Prediction of academic results with the nntool application in Matlab using artificial neural networks
In 2018, Peru participated in the International Student Assessment (PISA), where disappointing results were evidenced in students of regular basic education. In this sense, the prediction of academic results was considered as an instrument for improving school performance. The objective of this rese...
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| Publicado en: | Apuntes Universitarios |
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| Formato: | Artículo de revista |
| Lenguaje: | español |
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Universidad Peruana Unión
2021
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| _version_ | 1881695044933517312 |
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| author | Capuñay Sanchez, Dulce Lucero Incio Flores, Fernando Alain Estela Urbina, Ronald Omar Montenegro Camacho, Luis Delgado Soto, Jorge Antonio Cueva Valdivia, Johnny |
| author_browse | Capuñay Sanchez, Dulce Lucero Cueva Valdivia, Johnny Delgado Soto, Jorge Antonio Estela Urbina, Ronald Omar Incio Flores, Fernando Alain Montenegro Camacho, Luis |
| author_facet | Capuñay Sanchez, Dulce Lucero Incio Flores, Fernando Alain Estela Urbina, Ronald Omar Montenegro Camacho, Luis Delgado Soto, Jorge Antonio Cueva Valdivia, Johnny |
| author_sort | Capuñay Sanchez, Dulce Lucero |
| collection | Revistas |
| container_title | Apuntes Universitarios |
| description | In 2018, Peru participated in the International Student Assessment (PISA), where disappointing results were evidenced in students of regular basic education. In this sense, the prediction of academic results was considered as an instrument for improving school performance. The objective of this research was to predict the annual average of second grade students of the Educational Institution N ° 16093 in the province of Jaén-Peru, through the design and implementation of an artificial neural network (ANN). To collect data on variables that influence the annual average of the student, a questionnaire with dichotomous responses was developed. In the validation and reliability, we used the expert judgment criterion and the Kuder-Richarson test respectively, the reliability coefficient obtained in a pilot test applied to 15 students was 0.8359. In the Matlab Scientific Software with the help of the nntool application, the RNA was designed consisting of three hidden layers and an output layer. The ANN during training, validation and testing, registered a weighted correlation coefficient of 0.967190, and a mean square error of 0.05011. The neural model implemented under the given conditions achieved an effectiveness of 88.670% and 98.522%. |
| format | Online |
| id | oai:ojs2.apuntesuniversitarios.upeu.edu.pe:article/976 |
| institution | UPeU |
| issn | 2304-0335 2225-7136 |
| language | Spanish |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Universidad Peruana Unión |
| publisherStr | Universidad Peruana Unión |
| record_format | ojs |
| revista_str_mv | Apuntes Universitarios |
| spelling | oai:ojs2.apuntesuniversitarios.upeu.edu.pe:article/9762021-12-07T19:47:04Z Prediction of academic results with the nntool application in Matlab using artificial neural networks Predicción de resultados académicos con la aplicación nntool en Matlab utilizando redes neuronales artificiales Capuñay Sanchez, Dulce Lucero Incio Flores, Fernando Alain Estela Urbina, Ronald Omar Montenegro Camacho, Luis Delgado Soto, Jorge Antonio Cueva Valdivia, Johnny Academic performance, nntool, artificial neural networks, prediction, education. Rendimiento académico, nntool, redes neuronales artificiales, predicción, educación. In 2018, Peru participated in the International Student Assessment (PISA), where disappointing results were evidenced in students of regular basic education. In this sense, the prediction of academic results was considered as an instrument for improving school performance. The objective of this research was to predict the annual average of second grade students of the Educational Institution N ° 16093 in the province of Jaén-Peru, through the design and implementation of an artificial neural network (ANN). To collect data on variables that influence the annual average of the student, a questionnaire with dichotomous responses was