April 2012 Volume 15 Number 2 - Educational Technology & Society

The Analog system (Yan et al., 1996) consists of two main components, performing online and offline data
processing with respect to the web server activity. Past users activity is recorded in server log files which are
processed to form clusters of user sessions. The online component builds active user sessions which are then
classified into one of the clusters found by the offline component.
Perkowitz and Etzioni (1999) proposed Page Gather, a WUM system that builds index pages containing links to
similar pages. Page Gather creates index pages. The main hypothesis is that users behave coherently during their
navigation. It deals with page clusters instead of session clusters, and bases them on the previous assumption called
visit coherence, i.e. pages within the same session are in general conceptually related.
The SUGGEST WUM (Baraglia and Palmerini, 2002) system was designed to produce links to pages of the
potentional user interests. It can provide useful information to make web user navigation easier and to optimize web
server performance. It was implemented as a module to Apache web server.
In addition to the above mentioned general purpose WUM tools, there are also several specialized ones that are used
in the e-learning platforms. CourseVis (Mazza and Dimitrova, 2007) is a visualization tool that tracks web log data
from an LMS. By transforming this data, it generates graphical representations that keep instructors well-informed
about what precisely is happening in distance learning classes. GISMO (Mazza and Milani, 2004) is a tool similar to
CourseVis, but provides different information to instructors, such as students’ details in using the course material.
Sinergo/ColAT (Avouris at al., 2005) is a tool that acts as an interpreter of the students' activity in a LMS. Mostow et
al. (2005) provides a tool which uses log files in order to represent the instructor-student interaction in hierarchical
structure.
MATEP (Zorrilla and Álvarez, 2008) is another tool acting in two levels. First, it makes a mixture of data from
different sources suitably processed and integrated. These data originate from e-learning platform log files, virtual
courses, academic and demographic data. Second, it feeds them to a data webhouse which provides static and
dynamic reports. Sinergo/ColAT (Avouris et al., 2005) is a tool that offers interpretative views of the activity
developed by students in a group learning collaborative environment. Mostow (Mostow et al., 2005) describes a tool
that shows a hierarchical representation of tutor-student interaction taken from log files.
An automatic personalization approach is also proposed by Khribi et al. (2009). It provides online automatic
recommendations for active learners without requiring their explicit feedback through two modules: an off-line
module which preprocesses data to build learner and content models, and an online module which uses these models
on-the-fly to recognize the students’ needs and goals, and predict a recommendation list.
All these tools are based on the analysis of log files as our methodology does. Especially the Analog system (Yan et
al., 1996) and the last one proposed by Khribi et al. (2009) seeded the idea for a final tool acting in two levels: online
and off-line. However, none of the aforementioned tools proposes and uses indexes calculated by the pages and
sessions accessed by the users. These indexes derive after the pre-processing of the raw data contained in the log
files.
Methodology
The proposed methodology consists of three main steps, namely the logging step, the pre-processing and the
clustering step. These steps are based on the framework described in detail in a study by Kazanidis et al. (2009) and
facilitate the extraction of useful information from the data logged by a web server running an LMS. Instructors can
benefit from the methodology’s course evaluation indexes.
The main advantages of the proposed methodology are that: (i) it uses data mining techniques for user and course
evaluation; (ii) it proposes new indexes and metrics to be used with data mining algorithms; (iii) it can be easily
adapted to any LMS, (iv) it visualizes the results in a user friendly environment and allows interactive exploitation of
the data.
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