Evaluación por inteligencia artificial a un instrumento de estrategias de aprendizajes en estudiantes universitarios ecuatorianos

Currently, the learning strategies used by university students are very diverse and accompanied by technology. This study applies machine learning to evaluate the instrument for measuring learning strategies in university students. Correctional techniques were used, using univariant and multivariate statistics. In addition, the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was used. The instrument was applied to a sample of 984 students from the Pontifical Catholic University of Ecuador, Santo Domingo, from different undergraduate courses. The endogenous variables were Scale I and II; while the explanatory strategies were: motivational, metacognitive, process elaboration, context control and search strategies. The results allowed us to determine a high reliability of the questions, while the factor loadings of the constructs and the endogenous variables allowed us to verify that the indices are specific to each component of the learning strategies.