinants. In this article, we propose expanding on the traditional set of predictors by adding a third agency: intellectual curiosity. A series of path models based on a meta-analytically derived correlation matrix showed that (a) intelligence is the single most powerful predictor of academic performance; (b) the effects of intelligence on academic performance are not mediated by personality traits; (c) intelligence, Conscientiousness (as marker of effort), and Typical Intellectual Engagement (as marker of intellectual curiosity) are direct, correlated predictors of academic performance; and (d) the additive predictive effect of the personality traits of intellectual curiosity and effort rival that the influence of intelligence. Our results highlight that a “hungry mind” is a core determinant of individual differences in academic achievement.read more
Citations
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Journal ArticleDOI
Human cognitive abilities: A survey of factor analytic studies
TL;DR: A survey of factor analytic studies of human cognitive abilities can be found in this paper, with a focus on the role of factor analysis in human cognitive ability evaluation and cognition. But this survey is limited.
Journal ArticleDOI
Cognitive effort: A neuroeconomic approach.
Andrew Westbrook,Todd S. Braver +1 more
TL;DR: It is argued that the most intuitive and epistemologically valuable treatment is in terms of effort-based decision-making, and the benefits of a neuroeconomic research strategy are described, highlighting how it affords greater inferential traction than do traditional markers of cognitive effort.
Journal ArticleDOI
Openness to Experience and Intellect Differentially Predict Creative Achievement in the Arts and Sciences
Scott Barry Kaufman,Lena C. Quilty,Rachael G. Grazioplene,Jacob B. Hirsh,Jeremy R. Gray,Jordan B. Peterson,Colin G. DeYoung +6 more
TL;DR: The hypothesis that whereas Openness predicts creative achievement in the arts, Intellect predicts creative Achievement in the sciences is confirmed and inclusion of performance measures of general cognitive ability and divergent thinking indicated that the relation of Intellect to scientific creativity may be due to these abilities.
Journal ArticleDOI
True grit and genetics: Predicting academic achievement from personality.
TL;DR: The etiology of Grit is highly similar to other personality traits, not only in showing substantial genetic influence but also in showing no influence of shared environmental factors.
References
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TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
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Alternative Ways of Assessing Model Fit
Michael W. Browne,Robert Cudeck +1 more
TL;DR: In this paper, two types of error involved in fitting a model are considered, error of approximation and error of fit, where the first involves the fit of the model, and the second involves the model's shape.
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TL;DR: Hume's early years and education is described in a treatise of human nature as discussed by the authors. But it is not a complete account of the early years of his life and education.
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Testing Structural Equation Models
Kenneth A. Bollen,J. Scott Long +1 more
TL;DR: In this paper, Bollen et al. proposed a model fitting metric for Structural Equation Models, which is based on the Monte Carlo evaluation of Goodness-of-Fit measures.