developed. In the validation and reliability, we used the expert judgment criterion and the Kuder-Richarson test respectively, the reliability coefficient obtained in a pilot test applied to 15 students was 0.8359. In the Matlab Scientific Software with the help of the nntool application, the RNA was designed consisting of three hidden layers and an output layer. The ANN during training, validation and testing, registered a weighted correlation coefficient of 0.967190, and a mean square error of 0.05011. The neural model implemented under the given conditions achieved an effectiveness of 88.670% and 98.522%. En el 2018, Perú participó de la Evaluación Internacional de Estudiantes (PISA), donde se evidenciaron resultados desalentadores en los estudiantes de educación básica regular. En tal sentido, se consideró la predicción de resultados académicos como un instrumento de mejora del rendimiento escolar. El objetivo de esta investigación fue predecir el promedio anual de estudiantes del segundo grado de la Institución Educativa N°16093 en la provincia de Jaén-Perú, mediante el diseño e implementación de una red neuronal artificial (RNA). Para la recolección de datos de las variables que influyen en el promedio anual del estudiante, se elaboró un cuestionario de respuestas dicotómicas. En la validación y confiabilidad, se utilizó el criterio de juicio de expertos y la prueba Kuder-Richarson respectivamente, en el cual el coeficiente de confiabilidad obtenido en una prueba piloto aplicado a 15 estudiantes fue de 0.8359. En el software científico Matlab con la ayuda de la aplicación nntool, se diseñó la RNA formada por tres capas ocultas y una capa de salida. La RNA durante el entrenamiento, la validación y la prueba, registró un coeficiente de correlación ponderado de 0.967190, y un error cuadrático medio de 0.05011. El modelo neuronal implementado bajo las condiciones dadas logró una efectividad del 88.670% y 98.522%. Universidad Peruana Unión 2021-12-06 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://apuntesuniversitarios.upeu.edu.pe/index.php/revapuntes/article/view/976 10.17162/au.v12i1.976 Apuntes Universitarios; Vol. 12 Núm. 1 (2022): Revista de Investigación Apuntes Universitarios; 386-403 2304-0335 2225-7136 10.17162/au.v11i5 spa https://apuntesuniversitarios.upeu.edu.pe/index.php/revapuntes/article/view/976/869 Derechos de autor 2021 Dulce Lucero Capuñay Sanchez, Fernando Alain Incio Flores, Ronald Omar Estela Urbina, Luis Montenegro Camacho, Jorge Antonio Delgado Soto, Johnny Cueva Valdivia https://creativecommons.org/licenses/by/4.0 |
| spellingShingle | Academic performance, nntool, artificial neural networks, prediction, education. Rendimiento académico, nntool, redes neuronales artificiales, predicción, educación. Capuñay Sanchez, Dulce Lucero Incio Flores, Fernando Alain Estela Urbina, Ronald Omar Montenegro Camacho, Luis Delgado Soto, Jorge Antonio Cueva Valdivia, Johnny Prediction of academic results with the nntool application in Matlab using artificial neural networks |
| title | Prediction of academic results with the nntool application in Matlab using artificial neural networks |
| title_alt | Predicción de resultados académicos con la aplicación nntool en Matlab utilizando redes neuronales artificiales |
| title_full | Prediction of academic results with the nntool application in Matlab using artificial neural networks |
| title_fullStr | Prediction of academic results with the nntool application in Matlab using artificial neural networks |
| title_full_unstemmed | Prediction of academic results with the nntool application in Matlab using artificial neural networks |
| title_short | Prediction of academic results with the nntool application in Matlab using artificial neural networks |
| title_sort | prediction of academic results with the nntool application in matlab using artificial neural networks |
| topic | Academic performance, nntool, artificial neural networks, prediction, education. Rendimiento académico, nntool, redes neuronales artificiales, predicción, educación. |
| topic_facet | Academic performance, nntool, artificial neural networks, prediction, education. Rendimiento académico, nntool, redes neuronales artificiales, predicción, educación. |
| url | https://apuntesuniversitarios.upeu.edu.pe/index.php/revapuntes/article/view/976 |
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