Data Mining and Learning Analytics

CHAPTER 1EDUCATIONAL PROCESS MINING: A TUTORIAL AND CASE STUDY USING MOODLE DATA SETS

Cristóbal Romero¹, Rebeca Cerezo², Alejandro Bogarín¹, and Miguel Sánchez‐Santillán²

¹ Department of Computer Science, University of Córdoba, Córdoba, Spain

² Department of Psychology, University of Oviedo, Oviedo, Spain

The use of learning management systems (LMSs) has grown exponentially in recent years, which has had a strong effect on educational research. An LMS stores all students’ activities and interactions in files and databases at a very low level of granularity (Romero, Ventura, & García, 2008). All this information can be analyzed in order to provide relevant knowledge for all stakeholders involved in the teaching–learning process (students, teachers, institutions, researchers, etc.). To do this, data mining (DM) can be used to extract information from a data set and transform it into an understandable structure for further use. In fact, one of the challenges that the DM research community faces is determining how to allow professionals, apart from computer scientists, to take advantage of this methodology. Nowadays, DM techniques are applied successfully in many areas, such as business marketing, bioinformatics, and education. In particular, the area that applies DM techniques in educational settings is called educational data mining (EDM). EDM deals with unintelligible, raw educational data, but one of the core goals of this discipline—and the present chapter—is to make this ...