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Student Dropout Model Based on Logistic Regression

Abstract:

Student dropout is a phenomenon that affects the majority of higher education institutions in Ecuador. The objective of the research was to design a predictive model to detect possible dropouts before they decide to abandon their studies. This model is based on logistic regression, and the methodology used in this research is based on the Knowledge Discovery in Databases (KDD) Model; which has five stages: selection, processing, transformation, data mining and evaluation. The application of the Logit function of the R tool for the logistic regression helps the construction of the predictive model. This model evaluates possible dropout students and leads to the conclusion that grades have a greater influence on student dropout.