Statistical and connectionist models for predict the academic performance of universitary students
This paper analyzes the relationship between the academic performance of students entering professional profile's careers in the FACENA - UNNE in Corrientes, Argentina, during the first year, and their social-educational characteristics.Performance was measured by the approval of the partial ev...
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Escuela de Perfeccionamiento en Investigación Operativa
2018
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| Acceso en línea: | https://revistas.unc.edu.ar/index.php/epio/article/view/20348 |
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I10-R359-article-203482018-06-18T15:15:55Z Statistical and connectionist models for predict the academic performance of universitary students Modelos estadísticos y conexionistas para predecir el rendimiento académico de alumnos universitarios López, María V. Longoni, María G. Porcel, Eduardo A. academic performance university freshmen multinomial logistic regression neural networks multilayer perceptron radial basis function rendimiento académico ingresantes universitarios regresión logística multinomial redes neuronales perceptrón multicapa función de base radial This paper analyzes the relationship between the academic performance of students entering professional profile's careers in the FACENA - UNNE in Corrientes, Argentina, during the first year, and their social-educational characteristics.Performance was measured by the approval of the partial evaluation of the subjects in the first semester of the first year. A model of Multinomial Logistic Regression (MLR) and two models of neural networks of type Multilayer Perceptron (MP) and Radial Basis Function (RBF) were fitted to two data sets: a) students entering in Biochemistry, whose curriculum includes two subjects in the first semester of the first year, b) students entering careers whose curriculum includes three subjects in the first semester of the first year.In both cases, the PM model produced the best fit, and besides it was observed that in the case b) the three techniques showed high percentages of correct classification. The obtained results contribute to guide policies and strategies to improve the worrying levels of dropout and low performance of students in the first year of college. En este trabajo se analiza la relación del rendimiento académico de los alumnos ingresantes a las carreras de perfil profesional la FACENA – UNNE en Corrientes, Argentina, durante el primer año, con sus características socioeducativas. El rendimiento fue medido por la aprobación de los exámenes parciales de las asignaturas del primer cuatrimestre del primer año. Se ajustaron un modelo de Regresión Logística Multinomial (RLM) y dos modelos de redes neuronales de tipo Perceptrón Multicapa (PM) y de Función de Base Radial (FBR) a dos conjuntos de datos: a) alumnos ingresantes a Bioquímica, cuyos plan de estudios incluye dos asignaturas en el primer cuatrimestre del primer año; b) alumnos ingresantes a carreras cuyos planes de estudios incluyen tres asignaturas en el primer cuatrimestre del primer año.En ambos casos el modelo PM produjo el mejor ajuste, observándose que en el caso b) las tres técnicas utilizadas registraron altos porcentajes de clasificación correcta. Los resultados obtenidos contribuyen a orientar las políticas y estrategias institucionales para mejorar los preocupantes índices de desgranamiento, abandono y bajo rendimiento de los estudiantes en el primer año de universidad. Escuela de Perfeccionamiento en Investigación Operativa 2018-06-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.unc.edu.ar/index.php/epio/article/view/20348 Revista de la Escuela de Perfeccionamiento en Investigación Operativa; Vol. 20 Núm. 33 (2012): Octubre; 135-157 1853-9777 0329-7322 spa https://revistas.unc.edu.ar/index.php/epio/article/view/20348/19979 |
| institution |
Universidad Nacional de Córdoba |
| institution_str |
I-10 |
| repository_str |
R-359 |
| container_title_str |
Revista de la Escuela de Perfeccionamiento en Investigación Operativa |
| language |
Español |
| format |
Artículo revista |
| topic |
academic performance university freshmen multinomial logistic regression neural networks multilayer perceptron radial basis function rendimiento académico ingresantes universitarios regresión logística multinomial redes neuronales perceptrón multicapa función de base radial |
| spellingShingle |
academic performance university freshmen multinomial logistic regression neural networks multilayer perceptron radial basis function rendimiento académico ingresantes universitarios regresión logística multinomial redes neuronales perceptrón multicapa función de base radial López, María V. Longoni, María G. Porcel, Eduardo A. Statistical and connectionist models for predict the academic performance of universitary students |
| topic_facet |
academic performance university freshmen multinomial logistic regression neural networks multilayer perceptron radial basis function rendimiento académico ingresantes universitarios regresión logística multinomial redes neuronales perceptrón multicapa función de base radial |
| author |
López, María V. Longoni, María G. Porcel, Eduardo A. |
| author_facet |
López, María V. Longoni, María G. Porcel, Eduardo A. |
| author_sort |
López, María V. |
| title |
Statistical and connectionist models for predict the academic performance of universitary students |
| title_short |
Statistical and connectionist models for predict the academic performance of universitary students |
| title_full |
Statistical and connectionist models for predict the academic performance of universitary students |
| title_fullStr |
Statistical and connectionist models for predict the academic performance of universitary students |
| title_full_unstemmed |
Statistical and connectionist models for predict the academic performance of universitary students |
| title_sort |
statistical and connectionist models for predict the academic performance of universitary students |
| description |
This paper analyzes the relationship between the academic performance of students entering professional profile's careers in the FACENA - UNNE in Corrientes, Argentina, during the first year, and their social-educational characteristics.Performance was measured by the approval of the partial evaluation of the subjects in the first semester of the first year. A model of Multinomial Logistic Regression (MLR) and two models of neural networks of type Multilayer Perceptron (MP) and Radial Basis Function (RBF) were fitted to two data sets: a) students entering in Biochemistry, whose curriculum includes two subjects in the first semester of the first year, b) students entering careers whose curriculum includes three subjects in the first semester of the first year.In both cases, the PM model produced the best fit, and besides it was observed that in the case b) the three techniques showed high percentages of correct classification. The obtained results contribute to guide policies and strategies to improve the worrying levels of dropout and low performance of students in the first year of college. |
| publisher |
Escuela de Perfeccionamiento en Investigación Operativa |
| publishDate |
2018 |
| url |
https://revistas.unc.edu.ar/index.php/epio/article/view/20348 |
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