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.
Since 2017, Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT), has included, among the new students entering the Leveling Course of Escuela Politécnica Nacional (EPN), those from Affirmative Action population segment. These students tend to show low academic performance and high dropping out rates.
Mathematics, of considerable importance for engineering training, is the subject that shows the lowest indicator of academic performance during high school, for this reason, EPN in response to this problem, proposes a Mathematics Pilot Program for students of engineering from vulnerable groups. To this end, it was conducted a descriptive analysis of the information of students enrolled in the Leveling Course between the semesters 2017-A and 2018-A, and based on the criteria of the teachers of the Leveling Course, a diagnostic test was designed and applied to the new students of the 2018-B semester, this test evaluated only basic Mathematics.
It was determined that the students from Affirmative Action group presented low passing rate and high dropping out rate during the semesters under study, likewise, students of Affirmative Action group got a low average score in the diagnostic test which evidences poor academic level of these students prior to entering the Leveling Course. In this sense, it is proposed that the pilot program should be structured in 6 units, with special emphasis on Geometry and Trigonometry, and real-valued functions. It is recommended that the pilot program should be implemented as a course prior to the Leveling Course, so that the Government's Affirmative Action program fulfills its mission of effectively including students from vulnerable groups in the Higher Education System
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