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Explanatory model of emotional-cognitive variables in school mathematics performance: a longitudinal study in primary school.

Cerda G, Pérez C, Navarro JI, Aguilar M, Casas JA, Aragón E - Front Psychol (2015)

Bottom Line: The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model.This combined interaction model was able to predict 64.3% of the variability of observed performance.Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Metodología de la Investigación e Informática Educacional, Facultad de Educación, Universidad de Concepción Concepción, Chile.

ABSTRACT
This study tested a structural model of cognitive-emotional explanatory variables to explain performance in mathematics. The predictor variables assessed were related to students' level of development of early mathematical competencies (EMCs), specifically, relational and numerical competencies, predisposition toward mathematics, and the level of logical intelligence in a population of primary school Chilean students (n = 634). This longitudinal study also included the academic performance of the students during a period of 4 years as a variable. The sampled students were initially assessed by means of an Early Numeracy Test, and, subsequently, they were administered a Likert-type scale to measure their predisposition toward mathematics (EPMAT) and a basic test of logical intelligence. The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model. This combined interaction model was able to predict 64.3% of the variability of observed performance. Preschool students' performance in EMCs was a strong predictor for achievement in mathematics for students between 8 and 11 years of age. Therefore, this paper highlights the importance of EMCs and the modulating role of predisposition toward mathematics. Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain.

No MeSH data available.


Structural equations model of the complex interaction between EMCs and unfavorable predisposition toward mathematics with respect to performance in mathematics.
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Figure 3: Structural equations model of the complex interaction between EMCs and unfavorable predisposition toward mathematics with respect to performance in mathematics.

Mentions: Subsequently, the influence of predisposition toward mathematics for academic performance in mathematics was assessed (Figure 3). The model’s adjustment indices were χ2SB = 291.52, p = 0.000, CFI = 0.953, NNFI = 0.945, RMSEA = 0.044, IC(0.037–0.050), showing optimal fitting. Standardized regression coefficients indicate that EMCs have a direct and positive influence on students’ grade point averages in mathematics (β = 0.51; p < 0.001) and that they have an inverse and significant relation with an unfavorable predisposition toward mathematics (β = -0.32; p < 0.001). Likewise, an unfavorable predisposition toward mathematics has a negative and significant relation with academic performance in mathematics (β = -0.44; p < 0.001). This model predicts 59.7% of the variance in students’ grade point averages in the subject of mathematics.


Explanatory model of emotional-cognitive variables in school mathematics performance: a longitudinal study in primary school.

Cerda G, Pérez C, Navarro JI, Aguilar M, Casas JA, Aragón E - Front Psychol (2015)

Structural equations model of the complex interaction between EMCs and unfavorable predisposition toward mathematics with respect to performance in mathematics.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4561756&req=5

Figure 3: Structural equations model of the complex interaction between EMCs and unfavorable predisposition toward mathematics with respect to performance in mathematics.
Mentions: Subsequently, the influence of predisposition toward mathematics for academic performance in mathematics was assessed (Figure 3). The model’s adjustment indices were χ2SB = 291.52, p = 0.000, CFI = 0.953, NNFI = 0.945, RMSEA = 0.044, IC(0.037–0.050), showing optimal fitting. Standardized regression coefficients indicate that EMCs have a direct and positive influence on students’ grade point averages in mathematics (β = 0.51; p < 0.001) and that they have an inverse and significant relation with an unfavorable predisposition toward mathematics (β = -0.32; p < 0.001). Likewise, an unfavorable predisposition toward mathematics has a negative and significant relation with academic performance in mathematics (β = -0.44; p < 0.001). This model predicts 59.7% of the variance in students’ grade point averages in the subject of mathematics.

Bottom Line: The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model.This combined interaction model was able to predict 64.3% of the variability of observed performance.Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Metodología de la Investigación e Informática Educacional, Facultad de Educación, Universidad de Concepción Concepción, Chile.

ABSTRACT
This study tested a structural model of cognitive-emotional explanatory variables to explain performance in mathematics. The predictor variables assessed were related to students' level of development of early mathematical competencies (EMCs), specifically, relational and numerical competencies, predisposition toward mathematics, and the level of logical intelligence in a population of primary school Chilean students (n = 634). This longitudinal study also included the academic performance of the students during a period of 4 years as a variable. The sampled students were initially assessed by means of an Early Numeracy Test, and, subsequently, they were administered a Likert-type scale to measure their predisposition toward mathematics (EPMAT) and a basic test of logical intelligence. The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model. This combined interaction model was able to predict 64.3% of the variability of observed performance. Preschool students' performance in EMCs was a strong predictor for achievement in mathematics for students between 8 and 11 years of age. Therefore, this paper highlights the importance of EMCs and the modulating role of predisposition toward mathematics. Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain.

No MeSH data available.