A New Pre-Processing Approach Based on Clustering Users Traces According to their Learning Styles in Moodle LMS

Authors

  • Mohammed Aitdaoud LTIM, Department of Computer Science, Faculty of Sciences Ben M’sick, Hassan 2 University of Casablanca, Morocco https://orcid.org/0000-0002-8627-4429
  • Abdelwahed Namir LTIM, Department of Computer Science, Faculty of Sciences Ben M’sick, Hassan 2 University of Casablanca, Morocco
  • Mohammed Talbi ORDIPU, Faculty of Sciences Ben M’sick, Hassan 2 University of Casablanca, Morocco

DOI:

https://doi.org/10.3991/ijet.v18i07.37635

Keywords:

Educational Data mining, Clustering, Learning styles, Higher education, Technology Enhanced Learning, Learning management system

Abstract


Nowadays, many Moroccan universities and institutions start offering training and online courses "E-learning". Which accumulate a vast amount of information that is very valuable for analyzing students’ behavior and could create a gold mine of educational data. However, handling the vast quantities of data generated daily by the learning management systems (LMS) such as Moodle has become more and more complicated. This massive data can be used to improve decision making and management, which requires a proper extracting and cleaning methods.

The purpose of this paper is to suggest a new approach for the preprocessing of the execution traces generated during the interaction of learners with the Moodle LMS and especially the educational content in SCORM format.

In this study, we built two experimental corpus with the learning platform Moodle. Using the data generated by the experimental corpus, we applied the Clustering data mining technique as a preprocessing step in our process discovery. Hence, students with similar learning styles or performance levels are grouped together which should help us to build a partial process model (learning process) that are easier to understand for the decision makers.

Author Biographies

Mohammed Aitdaoud, LTIM, Department of Computer Science, Faculty of Sciences Ben M’sick, Hassan 2 University of Casablanca, Morocco

Mohammed Aitdaoud is a Ph.D. in Educational Data Analysis from the Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Morocco. He is currently a professor at the same faculty. Also, he is a member of the Laboratory of Technologies for Information and Modeling (LTIM) and also a member of the Observatory of Research in Didactics and University Pedagogy (ORDIPU). His research interests include educational technologies, distance training engineering, techniques of training, information systems, Educational Data Mining, and Educational Process Mining (email: mohammed.aitdaoud@univh2c.ma).

Abdelwahed Namir, LTIM, Department of Computer Science, Faculty of Sciences Ben M’sick, Hassan 2 University of Casablanca, Morocco

Abdelwahed Namir is a Ph.D. in Numerical Methods for Engineers from Mohammadia School of Engineers (EMI Rabat) Morocco. He is also the Head of the Laboratory of Technological Information and Modelisation (LTIM) Faculty of Sciences Ben M'Sik, University Hassan II - Casablanca, Morocco. His research interests include Mathematics, Computers, and modeling.

Mohammed Talbi, ORDIPU, Faculty of Sciences Ben M’sick, Hassan 2 University of Casablanca, Morocco

Mohammed Talbi is a Ph.D. in Sciences and Processes of Analysis from the University Pierre et Marie Curie of Paris. He is currently the Dean of the Faculty of Sciences Ben M'Sik at Hassan II University, B.P 7955 Sidi Othmane, Casablanca, Morocco, and the Director of the Observatory of Research in Didactics and University Pedagogy (ORDIPU) since 2014. He is an Expert in the fields of teaching and research on educational technologies, engineering of distance training, techniques of training, and information systems. He is the author of several national and international awards and has accumulated more than 30 years of scientific productions

Cover iJET Vol. 18 No. 07 (2023)

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Published

2023-04-05

How to Cite

Aitdaoud, M., Namir, A., & Talbi, M. (2023). A New Pre-Processing Approach Based on Clustering Users Traces According to their Learning Styles in Moodle LMS. International Journal of Emerging Technologies in Learning (iJET), 18(07), pp. 226–242. https://doi.org/10.3991/ijet.v18i07.37635

Issue

Vol. 18 No. 07 (2023)

Section

Papers

License

Copyright (c) 2023 Pr. Mohammed AITDAOUD, Pr. Abdelwahed NAMIR, Pr. Mohammed TALBI

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