Nowadays, the issue of student drop-out is addressed not only through the prism of pedagogy, but also by technological practices. In this paper, we demonstrate how a student drop-out could be predicted through a student’s performance using different Machine Learning techniques, i.e., supervised learning and unsupervised learning. The results show that various types of student engagement are essential factors in predicting drop-out and the final ECTS points achievements.
Machine Learning Model for Student Drop-Out Prediction Based on Student Engagement
Nalli G.;De Leone R.;
2023-01-01
Abstract
Nowadays, the issue of student drop-out is addressed not only through the prism of pedagogy, but also by technological practices. In this paper, we demonstrate how a student drop-out could be predicted through a student’s performance using different Machine Learning techniques, i.e., supervised learning and unsupervised learning. The results show that various types of student engagement are essential factors in predicting drop-out and the final ECTS points achievements.File in questo prodotto:
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