A model recommends best machine learning algorithm to classify learners based on their interactivity with moodle

In big data universities, an understanding of how the individual learning style and preferences interacts with the instructional medium presented is needed. In this study we examined the VARK learning style inventory using the variable-centered, person-centered and social approaches. We worked on a big “data set” which encompasses two data sources the first was LMS while the second was social media portals associated with that LMS. In order to make classification as well as prediction for the learner´s learning style LS, we applied “WEKA” as we established a comparative analysis among different machine learning algorithms in order to know which one is the fit for the used “data set”.