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Un modelo cognitivo-motivacional explicativo del rendimiento académico en la universidad A cognitive-motivational model explanatory of the academic achievement in the university

Estudios de Psicología (Impact Factor: 0.28). 05/1999; 20(1):77-100. DOI: 10.1174/02109390260288631

ABSTRACT El objetivo de este trabajo es estudiar las variables cognitivo-motivacionales que determinan el aprendizaje y el rendimiento académico en estudiantes universitarios. Uno de los supuestos básicos de los que partimos es que los motivos que guían la conducta académica determinan el tipo de recursos cognitivos que ponemos en marcha durante la situación de aprendizaje. Utilizamos un diseño no experimental, descriptivo y tranversal a través de cuestionarios. Para el análisis de datos hemos utilizado el análisis de ecuaciones estructurales (a través del programa estadístico LISREL 7). Los resultados más importante son: (a) los estudiantes utilizan diversas variables personales (variables cognitivas, motivacionales y afectivas) como criterio previo para analizar las tareas; (b) el nivel y tipo de motivación desarrollada influye en las estrategias de aprendizaje; (c) el tipo y número de estrategias de aprendizaje influye en el esfuerzo y persistencia del estudiante, y éstos, a su vez, sobre el rendimiento académico. Los resultados confirman las predicciones teóricas; sin embargo, debemos puntualizar dos cuestiones: (a) que hay otras variables que influyen en el proceso, y (b) que las variables son explicadas a distinta proporción (por ejemplo, la varianza en el rendimiento académico sólo es explicada en un 42%). The aim of this work is to study the cognitive-motivational variables which determine learning and academic achievement in university students. One of our basic assumptions was that motives which guide academic behavior determine the type of cognitive resources which are getting going during learning situation. A no experimental, descriptive and trasnversal design through questionnaires was used. To analyze data we have used structural equations analysis (through LISREL 7 statistical program). The most important results were: (a) students use several personal variables (cognitive. motivational and affective variables) as prior criteria to analyze the tasks; (b) level and type of developed motivation are going to affect learning strategies; (c) type and number of learning strategies influence on student's effort and persistence, and these, in turn, influence on academic achievement. Results confirm the theoretical predictions; however, we must point out two considerations: (a) there as other variables which are influencing in the process, and (b) variables are explained at different levels (for example, the variance in the academic achievement is only explained in a 42%).

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