Modelo multivariado de avaliação da aprendizagem em ensino superior remoto emergencial

Abstract

Before the pandemic of COVID-19, university managers had been showing interest in identifying factors that lead individuals to learn better, or to drop out of a course, or even to fail a subject. Finding answers to these questions becomes more evident when it comes to undergraduate Engineering courses, since these have high dropout rates. However, higher education institutions still lack protocols or, at least, indicators or instruments that allow managers to know these factors and the underlying problems in order to act preventively or make decisions. After the adoption of Emergency Remote Learning - ERE, and facing the uncertainties and new challenges of online education, the absence of this information can further compromise the quality of the offer of new distance education actions. Moreover, the ignorance of these factors makes it impossible to make a current diagnosis about the benefits and losses that the pandemic has brought to Engineering students. This thesis, therefore, seeks to identify which factors - here denominated predictor variables - impacted the learning processes of undergraduates who took Calculus 2, one of the curricular components with the highest retention rates in the initial semesters of Exact Science courses. To this end, a predictive multivariate model was proposed and tested. The research was conducted at a Brazilian federal public university in the second semester of 2020. A total of 507 individuals participated in the study, representing 51% of the target population. Primary data (three psychometric scales measuring students' psychosocial and contextual variables, as well as variables referring to teachers' teaching procedures) and secondary data (official documents of the institution involved) were used. Summative evaluation was performed, with after-the-fact analysis of results. Multivariate statistical techniques and methodological procedures based on psychometrics were used. After the analysis, based on the proposed model, the predictors that significantly impacted learning in Calculus 2 were: the family income variable, the selfregulatory and cognitive learning strategies variable, and the instructional events variable, which refers to the learning conditions provided by teachers during the academic semester. The multivariate model of this thesis is replicable and can guide managers in future decisions about the offer of remote courses and subjects, in any field of knowledge. The originality of this work is marked, above all, by the discovery of new variables that may compose future psychometric scales to assess learning outcomes of Engineering students in any discipline.