![]() The novelty of this work is to use student performance data and statistical analysis of online surveys to reveal patterns that can help reduce drop-out rates and transform the educational process, under extenuating and imposed distance learning circumstances. Reliability analysis was also performed and ANOVA (analysis of variance) was applied to clusters. Statistical analysis was performed and discussed in this paper including correlation analysis, factor analysis, and clustering. ![]() 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. 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 widespread adoption of distance learning has led instructors to form new digital learning environments and methods. ![]() ![]() This forced the transition from traditional education to fully distance learning environments for all levels of education. The COVID-19 pandemic has challenged many educational institutions around the world in 20 as traditional education has been interrupted to prevent the spread of the virus.
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