Seguimiento del egresado con técnicas de minería de datos: caso facultad de ingeniería pesquera de la Universidad Nacional san Luis Gonzaga periodo 2017-2021
Fecha
2025
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Universidad Nacional San Luis Gonzaga
Resumen
El Objetivo: Evaluar la información de los egresados de la facultad de Ingeniería Pesquera de la
universidad nacional San Luis Gonzaga utilizando minería de datos para el periodo 2017-2021. La
metodología utilizada del tipo aplicada, no experimental, de corte transversal retrospectivo, para lo
cual se utilizó la Metodología CRISP-DM (Cross-Industry Standard Process for Data Mining),
aplicando el algoritmo para aprendizaje no supervisado K-Means, del software Orange Data Mining.
siguiendo cada una de sus fases. Los resultados el tiempo de duración de la carrera profesional, están
entre los tiempos establecidos de 5 años, con una edad media de egreso entre 23.41-23.44 para ambas
escuelas pesquería y alimentos. Otros resultados, muestran que han obtenido su bachiller solo el
69.88% de la escuela de alimento y 77.46% de la escuela de pesquería siendo este valor muy reducido,
considerando que el bachiller es automático. Más se agudiza con los titulados, solo el 22.41% de la
escuela de alimentos y 16.42% de la escuela de pesquería. La información de las variables sobre el
trabajo de los egresados es muy escasa por lo cual no se pudo realizar el análisis. Concluyendo que
el software de minería de datos aporta importantes resultados para la facultad de ingeniería pesquera
que mejorar sus procesos de bachillerato y titulación, así como el registro de la condición laboral de
cada egresado, bachiller o titulado que es muy escasa.
The Objective: To evaluate the information of the graduates of the Faculty of Fisheries Engineering of the Universidad Nacional San Luis Gonzaga national university using data mining for the period 2017-2021. The methodology used was applied, non-experimental, retrospective cross-sectional, for which the CRISP-DM Methodology (Cross-Industry Standard Process for Data Mining) was used, applying the algorithm for unsupervised learning K-Means, of the Orange Data Mining software, following each of its phases. The results show that the duration of the professional career is within the established time frame of 5 years, with an average age of graduation between 23.41-23.44 for both the fishery and food schools. Other results show that only 69.88% of the food school and 77.46% of the fishery school have obtained their bachelor's degree, which is very low, considering that the bachelor's degree is automatic. It is even more acute with the graduates, only 22.41% of the food school and 16.42% of the fishery school. The information of the variables on the work of the graduates is very scarce, so the analysis could not be carried out. In conclusion, the data mining software provides important results for the faculty of fisheries engineering to improve its baccalaureate and degree processes, as well as the record of the employment status of each graduate, baccalaureate, or degree holder, which is very scarce.
The Objective: To evaluate the information of the graduates of the Faculty of Fisheries Engineering of the Universidad Nacional San Luis Gonzaga national university using data mining for the period 2017-2021. The methodology used was applied, non-experimental, retrospective cross-sectional, for which the CRISP-DM Methodology (Cross-Industry Standard Process for Data Mining) was used, applying the algorithm for unsupervised learning K-Means, of the Orange Data Mining software, following each of its phases. The results show that the duration of the professional career is within the established time frame of 5 years, with an average age of graduation between 23.41-23.44 for both the fishery and food schools. Other results show that only 69.88% of the food school and 77.46% of the fishery school have obtained their bachelor's degree, which is very low, considering that the bachelor's degree is automatic. It is even more acute with the graduates, only 22.41% of the food school and 16.42% of the fishery school. The information of the variables on the work of the graduates is very scarce, so the analysis could not be carried out. In conclusion, the data mining software provides important results for the faculty of fisheries engineering to improve its baccalaureate and degree processes, as well as the record of the employment status of each graduate, baccalaureate, or degree holder, which is very scarce.
Descripción
Palabras clave
Minería de datos, Metodología CRISP-DM, Seguimiento de egresados, Data mining
