The objective of this research is to reduce the dropout rate of students in the Faculty of Systems Engineering and Informatics of the Universidad Nacional Mayor de San Marcos – FISI-UNMSM, through the implementation of an intelligent system with a data mining approach and the autonomous learning algorithm (decision trees) that predicts which students are at risk of dropping out. It was developed in Python and the free software Weka, for this purpose student data was collected from 2014 to 2020. This solution increases the availability and the level of satisfaction of the faculty; in the learning process, an accuracy percentage of 90.34% and precision of 95.91% was obtained, so the data mining model is considered valid. In addition, it was found that the variables that most influenced students in making the decision to abandon their studies were the historical weighted average, the weighted average of the last cycle and the number of credits passed.
Según los compendios estadísticos publicados en la página web de Universidad Nacional Mayor de San Marcos por la Oficina General de Planificación, en la facultad de Ingeniería de Sistemas e Informática la cantidad de titulados por la modalidad de sustentación de tesis es inferior al 20% de la cantidad total de titulados, esto significa que más de 80% se titula mediante otras modalidades no vinculantes a la investigación. La consecuencia es la baja cantidad de tesis producidas, por ello, el objetivo de la presente investigación es incrementar el porcentaje de titulados por tesis en la facultad de Ingeniería de Sistemas e Informática mediante un apoyo extraordinario, por parte de los docentes, en las asesorías de los trabajos de investigación de los estudiantes. El proyecto se implementó durante los años 2015 y 2016 y como resultado se obtuvo un incremento del 72% de titulados por la modalidad de sustentación de tesis.
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