INTRODUCTION OF EDUCATIONAL DATA MINING BY USING A VARIETY OF TECHNIQUES IN ORDER TO ACHIEVE THE GOAL FROM THE MOODLE LMS

Ali Akhtar, Mohammad Serajuddin, Hasan Zafrul

Abstract


Different works relating to this specialty have been done in recent years and several data extraction approaches have been used to solve numerous educational problems. This analysis compares the Felder-Silverman Learning Style Model component of student activity in Moddle class with three data mining algorithms for the identification of knowledge presentation dimension (visual/verbal) learning style. This study analyzes Moodle LMS student log data using data mining strategies to identify their learning styles that rely on one aspect of the learning style of Feld-Silverman: visual/verbal. The WEKA compares various classification algorithms as classified J48 Decision Tree, Naive Bayes and Portion. The selected classifiers were evaluated using a 10-fold cross validation. The tests revealed that at 71.18 percent the Naive Bays achieve the strongest score.

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Keywords


data mining, educational data mining, techniques, Moodle LMS, Weka, Felder- Silverman learning style model

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References


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DOI: http://dx.doi.org/10.46827/ejoe.v6i1.3769

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