Cognition and cognizance in preschool predict school achievement in primary school

Δημητρίου, Ανδρέας; Kazali, Elena; Kazi, Smaragda; Spanoudis, George

doi:10.1016/j.cogdev.2020.100872

Cited by 23 publications

(23 citation statements)

References 22 publications

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“…This makes us reconsider the moment at which the influence of the ESCS begins to determine results in school effectiveness [2,[7][8][9][10][11][12][13][14][15]. Performance at these ages could be explained more by individual characteristics, such as differences in executive functions [57][58][59][60][61][62] than by context. Although these results could be evaluated as positive, since it is not contextual factors that determine performance at early ages of schooling, they do invite reflection on the factors that could affect performance at these ages and when contextual factors begin to gain importance.…”

Section: Discussionmentioning

confidence: 99%

Influence of Contextual Variables on Educational Performance: A Study Using Hierarchical Segmentation Trees

2020

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The general objective of this study is to explore the relationship between students’ contextual characteristics and their performance in mathematical reasoning (MR) and linguistic comprehension (LC) skills. The census data from the ESCALA (EScritura, CAlculo y Lectura en Andalucía) tests developed by Agencia Andaluza de Evaluación Educativa (AGAEVE) in 2017 were used. These tests are carried out in the second year of primary school in the Autonomous Community of Andalusia (Spain). These data have been analysed through the data mining technique known as segmentation trees, using the CRT (Classification and regression trees) algorithm for each of the skills. This has allowed the detection of the high influence of social and cultural status (ESCS) and familial expectations regarding academic performance in both tests. In addition, it allows us to point out that there are different interactions between contextual characteristics and their relationship to performance in MR and LC. These results have made it possible to establish groups of students who may be at risk of not reaching the minimum required levels. Some characteristics of at-risk students are low ESCS, low family expectations or being born in the last six months of the year. The detection of at-risk profiles could contribute to the optimisation of the performance of these groups by creating specific plans.

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Section: Discussionmentioning

confidence: 99%

Influence of Contextual Variables on Educational Performance: A Study Using Hierarchical Segmentation Trees

2020

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“…Ahora bien, de las tres variables cognitivas que predicen el rendimiento académico en este estudio, solo la memoria de trabajo verbal tuvo una asociación mediana. Esto es coherente con el hecho de que en estudios previos la memoria de trabajo ha sido fuertemente implicada en el rendimiento académico, y su papel en los procesos de aprendizaje ha sido ampliamente documentado en la infancia (Demetriou, Kazali, Kazi, & Spanoudis, 2020;Ramos et al, 2018;Reyes et al, 2015;Simone, et al, 2018;Swanson, & Alloway, 2012;Visu-Petra et al, 2011). El presente trabajo es una contribución al estudio de esta asociación en población universitaria.…”

Section: Discussionunclassified

Las funciones ejecutivas como predictoras del rendimiento académico de estudiantes universitarios

Gutiérrez-Ruiz

Paternina

Zakzuk

et al. 2020

PSYE

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En este trabajo se estudia la relación entre Funciones ejecutivas (FE) y rendimiento académico, analizando mediante medidas cognitivas de FE si la memoria de trabajo, el control inhibitorio, la flexibilidad cognitiva, y la capacidad de abstracción y formación de conceptos predicen el logro académico general de estudiantes universitarios. Se realizó un estudio no experimental transversal correlacional-causal en el que participaron 104 estudiantes universitarios, quienes fueron evaluados mediante el Test de clasificación de tarjetas de Wisconsisn modificado (M-WCST), el Test de Colores y Palabras Stroop, y el índice de Memoria de trabajo de la Escala de inteligencia para adultos de Wechsler WAIS-III; y se registró su promedio acumulado de calificaciones del último periodo académico cursado. Se realizó un análisis de regresión jerárquica múltiple para evaluar si las FE contribuyen significativamente al logro académico general, y se comparó el desempeño en tareas de FE entre estudiantes con rendimiento académico bajo y promedio. Los resultados mostraron que la memoria de trabajo verbal, la flexibilidad cognitiva y la capacidad de abstracción y formación de conceptos predicen el rendimiento académico general de los estudiantes universitarios, aunque logran explicar solo el 19,8% de la varianza. Se encontró diferencia estadísticamente significativa con un tamaño del efecto mediano entre el grupo con rendimiento académico bajo y promedio en la tarea de memoria de trabajo verbal Aritmética. Se plantea la necesidad de considerar la edad como posible variable mediadora de la relación entre FE y rendimiento académico general, y de explorar otras variables cognitivas, conductuales y psicosociales que permitan predecir en mayor medida el rendimiento académico de estudiantes universitarios.

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“…Interestingly, the best predictors of school performance at different developmental phases are the processes defining g in each phase: attention control and representational awareness in preschool, inductive reasoning and working memory in primary school, and deductive reasoning and self-evaluation accuracy in adolescence Demetriou, Kazali et al, 2020;).…”

Section: Where Does Change In G Come From?mentioning

confidence: 99%

The future of intelligence: The central meaning-making unit of intelligence in the mind, the brain, and artificial intelligence

et al. 2021

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This paper focuses on general intelligence, g. We first point to broadly accepted facts about g: it is robust, reliable, and sensitive to learning. We then summarize conflicting theories about its nature and development (Mutualism, Process Overlap Theory, and Dynamic Mental Field Theory) and suggest how future research may resolve their disputes. A model is proposed for g involving a core meaning-making mechanism, noetron, drawing on Alignment, Abstraction, and Cognizance, perpetually generating new mental content. Noetron develops through several levels of control: episodic → attentional → inferential → truth → epistemic control in infancy, preschool, childhood, adolescence, and adulthood, respectively. Finally, we propose an agenda for future brain, assuming a brain noetron, and artificial intelligence research, assuming an artificial noetron, that might uncover the underlying brain mechanisms of g and generate artificial general intelligence.

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Cognition and cognizance in preschool predict school achievement in primary school

Cited by 23 publications

References 22 publications

Influence of Contextual Variables on Educational Performance: A Study Using Hierarchical Segmentation Trees

Influence of Contextual Variables on Educational Performance: A Study Using Hierarchical Segmentation Trees

Las funciones ejecutivas como predictoras del rendimiento académico de estudiantes universitarios

The future of intelligence: The central meaning-making unit of intelligence in the mind, the brain, and artificial intelligence

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