Mood and academic satisfaction judgments: affective infusion analyzed by semantic networks

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Published: Jul 1, 2017

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Pablo Ezequiel Flores-Kanter
Universidad Siglo 21
Argentina
http://orcid.org/0000-0002-6712-779X
Silvia Adriana Rossi
Universidad Nacional de Córdoba
Argentina
http://orcid.org/0000-0002-7640-1607
Julieta Llamas
Universidad Nacional de Córdoba
Argentina
http://orcid.org/0000-0002-4409-5553
Leonardo Adrián Medrano
Universidad Siglo 21
Argentina
http://orcid.org/0000-0002-3371-5040
Vol. 4 No. 1 (2017), Articles, pages 18-25
DOI: https://doi.org/10.17979/reipe.2017.4.1.1910
Submitted: Dec 5, 2016 Accepted: Jun 1, 2017 Published: Jul 1, 2017
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Abstract

La Satisfacción Académica ha demostrado ser una variable clave para comprender y promover el comportamiento académico óptimo en la población universitaria. Diferentes investigaciones han demostrado además que los juicios de satisfacción, al igual que otros juicios sociales, no reflejan necesariamente estados internos estables. Por el contrario, estarían sujetos a una variedad de influencias transitorias, como pueden ser los estados anímicos. El presente estudio tuvo como objetivo principal verificar la relación entre el estado de ánimo y los juicios de satisfacción académica en estudiantes universitarios. La muestra fue dividida en dos grupos en función del estado de ánimo predominante (grupo de afecto negativo intenso n = 46 y grupo afecto positivo intenso n = 48), comparando luego los grupos respecto a la satisfacción académica. Mientras el estado de ánimo se midió con la escala PANAS, los juicios de satisfacción se obtuvieron mediante la Técnica de Redes Semánticas Naturales. Consistentemente con la Teoría de la Red Asociativa y el Modelo de Procesamiento de la Información, los resultados muestran que la elaboración cognitiva de la satisfacción académica varía de manera congruente con el estado anímico predominante en los estudiantes. Se discuten las implicaciones y limitaciones del estudio

Keywords:

academic satisfaction, moods, university students, Natural Semantic Networks

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