Predicting Computer Engineering students' dropout in Cuban Higher Education with pre-enrollment and early performance data

Abstract

We present an educational data analytics case study aimed at the early detection of potential dropout in Computer Engineering studies in Cuba. We have employed institutional data of 456 students and performed several experiments for predicting their permanency into three (promotion, repetition, and dropout) or two classes (promoting, not promoting). We have also tested a combination of classification features for training and testing decision trees and neural networks; including information obtained at the time of enrollment, after the first semester and after the first academic year. Our results show a considerable accuracy using all features (96.71%). Using only the features available at the time of enrolment and after the first semester we obtain very positive results (68.86% and 93.85% accuracy respectively) with a high recall of non-promoting students. Thus, it is possible to obtain an early assessment of the risk of dropout that can help defining prevention policies.

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

“…Thus, understanding the causes of higher education dropout is essential to eliminate or at least minimise them as much as possible. However, this is a task of great complexity since dropping out is a multifaceted phenomenon in which heterogeneous variables are implicit [3].…”