Machine learning: An application for School Dropout prevention
DOI:
https://doi.org/10.33448/rsd-v14i6.49029Keywords:
Retention, Dropout, Machine learning, Naive bayes, Logistic regression.Abstract
The retention of students in mathematics subjects continues to be a major concern for educational institutions and is a multifaceted challenge, since it directly influences the effectiveness of the teaching process and, consequently, encourages school dropouts. The aim of this study is to present an application of machine learning to prevent school drop-outs. The aim of this research is to discern the probabilities associated with passing or failing students enrolled in mathematics subjects in the first semester of school, based on historical records of academic performance at the Federal Institute of Ceará (IFCE), Fortaleza campus. To this end, Machine Learning algorithms were applied to indicate the probability of a student passing or failing mathematics in the first semester, generating reports to help decision making by the coordinators of the courses in which this subject is taught. The Logistic Regression and Naive Bayes algorithms will be used. To do this, libraries available for the Python language will be used, namely Scikit-learn. The expected result is that the Machine Learning algorithms will provide insights into preventive academic pedagogical and managerial actions aimed at reducing failure rates and, consequently, school dropout.
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