Open AccessINTRODUCTION OF EDUCATIONAL DATA MINING BY USING A VARIETY OF TECHNIQUES IN ORDER TO ACHIEVE THE GOAL FROM THE MOODLE LMS
Author(s) -
Aisha Akhtar,
Mohammad Serajuddin,
Zahidah Hasan
Publication year - 2021
Publication title -
european journal of open education and e- learning studies
Language(s) - English
Resource type - Journals
ISSN - 2501-9120
DOI - 10.46827/ejoe.v6i1.3769
Subject(s) - c4.5 algorithm , machine learning , computer science , naive bayes classifier , decision tree , artificial intelligence , tree (set theory) , class (philosophy) , multimedia , support vector machine , mathematics , mathematical analysis
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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