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A Clustering Methodology of Web Log Data for Learning Management Systems

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Journal of Educational Technology & Society Volume 15, Number 2, ISSN 1176-3647 e-ISSN 1176-3647


Learning Management Systems (LMS) collect large amounts of data. Data mining techniques can be applied to analyse their web data log files. The instructors may use this data for assessing and measuring their courses. In this respect, we have proposed a methodology for analysing LMS courses and students' activity. This methodology uses a Markov CLustering (MCL) algorithm for clustering the students' activity and a SimpleKMeans algorithm for clustering the courses. Additionally we provide a visualisation of the results using scatter plots and 3D graphs. We propose specific metrics for the assessment of the courses based on the course usage. These metrics applied to data originated from the LMS log files of the Information Management Department of the TEI of Kavala. The results show that these metrics, if combined properly, can quantify quality characteristics of the courses. Furthermore, the application of the MCL algorithm to students' activities provides useful insights to their usage of the LMS platform. (Contains 2 tables and 8 figures.)


Valsamidis, S., Kontogiannis, S., Kazanidis, I., Theodosiou, T. & Karakos, A. (2012). A Clustering Methodology of Web Log Data for Learning Management Systems. Journal of Educational Technology & Society, 15(2), 154-167. Retrieved August 5, 2020 from .

This record was imported from ERIC on April 18, 2013. [Original Record]

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