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The failure and dropout of university studies are issues that worry all nations due to the personal, social, and economic costs that this they entail. Because the dropout phenomenon is complex and involves numerous factors, to reverse it would involve a comprehensive approach through interventions aimed at the factors identified as key in the decision to drop out. Therefore, the main objective of this work is to determine the profile of students who enter the EPN (STEM higher-education institution) to analyze the characteristics that differentiate students who drop out early in their career and those who stay in school. A sample of 624 students who accessed the EPN leveling course (a compulsory course at the beginning of their studies) participated in the study. A total of 26.6% of the participants were women. A total of 50.7% of the participants passed the course. Data referring to social, economic, and academic variables were analyzed. Comparison techniques, as well as artificial neural networks, were used to compare characteristic profiles of students who passed the leveling course and those who dropped out. The results showed significant differences between the profiles of the students who passed and those who dropped out with regard to the variables related to previous academic performance and motivational and attributional aspects. The artificial neural networks corroborated the importance of these variables in predicting dropout. In this research, the key variables predicting whether a student continues or leaves higher education are revealed, allowing the identification of students at possible risk of dropping out and thus promoting initiatives to provide adequate academic support and improve student retention. This research was supported by National Secretariat of Higher Education, Science and Technology, (SENESCYT;PIC-18-INE-EPN-002).

In recent decades, efforts have been made to achieve a positive coexistence among adolescents in secondary schools and create a healthy environment to prepare them to face the present-day challenges. Therefore, this study highlights the educational purpose of improving emotional management and social skills as well as decreasing antisocial and criminal behaviour among secondary education students through an educational training programme. Accordingly, to verify the effectiveness of the project, a quasi-experimental design with a pre-test/post-test structure and a control group was adopted. To achieve this, a total of 141 Spanish secondary school students participated in this study and were randomly assigned to one of two experimental conditions. The first (experimental) group (n = 55) participated in the training programme, correspondingly, the second group (control) (n = 57) followed the usual mentoring activities planned for the entire educational centre. Of the total number of participants, 52.7% of the sample were men and 47.3% were women. The mean age of the participants was 13.01 years old (SD = 0.935). The results showed improvements in the environment with adequate training and the correct application of a programme involving emotional intelligence (EI) among secondary education students. Furthermore, a decrease in conflicts and enhanced relations between the members of the educational community was evidenced. Finally, the practical implications for improving coexistence in secondary schools are discussed.

In Ecuador, affirmative action policies enable students from vulnerable groups to preferentially enter universities. However, these policies are limited to admission and do not include academic or socio-economic support mechanisms that, according to the literature, promote student insertion in the higher education system. In this study, the effects of socio-academic intervention on the academic performance of vulnerable students are presented. For this, 41 students were selected among 164 vulnerable students entering the Escuela Politécnica Nacional in the second term of 2019. The 41 students attended a socio-academic intervention course for one term, while the remaining 123 attended the Escuela Politécnica Nacional levelling course directly. Once both groups of students finished the levelling course, their performance in each of the course subjects was compared. The results showed that the academic performance of the students in the intervention was significantly higher in mathematics and geometry compared to the students who had no intervention. These results show that the socio-academic intervention promotes the real insertion of vulnerable students in the university system. This research was supported by the Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT; PIC-18-INE-EPN-002).

The school-dropout problem is a serious issue that affects both a country&rsquo, 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 Polit&eacute, cnica 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&ndash, 7&ndash, 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.