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Modelos de Redes Neuronales Perceptrón Multicapa y de Base Radial para la predicción del rendimiento académico de alumnos universitarios
| Content Provider | Semantic Scholar |
|---|---|
| Author | Longoni, María G. Porcel, Eduardo A. López, María Victoria Dapozo, Gladys N. |
| Copyright Year | 2010 |
| Abstract | This paper analyzes the relation between the academic performance of freshmen from FACENA - UNNE in Corrientes, Argentina and their social- educational characteristics. The performance was measured by the number of passed midterm exams of the subjects in the first semester of the first year of study, and the approval of the first subject of mathematics that students study. Multilayer Perceptron (MP) and Radial Basis Function (RBF) neural networks models were adjusted to two data sets: a) students entering careers whose curricula include two courses in the first semester of the first year, obtaining total correct classification rates of 78.2% and 70.7% respectively; b ) students entering careers whose curricula includes three courses in the first semester of the first year, obtaining total correct classification rates of 75.7% and 68.6% respectively. The obtained results contribute to lead policy and institutional strategies to improve the worrying levels of drop out and poor performance of university freshmen, particularly those from FACENA-UNNE. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://sedici.unlp.edu.ar/bitstream/handle/10915/19333/Documento_completo.pdf?sequence=1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
