Academic behavior analysis in virtual courses using a data mining approach

Abstract

Virtual education is one of the educational trends of the 21st century; however knowing the perception of students is a new challenge. This article presents a proposal to define the essential components for the construction of a model for the analysis of the records given by the students enrolled in courses in a virtual learning platform (VLE). The article after a review of the use of data analytics in VLE presents a strategy to characterize the data generated by the student according to the frequency and the slice of the day and week that access the material. With these metrics, clustering analysis is performed and visualized through a map of self-organized Neural Networks. The results presented correspond to five courses of a postgraduate career, where was found that students have greater participation in the forums in the daytime than in the nighttime. Also, they participate more during the week than weekends. These results open the possibility to identify possible early behaviors, which let to implement tools to prevent future desertions or possible low academic performance.