Destrezas académicas y velocidad de procesamiento. Modelos predictivos del rendimiento escolar en básica primaria

Juan Pablo Sánchez-Escudero; Carolina Medina-Gómez; Yuliana Gómez-Toro

doi:10.21500/19002386.3754

Published: 2019-01-19

DOI: 10.21500/19002386.3754

Destrezas académicas y velocidad de procesamiento. Modelos predictivos del rendimiento escolar en básica primaria

Juan Pablo Sánchez-Escudero

Universidad Católica de Oriente

Carolina Medina-Gómez

Universidad Católica de Oriente

Yuliana Gómez-Toro

Universidad Católica de Oriente

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https://doi.org/10.21500/19002386.3754

Issue: Vol. 13 No. 1 (2019)

How to Cite

Sánchez-Escudero, J. P., Medina-Gómez, C., & Gómez-Toro, Y. (2019). Destrezas académicas y velocidad de procesamiento. Modelos predictivos del rendimiento escolar en básica primaria. Psychologia, 13(1), 25–39. https://doi.org/10.21500/19002386.3754

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Abstract

The Cattell-Horn-Carroll (CHC) intelligence model proposes that the cognitive processes that make up intelligence can be conceptualized as specific skills, involved in particular tasks, and general skills, related to a wide variety of contexts. Among the most studied skills under this model is the speed of processing, identified as one of the best predictors of academic performance and general cognitive ability. This article presents the results of the analysis of the relationship between processing speed and general academic performance. A sample of 223 students (53% women) of preschool and primary school was evaluated. The results show a difference in the predictive capacity of the perceptual component (β = .76, p < .001) and conceptual (β = .09; p = .121) of the processing speed in basic academic processes of reading and mathematics, as well as similar adjustments in regression models from their conceptualization as general (R² = .68) or specific (R² = .69) ability. The analysis of the grade-to-grade relationship showed changes in the predictive capacity of processing speed over academic skills as the educational process progresses, supporting previously established models in the area (Cai, Li & Deng, 2013; Demetriou, Spanodius & Shayer, 2014). Finally, a model of structural equations (X² = 1.431, p = .232, CFI = 1.000, TLI = .999, NFI = .999, RFI = .996, RMSEA = .044) was used to prove the adjustment of the proposed models to data.