Universities are committed to offering quality education; however, a high rate of academic failure is often observed in the first year of studies. Considering the impact that motivation and emotional aspects can have on students’ commitment to study and therefore on their academic performance, achievement, and well-being, this study aims to identify the factors associated with academic success or failure in 1071 students entering the National Polytechnic School (Quito, Ecuador). The data were compiled from the existing computer records of the university with the permission of the responsible administrative staff. A predictive model has been used and a binary logistic regression analysis was carried out through the step-forward regression procedure based on the Wald statistic to analyze the predictive capacity of the variables related to emotional intelligence, motivational and self- regulated socio-cognitive skills, goal orientation, and prior academic achievement (measured by university entrance marks and through a knowledge test carried out at the beginning of the university academic year). To determine the cut-off point for the best discriminatory power of each of the variables, a Receiver Operating Characteristics (ROC) curve analysis has been used. The results indicate that the variables that are significant in the prediction of academic success or failure are the two academic performance measures: the emotional attention variable, and the performance-approach goals and the motivational self-efficacy variable. Additionally, the highest predictive power is displayed by the prior academic performance measure obtained through the knowledge test conducted at the beginning of the university course.

This paper addresses the relationship between student evaluation of teaching (SET) and academic achievement in higher education. Meta-analytic studies on teaching effectiveness show a wide range of results, ranging from small to medium correlations between SET and student achievement, based on diverse methodological approaches, sample size studies, and contexts. This work aimed to relate SET, prior academic achievement, and academic achievement in a large sample of higher education students and teachers, using different methodological procedures, which consider as distinct units of analysis the group class and the individuals, the variability between students within classes, and the variability between group-class means, simultaneously. The data analysis included the calculation of group-class means and its relationship with the group-class mean academic achievement, through correlation and hierarchical regression techniques; additionally, a multilevel path analysis was applied to the relationship between prior academic achievement, SET, and their academic achievement, considering the variability among group classes. A multisection analysis was also carried out in those course disciplines in which there was more than one class group (section). The results of individual and group-class analysis revealed that SET was moderately low but related to academic achievement in a significant way once the effect of previous academic achievement was controlled. In addition, multilevel path analysis revealed the effect of SET on achievement, both within and between group-class levels. The results of the analysis carried out in the course disciplines with different sections, according to a multisection design, yielded similar results to the individual and aggregated data analyses. Taken together, the results revealed that SET was low related to academic achievement, once the effect of previous academic achievement was controlled. From these results, it follows that the use of SET as a measure of teachers’ effectiveness for making administrative decisions remains controversial.

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.

The general purpose of this work is 2-fold, to validate scales and to present the methodological procedure to reduce these scales to validate a rating scale for the student evaluation of teaching in the context of a Polytechnic Higher Education Institution. We explored the relationship between the long and short versions of the scale; examine their invariance in relation to relevant variables such as gender. Data were obtained from a sample of 6,110 students enrolled in a polytechnic higher education institution, most of whom were male. Data analysis included descriptive analysis, intraclass correlation, exploratory structural equation modeling (ESEM), confirmatory factorial analysis, correlations between the short and long form corrected for the shared error variance, gender measurement invariance, reliability using congeneric correlated factors, and correlations with academic achievement for the class as unit with an analysis following a multisection design. Results showed four highly correlated factors that do not exclude a general factor, with an excellent fit to data; configural, metric, and scalar gender measurement invariance; high reliability for both the long and short scale and subscales; high short and long-form scale correlations; and moderate but significant correlations between the long and short versions of the scales with academic performance, with individual and aggregate data collected from classes or sections. To conclude, this work shows the possibility of developing student evaluation of teaching scales with a short form scale, which maintains the same high reliability and validity indexes as the longer scale.

The COVID-19 pandemic has changed the daily lives of all people worldwide, affecting not only society but also various sectors such as finance, tourism, etc. To counteract the pandemic, measures are required to detect contagions and take the necessary actions to prevent the virus spread. In this work, a Transfer Learning approach has been used to model COVID-19 coughs as a previous step to the diagnosis of the illness. The data set of the University of Cambridge, ComParE 2021 COVID-19 Cough Sub-Challenge, has been used, which consists of 725 samples of cough sounds from 397 people of which 119 have been diagnosed with positive COVID-19, besides, a data augmentation technique has been used to balance the data set. This work evaluates the performance of the pre-trained VGGish model for the classification of the audio cough signals as COVID or Not COVID cough. For this purpose, the VGGish model is used as a feature extractor and a convolutional neural network provides the final classification of the cough recordings to determine whether they are COVID-19 positive or negative. Despite the difficulty of the task, optimum results have been founded to detect negative cases obtaining up to 81% of precision. Considering the Unweighted Average Recall (UAR) as metric, the methodology proposed in this work has obtained an improvement up to 3% comparing to OpenSmile technique when the same database has been used.

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.