Understanding Catalan University Dropout from a Comparative Approach

Gómez, David Rodríguez; Condom, Mònica Feixas i; Gairín, Joaquìn; Muñoz, José Luis Maté

doi:10.1016/j.sbspro.2012.05.314

Cited by 6 publications

(9 citation statements)

References 1 publication

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“…Moreover, the lack of orientation after secondary studies and the low academic performance of students during high school is considered as a major cause of unreadiness for higher education which boosts the risks of dropout (Rodríguez-Gómez, Feixas, Gairín & Muñoz, 2012). Students from different backgrounds who were well-prepared academically in high school and graduated with good grades are usually expected to graduate successfully in college unlike those with poor high school education (Páramo, Araújo, Vacas, Almeida, & González, 2017).…”

Section: External Dimensionsmentioning

confidence: 99%

“…Besides, studies have shown that many educational institutions are academically underprepared to transfer the necessary knowledge and skills for students to enter the job market. Such a fact increases the students´ doubt and feeling of unreadiness for the labor market which pushes them to dropout (Rodríguez-Gómez et al, 2012, Rodríguez-Gómez, Feixas, Gairín, & Muñoz, 2015.…”

Section: Organizational Dimensionsmentioning

confidence: 99%

“…The main predictor of students´ satisfaction in higher education institutions does not lie in attracting students to register but in maintaining students to successfully finish their study programs and graduate with satisfaction. (Suhre et al, 2007, Christenson et al, 2012, Rodríguez-Gómez et al, 2012.…”

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

The Academic Dropout Wheel Analyzing the Antecedents of Higher Education Dropout in Education Studies

Naaman

2021

The European Educational Researcher

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Higher education dropout has been considered a major concern for several researchers in the field of education around the World. Although different studies were carried out to deal with the topic, they all faced common limitations. This paper explores the twofold research conducted to investigate higher education dropout in Education studies at two of the main Catalan public universities. First, a review of the different theoretical perspectives that tackled dropout was carried out to combine the direct and indirect dropout predictors. Second, some research was conducted in the faculties of Education in the selected two Catalan public universities to investigate the antecedents of higher education dropout. The utilized instrument discussed in this paper is the survey. In the first part, the predictors of higher education dropout were combined in a 5-layer model called the ¨Dimensions of Academic Dropout” wheel (Naaman, 2018). In the second part, the results of the survey analyzed quantitively revealed that dissatisfaction from the program and academic failure are the most important antecedents of higher education dropout in education studies in Catalonia.

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Section: External Dimensionsmentioning

confidence: 99%

Section: Organizational Dimensionsmentioning

confidence: 99%

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The Academic Dropout Wheel Analyzing the Antecedents of Higher Education Dropout in Education Studies

Naaman

2021

The European Educational Researcher

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“…The progress of a country is highly dependent on the education of its citizens (Munizaga-Mellado, Cifuentes-Orellana & Beltrán-Gabriel, 2018;Rodríguez-Gómez, Feixas, M., Gairín & Muñoz, 2012), as education plays a central role and has a cross cutting impact on all aspects of human life. Universities prepare students to perform as professionals and create environments for national advancement based on creativity and innovation, supporting economic growth for the country.…”

Section: Introductionmentioning

confidence: 99%

Predicting Computer Engineering students' dropout in Cuban Higher Education with pre-enrollment and early performance data

Alvarez

Callejas

Griol

2020

J. Technol. Sci. Educ.

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We present an educational data analytics case study aimed at the early detection of potential dropout in Computer Engineering studies in Cuba. We have employed institutional data of 456 students and performed several experiments for predicting their permanency into three (promotion, repetition, and dropout) or two classes (promoting, not promoting). We have also tested a combination of classification features for training and testing decision trees and neural networks; including information obtained at the time of enrollment, after the first semester and after the first academic year. Our results show a considerable accuracy using all features (96.71%). Using only the features available at the time of enrolment and after the first semester we obtain very positive results (68.86% and 93.85% accuracy respectively) with a high recall of non-promoting students. Thus, it is possible to obtain an early assessment of the risk of dropout that can help defining prevention policies.

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“…Indeed, each of these factors can have an impact on academic performance and on the adaptation to college life. The ability to overcome these factors will later enable the student to achieve the objectives offered by his professional education (Alarcon & Edwars, 2013;Rodríguez-Gómez, Feixas, Gairín, & Muñoz, 2012;Vries, León Arenas, Romero Muñoz, & Hernández Saldaña, 2011). When a goal is set, the motivation that will drive the achievement of the proposed goal is defined.…”

mentioning

confidence: 99%