Friday 24th of November 2017
 

Multilevel clustering and association rule mining for learners profiles analysis


Nawal Sael, Abdelaziz Marzak and Hicham Behja

Educational Data Mining is concerned with developing methods for exploring data that come from educational domains, and using those methods to better understand learner, and how they interact with those environments. In this research, we benefit from a new preprocessing approach applied to Moodle platform [1] [2] in order to apply clustering and association rule mining techniques to analyze learners behaviors, to help in learning evaluation, and to enhance the structure of a given SCORM content. We adopted the feature selection process and multilevel clustering that allowed us to confirm the importance of these new data preprocessing methods and to validate the usefulness of the attributes describing the learners\' interactions with the SCORM content pertaining to learners profiles detection. We also benefited from this approach as we sought to find possible relationships between the different parts of the relevant content and to help the teacher/ tutor to evaluate the structure of such content.

Keywords: Educational data mining; Moodle; preprocessing; clustering; association rule mining; learning profiles

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ABOUT THE AUTHORS

Nawal Sael
Laboratory of Information Technology and Modelization, Faculty of Science Ben Mísik

Abdelaziz Marzak
Laboratory of Information Technology and Modelization, Faculty of Science Ben Mísik

Hicham Behja
Laboratory of Command and Control and Production Systems, National High School of Art and Craft


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