Rendimiento Académico Y Otras Variables Psicosocio-Familiares En Alumnos De Educación Secundaria Obligatoria

Abstract

This paper attempts to raise awareness of the need to transfer the research linking academic performance mainly personal and endogenous variables (intra-psychic), to other family, social and contextual, in order to expand and add with new data some of the shortcomings obtained on the results of research based on the theories of traits and factors, the differentialfactorial approach to intelligence and clinical models. Significant empirical data were obtained in some variables, which provide an “optimal constellation of variables” which may be more likely to achieve better academic achievement. 317 students of Secondary Education were part of this research in a public center of Zaragoza (Aragon-Spain) with a high level of immigration (53%), who were given an “ad hoc” Family Settings, Psychosocial and Contextual Questionnaire” whose factorial analysis yielded three factors: Context Immigration, Family Settings and academic autobiographical history and study habits. Further analysis of variance, correlation and regression allow us to glimpse a configuration of the most important variables that point to a hypothetical “academic success” with moderate but significant indices, which provide new information that may allow progress in the construction of better theoretical models in this educational field where there is great ethnic and cultural diversity, which requires further research in the future. However, the need to return to the inclusion of general assessments of general intelligence and effective study time by students is redesigned for further research, given the low level of academic skills and competence shown by a large majority of immigrant students, in order to obtain better performance prediction models, based on hierarchical linear models and multilevel analysis, with programs like MLwin that analyze the relationships between variables of different levels or other including fixed, random effects and covariates in the model predictor.