Underlying variables in the understanding of movement in engineering students

Abstract

This article describes the understanding of motion by active students taking Newtonian physics for engineering, supported by active learning, during the pandemic due to COVID-19; in addition, an unsupervised predictive model of learning achievement was constructed from variables identified using the principal component analysis technique on the responses. the instrument used is the modified test of understanding graphs-kinematics comprehension. students from two universities in Bogotá, Colombia participated. The results show a lower level of accuracy in students in remote face-to-face mode, compared to the reference group of physical presence; by way of reflection, the forced educational experiment implies resizing the teaching activity in the teaching and learning of movement.