Characteristics Of Self-Regulation Of Engineering Students To Predict And Improve Their Academic Performance

This paper describes the adaptation and validation of an instrument aimed to determine self-regulation skills, learning strategies and affective strategies of engineering students from the Tecnologico de Monterrey, Mexico City Campus. A statistical validation with Cronbach's alphas and a social validation in which students indicated whether or not they agree with their results on each dimension were carried out. An online system where students answered an appropriate questionnaire to determine their student-profile was developed. The social validation shows a very good agreement among the profiles obtained with the instrument and the perception of the students. Suitable statistical techniques allow classifying samples of students in different clusters according to their profiles, where members of each cluster have similar profiles. Finally, this instrument, along with additional student academic information, allows to predict their academic performance based on statistical methods, and provides support for instructors to focus their teaching strategies and methods, in order to work and pay attention on those students whose self-regulation dimensions were relatively poor.