Descubrimiento de patrones de interacción en cursos MOOC en entornos ONLINE: un enfoque utilizando minería de procesos. Caso de estudio: “Curso de la metodología DICREVOA en Open edX”

Abstract

Massive Open Online Courses (MOOC) open the opportunity for students around the world to access to high quality content, timeless and flexible. However, many people who enroll in a MOOC fail to finish. This is because, on the one hand, studying in these online environments requires students are able to self-regulate effectively for their learning. On the other hand, current MOOC platforms do not provide enough support for students to achieve finish the courses. In a MOOC, where the heterogeneity of the participants is a constant, self-regulated learning it is key to successfully complete them. But not all students effectively use the various self-regulatory strategies during their learning process. To this is added, the individual preference of each student to learn, that is, their learning style. The aim of this study is to explore the sequence of activities that the participants in a MOOC and determine differences in the sequences of activities among participants with different levels of self-regulation and different learning styles. To achieve this, in this study the logs are analyzed with traces of data from a MOOC course and using process mining techniques combined with self-reports, sequence patterns of student activities are extracted. The findings show that participants with different self-regulatory profiles perform sequences similar but with different intensity, while participants with different learning styles perform sequences different of activities when moving in a MOOC. These results may serve to support future decisions regarding the design of the course and platform.