Abstract:

Learning management systems (LMS) provide education with tools that help manage resources and log activities as well as evaluation in the development and fulfillment of tasks by students. The tools that contribute to LMS have become a subject of analysis by the areas of learning management that are part of educational institutions. The analysis starts from a large amount of data generated in the LMS, these data arouse the interest of institutions to look for patterns that help teachers to customize resources and improve education. Several of the proprietary or free LMS platforms have created modules or plugins with the ability to generate reports based on the available data of the activities performed. These modules have, to a certain extent, covered learning assessment needs. However, to make decisions for the improvement of e-learning, a broader analysis of the data is necessary with the use of tools that suitably fit the different LMS platforms. The answer to this process is given by data mining, its characteristics allows to apply several methods and algorithms to the data where individuals share patterns or even draw projections of behavior.

Once the appropriate model-driven development (MDD) tool has been established, the process will be analyzed in a case study focused on the Moodle platform. The case study was done to a group of students from a University of Ecuador, specifically in a computer course. In this analysis we can find patterns in students' performance, applying a search algorithm. With these results the student can be offered a personalized education or create methods to improve e-learning.

In this paper, section II presents several concepts necessary to approach the problem from previous work; section III details the pre-processing step required for the analysis of the data using the main techniques of data mining; section IV performs an analysis of the results obtained; finally section V presents the conclusions and makes recommendations for future investigations.

Keywords:

Data mining, LMS, e-learning.