Assessment academic performance in online courses: a multivariate model

Abstract

Grades and other student academic indicators are the most evaluated parameters in studies about teaching and learning. However, several authors point out that these indexes, alone, are not enough to explain the students' performance. To understand the predictors that facilitate or compromise learning in higher education, it is neces-sary to consider other variables related to students, teachers, and the teaching con-text. Thus, this paper presents a multivariate model for assessing student performance in online courses. The model was tested in the context of the COVID-19 pandemic with undergraduate students in Engineering. The course was Calculus 2. Data were analyzed using multivariate statistical analysis techniques. Among the 10 variables tested in the model. Three variables were significant and showed that the students' performance in Calculus 2 was impacted by: family income, cognitive and self-regulatory learning strategies, and teachers' instructional events. The main con-tribution of the study is the construction of a multivariate model that can be replicate in other contexts. In practices, professors and managers will have inputs to better plan the disciplines and avoid increasing retention and dropout rates.