Seguimiento del aprendizaje mediante la minería de datos educativos

Abstract

This Educational data mining focuses on creating methods to explore the unique types of data that come from educational environments to generate solutions and strategies at different levels:pedagogical, curricular, management, and institutional. This work aims to contribute to the predictive model for academic monitoring using educational data mining. In this project, the PCN method was developed in a quasi-experimental design, in which academic learning can be quantified through free written comments.This allows teachers to obtain a visualization of trends for future classes focused on personalized teaching. Education is not only based on students acquiring knowledge, but also on the way they interact, idealize, reflect, and learn throughout their education. It is concluded that it is essential to contribute to improving the student experience in a higher education course, as well as their expectations for their academic career, but above all, to see the opportunity to obtain information about the process that the student carries out according to the analysis of their learning, in such a way that factors that positively affect the pattern of behavior, their commitment and their performance are determined.