Locus de control y rendimiento académico en educación universitaria: Una revisión bibliográfica

Pernía, Carlos Alberto Mayora; Morgado, Nelly Fernández de

doi:10.15359/ree.19-3.16

Cited by 15 publications

(13 citation statements)

References 19 publications

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“…If this evaluation originates from a state of satisfaction, it also provokes a greater interest in the study and, therefore, in deep learning strategies. Ivancevich, Konopaske, and Matteson (2006) and Key (2002) explain how greater satisfaction and performance in the subject (self-efficacy) derive towards a greater interest in the study and, therefore, in deep learning strategies (Mayora-Pernía & Morgado, 2015). Based on the theory of planned behavior, it is predictable that student satisfaction will have a positive influence on the deep learning approaches (Ajzen, 1991;Keller, 2009).…”

Section: User Satisfaction and Deep Learning Approachmentioning

confidence: 99%

Gamification and deep learning approaches in higher education

Aguiar-Castillo

Clavijo-Rodriguez

Hernández-López

et al. 2021

Journal of Hospitality, Leisure, Sport & Tourism Education

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Gamification is a tool that has been used to promote commitment and motivation to students. This fact, coupled with the intensive use made by students of their smart phones, has led to the development of the HEgameApp application. This study shows that students' satisfaction will be influenced directly by application features, and it will influence pro-deep-learning approaches. Besides, the initiative will be visible through word of mouth. According to this study a gamified application can contribute to the pro-deep-learning approaches as students wish to expose their private learning-related actions to friends. An interesting finding of this study is the connection between the users' satisfaction and the promotion of behaviors related to deep learning strategies, since the more satisfied the students are, the more their deep learning strategies will be promoted. Another relevant finding comes from the teaching institution, as this type of initiative is very well accepted by the students and will improve the image, providing a smart academic, studentfocused reputation to the implementing academia.

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Section: User Satisfaction and Deep Learning Approachmentioning

confidence: 99%

Gamification and deep learning approaches in higher education

Aguiar-Castillo

Clavijo-Rodriguez

Hernández-López

et al. 2021

Journal of Hospitality, Leisure, Sport & Tourism Education

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“…Finally, academic performance is a multidimensional variable that can be influenced by personal factors (i.e., sociodemographic and psychological) and contextual ones (i.e., economic, familial and academic; Mayora-Pernía and Fernández-Morgado, 2015). More specifically, in the first year or semester, academic performance has a high positive correlation with permanence at university (Araque et al, 2009;Bernardo et al, 2016), and is considered an indicator of adaptation to the demands of academic life (Aranda et al, 2013) and one of the best predictors of retention in second and third year (Westrick et al, 2015).…”

Section: Theoretical Perspective Background and Prior Researchmentioning

confidence: 99%

Predictive model of the dropout intention of Chilean university students

et al. 2023

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Dropping out of university studies is one of the current problems of Higher Education; the increased rates during the first year of the study programme is considerable around the world. Dropping out has negative social implications that are reflected at the personal, family, institutional, and educational levels. The aim of this study was to evaluate a predictive model considering the mediation of university social satisfaction and perceived academic performance within the relations between perceived social support, social self-efficacy and academic purposes with career satisfaction and dropout intention in Chilean university students. A non-experimental explanatory design of latent and observed variables was used. Structural equation analyses with Mplus software were performed. The sample consisted of 956 first year university students. The study complied with the ethical requirements for research with human subjects. As a result, a predictive model with adequate adjustment indexes was obtained. When evaluating the explanatory capacity through the coefficient of determination (R2), it was observed that it explains 38.9 and 27.4% of the variance of the dropout intention and career satisfaction, respectively. This percentage of explanation indicates a large effect size in Social Sciences; therefore, they are considered adequate predictive models. The mediation of university social satisfaction on the relationships between social support, social self-efficacy, and academic purposes with academic adjustment and dropout intention was, respectively, confirmed. The perception of academic performance has less influence on dropout intention and on career satisfaction among first-year students. The model obtained allows explaining the dropout intention and career satisfaction in first year students. In addition, it is composed of variables that can potentially be modified in the interaction of students and professors.

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“…A partir de lo anterior, se deduce que conceptualmente el enfoque de la responsabilidad social universitaria (rsu) implica una serie de características psicosociales que denotan una forma de ser y de actuar de la persona considerada como socialmente responsable. Si bien la investigación e implementación del enfoque está en sus inicios (anuies, 2018), algunos estudios han aportado evidencia en torno a la vinculación existente, tanto a nivel teórico como empírico, entre la responsabilidad social y algunas variables psicosociales como la empatía (Arango et al, 2014;Martí, 2011;Martí et al, 2014), el autoconcepto (Esnaola et al, 2008;Goñí, 2009), el locus de control (Mayorá-Pernía y Fernández, 2015;Pinto, 2000;Serrano, 2003;Velasco et al, 2015), el enfrentamiento a los problemas (Góngora, 2000;Góngora et al, 2008) y la salud mental positiva (Barrera y Flores, 2015;Castillo León, 2016;Lluch, 1999). Cada uno de los estudios anteriores ha explorado, por separado, la asociación entre la responsabilidad social con cada una de las variables antes mencionadas.…”

Section: Conceptualización De Responsabilidad Social Universitaria Y ...unclassified

Modelos psicosociales de responsabilidad social en universitarios de diferentes campus

Pereira¹,

Castillo-León²,

Hernández-Payán³

2021

RIES

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En este artículo se desarrollan modelos para explicar la responsabilidad social en estudiantes de diferentes áreas del conocimiento de una universidad pública del sureste mexicano. Participaron 500 estudiantes elegidos al azar. Se les administraron instrumentos para medir locus de control, autoconcepto, enfrentamiento a los problemas, empatía, salud mental positiva y responsabilidad social. Los resultados mostraron diferencias significativas en todas las variables por área del conocimiento. Asimismo, los modelos psicosociales resultantes evidenciaron la contribución del autoconcepto, el enfrentamiento y la empatía para la responsabilidad social universitaria. Los resultados se analizan a partir de la congruencia con el enfoque de la responsabilidad social universitaria.

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Locus de control y rendimiento académico en educación universitaria: Una revisión bibliográfica

Cited by 15 publications

References 19 publications

Gamification and deep learning approaches in higher education

Gamification and deep learning approaches in higher education

Predictive model of the dropout intention of Chilean university students

Modelos psicosociales de responsabilidad social en universitarios de diferentes campus

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