Early Dropout Prediction Model: A Case Study of University Leveling Course Students

Iván Sandoval-Palis,Raquel Gilar-Corbi,David Naranjo,Jack Vidal

doi:10.3390/su12229314

Iván Sandoval-Palis, Raquel Gilar-Corbi + Show 2 more

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https://doi.org/10.3390/su12229314

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Abstract

The school-dropout problem is a serious issue that affects both a country’s education system and its economy, given the substantial investment in education made by national governments. One strategy for counteracting the problem at an early stage is to identify students at risk of dropping out. The present study introduces a model to predict student dropout rates in the Escuela Politécnica Nacional leveling course. Data related to 2097 higher education students were analyzed; a logistic regression model and an artificial neural network model were trained using four variables, which incorporated student academic and socio-economic information. After comparing the two models, the neural network, with an experimentally defined architecture of 4–7–1 architecture and a logistic activation function, was selected as the model that should be applied to early predict dropout in the leveling course. The study findings show that students with the highest risk of dropping out are those in vulnerable situations, with low application grades, from the Costa regime, who are enrolled in the leveling course for technical degrees. This model can be used by the university authorities to identify possible dropout cases, as well as to establish policies to reduce university dropout and failure rates.

Highlights

One of the most serious problems facing academic institutions is the high rate of student failure
Since 2017, SENESCYT has included, among the new students entering the Escuela Politécnica Nacional (EPN) leveling course, those students coming from this new admission process [34]
Of the four variables considered to model dropping out of the leveling course through logistic regression, only application grade and regime had statistical importance at a significance level of 5%

Summary

Introduction

One of the most serious problems facing academic institutions is the high rate of student failure. In 1973, Tinto and Cullen defined two categories of dropping out: leaving the college of registration, and failing to obtain any degree [5]. Both definitions cover a wide range of concerns, including economics. As governments make substantial investments in public and community colleges, they need to measure how much they spend on students who drop out during the first year. During the 2008–2009 academic year, U.S taxpayers spent more than USD 900 million on full-time, degree-seeking community college students who dropped out during their first year [6]. Between 2003 and 2008, the U.S invested nearly USD 6.2 billion in colleges and universities to educate students who did not return for a second year. State governments gave more than USD 1.4 billion and the federal government gave more USD 1.5 billion in grants to students who did not return for a second year [7]

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