Citas bibligráficas
Cevallos, E., Barahona, C. (2021). Modelo para automatizar el proceso de predicción de la deserción en estudiantes universitarios en el primer año de estudio [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/656740
Cevallos, E., Barahona, C. Modelo para automatizar el proceso de predicción de la deserción en estudiantes universitarios en el primer año de estudio [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2021. http://hdl.handle.net/10757/656740
@misc{renati/396231,
title = "Modelo para automatizar el proceso de predicción de la deserción en estudiantes universitarios en el primer año de estudio",
author = "Barahona Chunga, Claudio Jorge",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2021"
}
This research proposes a model for the automation of prediction of university student dropout. This research arises from an existing problem in the Peruvian educational sector: university student dropout; that is, those university students who partially or permanently abandon their studies. The purpose of the research is to provide a solution that contributes to reducing the university dropout rate, applying predictive analysis technologies and data mining, which detects in advance students with the possibility of dropping out of their studies, thus providing educational institutions with greater visibility and opportunities. of action before this problem. A predictive analysis model was designed, based on the analysis and definition of 15 prediction variables, 3 phases and the application of prediction algorithms, based on the Educational Data Mining (EDM) discipline and supported by the IBM SPSS Modeler platform. To validate, the application of 4 prediction algorithms was evaluated: decision trees, Bayesian networks, linear regression, and neural networks; in a study at a university institution in Lima. The results indicate that Bayesian networks perform better than other algorithms, compared under the metrics of precision, accuracy, specificity, and error rate. Particularly, the precision of Bayesian networks reaches 67.10% while for decision trees (the second-best algorithm) it is 61.92% in the training sample for the iteration with a ratio of 8: 2. In addition, the variables "sports person" (0.29%), "own home" (0.20%) and "high school grades" (0.15%) are the ones that contribute the most to the prediction model.
Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons