Model based on machine learning to predict the approval of an engineering course

Abstract

In the context of virtual and distance education, the course "Introduction to Telecommunications Engineering" at the National Open and Distance University (UNAD) has faced the challenge of student attrition at first enrolment. To address this problem, a research study was carried out to develop a Data Mining model based on the CRISP-DM methodology. The study used a dataset with 808 student records collected in different academic periods of the year 2021. Several Machine Learning techniques were applied, such as Random Forest, Tree Decision, Knn, SVM and Neural Network, evaluating their performance through k-10 cross-validation. The results revealed that the Random Forest model obtained the best performance, predicting with 80% accuracy the passing rate of the course. This model is relevant for identifying students at risk of dropping out of the course early, allowing the implementation of proactive strategies to address dropout and improve course indicators. Although some results were slightly lower than previous research, the model remains a valuable tool to support student retention strategies in the "Introduction to Telecommunications Engineering" course at UNAD.