Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic

Abstract

The COVID-19 pandemic has challenged many educational institutions around the world in 2020 and 2021 as traditional education has been interrupted to prevent the spread of the virus. This forced the transition from traditional education to fully distance learning envi-ronments for all levels of education. The widespread adoption of distance learning has led instructors to form new digital learning environments and methods. In response to this unexpected situation, data regarding engineering students and their interaction with the learning environment was accumulated and processed, generating a matrix of 129 × 165 variables. The motivation for this research is to identify new variables that impact student performance during the disorientation of the educational process due to the COVID-19 pandemic. Statistical analysis was performed and discussed in this paper including correla-tion analysis, factor analysis, and clustering. Reliability analysis was also performed and ANOVA (analysis of variance) was applied to clusters. The novelty of this work is to use student performance data and statistical analysis of online surveys to reveal patterns that can help reduce dropout rates and transform the educational process, under extenuating and imposed distance learning circumstances. A major finding is that by applying innovative teaching methods, thereby meeting the challenge of an imposed distance learning environ-ment, students' spatial conceptions improve, overcoming the absence of a physical learning space. Deep insights for individual students were discovered, as well as significant relation-ships between students' transition from secondary to higher education and their understand-ing of geometric features. Evidence of the effectiveness of the online learning framework that was integrated showed that it positively influenced students' learning styles.