April 2012 Volume 15 Number 2 - Educational Technology & Society

Valsamidis, S., Kontogiannis, S., Kazanidis, I., Theodosiou, T., & Karakos, A. (2012). A Clustering Methodology of Web Log
Data for Learning Management Systems. Educational Technology & Society, 15 (2), 154–167.
A Clustering Methodology of Web Log Data for Learning Management
Systems
Stavros Valsamidis 1 , Sotirios Kontogiannis 1 , Ioannis Kazanidis 2 , Theodosios Theodosiou 2
and Alexandros Karakos 1
1 Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece //
2 Accounting Department, Kavala Institute of Technology, Agios Loukas, 65404, Kavala, Greece //
svalsam@ee.duth.gr // skontog@ee.duth.gr // kazanidis@teikav.edu.gr // theodosios.theodosiou@gmail.com //
karakos@ee.duth.gr
(Submitted July 2, 2010; Revised January 14, 2011; Accepted April 1, 2011)
ABSTRACT
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.
Keywords
E-learning, Web mining, Clustering, Metrics
Introduction
Learning Management Systems (LMSs) offer a lot of methods for the distribution of information and for the
communication between the participants on a course. They allow instructors to deliver assignments to the students,
produce and publish educational material, prepare assessments and tests, tutor distant classes and activate archive
storage, news feeds and students’ interaction with multimedia. They also enhance collaborative learning with
discussion forums, chats and wikis (Romero et al., 2008a).
Some of the most well-known commercial LMS are Blackboard, Virtual-U, WebCT and TopClass, while Moodle,
Ilias, Claroline and aTutor are open source, freely distributed LMSs (Romero et al., 2008a). In Greece, the Greek
University Network (GUNet) uses the platform Open eClass (GUNet, 2009), which is an evolution of Claroline
(Claroline, 2009). This system is an asynchronous distance education platform which uses Apache as a web server,
MySQL as its database server and has been implemented in PHP. Open eClass is open source software under
General Public Licence (GPL).
Due to the volume of data, one of the main problems of any LMS is the lack of exploitation of the acquired
information. Most of the times, these systems produce reports with statistical data, which, however, don’t help
instructors to draw useful conclusions either about the course or about the students; they are useful only for
administrative purposes of each platform. Moreover, the existing e-learning platforms do not offer concrete tools for
the assessment of user actions and course educational content.
Data and web mining
Data mining is the search for relationships and patterns that exist in large databases, but are 'hidden' among the vast
amounts of data. It is part of the whole Knowledge Data Discovery (KDD) process. KDD is the complete set of
processes for knowledge discovery in databases that aims at the detection of valid information and pattern
recognition in raw data (Kantardzic, 2003). The classical KDD process includes 5 phases: data pre-processing, data
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