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Diálogos sobre educación. Temas actuales en investigación educativa

versão On-line ISSN 2007-2171

Resumo

RICO PAEZ, Andrés; GAYTAN RAMIREZ, Nora Diana  e  SANCHEZ GUZMAN, Daniel. Construction and implementation of a model to predict the academic performance of university students using the Naïve Bayes algorithm. Diálogos sobre educ. Temas actuales en investig. educ. [online]. 2019, vol.10, n.19, 00011. ISSN 2007-2171.  https://doi.org/10.32870/dse.v0i19.509.

One of the most widely used applications of educational data mining is predicting academic performance. The aim of this paper is to present the construction, evaluation and implementation of a predictive model of the academic performance of university students by means of the data mining technique known as the Naïve Bayes algorithm. We collected data from 122 students as training for the algorithm and applied the model to predict the academic performance of 71 students. The results show that, in addition to obtaining predictions of academic performance, the predictive model also identifies the factors that influence it the most. This type of study allows teachers to design prevention strategies and identify students who are vulnerable to failure.

Palavras-chave : prediction; academic performance; data mining; predictive model; Naïve Bayes algorithm.

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