A Real-Life Machine Learning Experience for Predicting University Dropout at Different Stages Using Academic Data

Abstract

High levels of school dropout are a major burden on the educational and professional development of a country's inhabitants. A country's prosperity depends, among other factors, on its ability to produce higher education graduates capable of moving a country forward. To alleviate the dropout problem, more and more institutions are turning to the possibilities that artificial intelligence can provide to predict dropout as early as possible. The difficulty of accessing personal data and privacy issues that it entails force the institutions to rely on the Academic Data of their students to create accurate and reliable predictive systems. This work focuses on creating the best possible predictive model based solely on academic data, and accordingly, its capacity to infer knowledge must be maximised. Thus, Feature Engineering and Instance Engineering techniques such as dealing with redundancy, significance of the features, correlation, cardinality features, missing values, creation or elimination of features, data fusion, removal of unuseful instances, binning, resampling, normalisation, or encoding are applied in detail before the construction of well-known models such as Gradient Boosting, Random Forest, and Support Vector Machine along with an Ensemble of them at different stages: prior to enrolment, at the end of the first semester, at the end of the second semester, at the end of the third semester, and at the end of the fourth semester. Through the construction of these predictive models that serve as inputs to a decision support system, the application of effective dropout prevention policies can be applied.

Forecasting Students Dropout: A UTAD University Study

“…Fernádez-García et al [10] defined several models from enrollment up to the fourth semester using mainly academic data. The approach considered the output of previous stages, i.e., each step assumed the prior knowledge generated.…”

“…Usually, the other data was not registered by UTAD's Staff. Therefore, the working dataset curricular units (courses) are identified with the acronym CU in Table 2 and its marks ranges [10,20] if the students succeeds or 0 if fails. Figure 1 plots the pairwise relationships between the most important curricular units obtained in Section 3.6.…”

Predicting Student-Teachers Dropout Risk and Early Identification: A Four-Step Logistic Regression Approach

“…Students' dropout is a complicated issue in the learning process, with its attendant negative implications on students, academic institutions, economic resources, and society at large [1,2]. It is a serious problem in both developed and developing countries, but it is even more so in the leastdeveloped economies [3,4].…”