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Data mining in Learning Management Systems in University Education

Abstract:

This study describes the mining of educational data (MDE) applied to learning management systems (LMS) in order to identify patterns of behavior, which will lead to the choice of resources and more appropriate activities in Moodle. The research is transversal and descriptive - exploratory, using three computer tools such as: Excel 2016, Statistical Program for Social Sciences (SPSS) and Rapidmining 7.5, data from 125 students, monitored in a b-learning course. obtaining the following results, the students connect significantly in the morning schedule, their monthly interaction activity is equitable except the months of beginning and end of the period that are minor, the activities that most interact the student is the revision of tasks, 3 groups were created using the clustering technique and with the use of the average k algorithm, the average performance vector was obtained, obtaining a considerable distance between these groups, concluding that there is a correlation between the level of activity and its academic performance, as It also collects behavioral profiles and compares them with their level of activity, with the aim of creating groups with similar characteristics ensuring that resources and activities are adapted to the needs of students.