Implementación de modelo predictivo para identificar deserción estudiantil en UNITEC
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Universidad Tecnológica Centroamericana UNITEC
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Esta investigación se realizó con el objetivo de implementar un modelo predictivo para identificar los estudiantes con riesgo de deserción de un periodo académico a otro en la Universidad Tecnológica Centroamericana (UNITEC) en estudiantes de pregrado de las ciudades de Tegucigalpa y San Pedro Sula. En el estudio se realizaron procesos de análisis de datos, procesamiento de datos como limpiezas y transformaciones, se entrenó el algoritmo de aprendizaje y se evaluaron los resultados en función de cada uno de los indicadores de eficiencia, una vez realizado el entrenamiento se llevó a cabo el proceso de implementación, todo ello con el fin de que el modelo predictivo sea una alerta temprana para la universidad para mitigar la deserción, mejore el enfoque de esfuerzos y recursos y aumente los índices de retención.
This research was conducted with the aim of implementing a predictive model to identify students at risk of dropping out from one academic period to another at the Universidad Tecnológica Centroamericana (UNITEC) among undergraduate students in the cities of Tegucigalpa and San Pedro Sula. The study involved data analysis processes, data preprocessing such as cleaning and transformations, training of the learning algorithm, and evaluation of results based on each of the efficiency indicators. Once the training was completed, the implementation process was carried out, all with the goal of making the predictive model an early alert for the university to mitigate dropout, improve the focus of efforts and resources, and increase retention rates.
This research was conducted with the aim of implementing a predictive model to identify students at risk of dropping out from one academic period to another at the Universidad Tecnológica Centroamericana (UNITEC) among undergraduate students in the cities of Tegucigalpa and San Pedro Sula. The study involved data analysis processes, data preprocessing such as cleaning and transformations, training of the learning algorithm, and evaluation of results based on each of the efficiency indicators. Once the training was completed, the implementation process was carried out, all with the goal of making the predictive model an early alert for the university to mitigate dropout, improve the focus of efforts and resources, and increase retention rates.