Identifying Students at Risk to Academic Dropout in Higher Education
A main goal of the university institution should be to reduce the desertion of its students, in fact, the dropout rate constitutes a basic indicator in the accreditation processes of university centers. Thus, evaluating the cognitive functions and learning skills of students with an increased risk o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Education Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7102/11/8/427 |
| _version_ | 1797524143651946496 |
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| author | María Gómez Gallego Alfonso Palazón Perez de los Cobos Juan Cándido Gómez Gallego |
| author_facet | María Gómez Gallego Alfonso Palazón Perez de los Cobos Juan Cándido Gómez Gallego |
| author_sort | María Gómez Gallego |
| collection | DOAJ |
| description | A main goal of the university institution should be to reduce the desertion of its students, in fact, the dropout rate constitutes a basic indicator in the accreditation processes of university centers. Thus, evaluating the cognitive functions and learning skills of students with an increased risk of academic failure can be useful for the adoption of strategies for preventing and reducing school dropout. In this research, cognitive functions and learning skills in 284 university students were evaluated. Academic performance predictors were identified, and conglomerates analysis was carried out to establish groups according to those variables. The stability and validity of the conglomerates were tested with discriminant analyzes and comparison tests. The variables associated significantly to academic performance were: attention, intelligence, motivation, metacognition and affective components. The conglomerate analysis suggested a three-group solution: (1) students with cognitive skills of moderate to high, but deficient learning strategies; (2) students with cognitive and learning capabilities of moderate to high; (3) students with cognitive functions low and moderate learning capacity. Students from groups 1 and 3 showed worse academic performance; 83.3% of students at risk of desertion belonged to such groups. Two groups of students have been identified with the highest risk of academic failure: those with poor cognitive capacity and those with bad learning skills. |
| first_indexed | 2024-03-10T08:53:11Z |
| format | Article |
| id | doaj.art-fde83e25598047b593012302bebdc190 |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-10T08:53:11Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-fde83e25598047b593012302bebdc1902023-11-22T07:23:26ZengMDPI AGEducation Sciences2227-71022021-08-0111842710.3390/educsci11080427Identifying Students at Risk to Academic Dropout in Higher EducationMaría Gómez Gallego0Alfonso Palazón Perez de los Cobos1Juan Cándido Gómez Gallego2Clinical Neuroscience Research Group, Faculty of Health Sciences, Catholic University of Saint Anthony, 30107 Murcia, SpainClinical Neuroscience Research Group, Faculty of Health Sciences, Catholic University of Saint Anthony, 30107 Murcia, SpainApplied Economics Department, University of Murcia, 30100 Murcia, SpainA main goal of the university institution should be to reduce the desertion of its students, in fact, the dropout rate constitutes a basic indicator in the accreditation processes of university centers. Thus, evaluating the cognitive functions and learning skills of students with an increased risk of academic failure can be useful for the adoption of strategies for preventing and reducing school dropout. In this research, cognitive functions and learning skills in 284 university students were evaluated. Academic performance predictors were identified, and conglomerates analysis was carried out to establish groups according to those variables. The stability and validity of the conglomerates were tested with discriminant analyzes and comparison tests. The variables associated significantly to academic performance were: attention, intelligence, motivation, metacognition and affective components. The conglomerate analysis suggested a three-group solution: (1) students with cognitive skills of moderate to high, but deficient learning strategies; (2) students with cognitive and learning capabilities of moderate to high; (3) students with cognitive functions low and moderate learning capacity. Students from groups 1 and 3 showed worse academic performance; 83.3% of students at risk of desertion belonged to such groups. Two groups of students have been identified with the highest risk of academic failure: those with poor cognitive capacity and those with bad learning skills.https://www.mdpi.com/2227-7102/11/8/427academic failurelearning strategieshigher educationattentionintelligence |
| spellingShingle | María Gómez Gallego Alfonso Palazón Perez de los Cobos Juan Cándido Gómez Gallego Identifying Students at Risk to Academic Dropout in Higher Education Education Sciences academic failure learning strategies higher education attention intelligence |
| title | Identifying Students at Risk to Academic Dropout in Higher Education |
| title_full | Identifying Students at Risk to Academic Dropout in Higher Education |
| title_fullStr | Identifying Students at Risk to Academic Dropout in Higher Education |
| title_full_unstemmed | Identifying Students at Risk to Academic Dropout in Higher Education |
| title_short | Identifying Students at Risk to Academic Dropout in Higher Education |
| title_sort | identifying students at risk to academic dropout in higher education |
| topic | academic failure learning strategies higher education attention intelligence |
| url | https://www.mdpi.com/2227-7102/11/8/427 |
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