Technology Trends

Botto-Tobar, M.; Pizarro, Guillermo; Zúñiga-Prieto, Miguel; D'Armas, Mayra; Sánchez, Miguel Zúñiga

doi:10.1007/978-3-030-05532-5

Cited by 7 publications

(1 citation statement)

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“…However, despite the fact that the ANN model proposed in this study makes predictions at the stage in which students are just entering university, its application is an advantage in terms of carrying out early academic interventions, since dropout rates are higher during the first years of university [47].…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Resultsmentioning

confidence: 99%