Volume Two - Academic Conferences

Cristina Wanzeller and Orlando Belo
often used in combination with eLearning, but for different purposes, such as content categorization
and retrieval (Rodrigues et al. 2007) and for personalizing courses (Chorfi and Jemni 2004).
In this paper we present the Mining Plans Selector (MPS) system, focusing the practical use of the
two major assistance tasks that the system intents to fulfil: 1) aiding users in WUM processes
development, by recommending the most suited mining plans; and 2) semi-automated knowledge
acquisition support, from past successful WUM processes, into a centralized case base. The system
has the ability to suggest the most suited WUM plans to solve a specific usage data analysis problem.
The MPS also provides support to collect and organize the knowledge acquired from the experience
on solving WUM problems (usage cases, reflecting each one different usage past profiles), bringing
such knowledge up to date and promoting the system’s sustained incremental learning. New WUM
processes are stored on a collective case base, centralizing a key resource to the system’s capacity
to solve problems in a subject usage oriented knowledge base. When adopting and using a WUM
plan suggested by the system we’ll be able to explore in a more effective way the usage information
gathered inside the target eLearning platform and, with the results, defining better site organizations
and detect obsolete resources and know what kind of use users do to current eLearning platform
resources. Consequently, we have the basis to optimize site functionalities, reducing operational and
maintenance costs. Section 2 discusses main characteristics and challenges of eLearning usage
analysis. Sections 3 and 4 describe some issues and the utility of the MPS system. Finally, section 5
presents some conclusions and future work.
2. Usage analysis in eLearning systems
Several organizations explore eLearning platforms and need to make the process of creating, using
and maintaining the contents more efficient and effective. In order to obtain feedback and to
guarantee platforms effectiveness, we should analyse its usage. Web based eLearning platforms are
the most promising and interesting kind of platforms, due to their success, given the advantage of
easy access (Pahl 2004). They are a specific type of a Web site, and so, we may apply similar
principles, techniques and approaches to deal with analogous problems. This strategy is corroborated
by the increasing research involved on applying WUM in this area.
Within the context of vulgar Web sites, clickstream data is logged using non-intrusive forms, being a
valuable source of users’ behaviour information. This data may be gathered on a periodic basis to be
analysed consistently. Usage data may also be combined with other types of information about users,
pushing profiling to completely new levels (Srivastava et al. 2002). Moreover, we can anticipate
discontent and even new user’ needs and, so, react faster. Exploring WUM to extract knowledge from
this and related data has hereby potential enormous benefits to organizations. Some important and
actionable areas of WUM exploration consist of Web personalization, business intelligence, system
performance improvement and site content and structure enhancement (Srivastava et al. 2000)
On the context of eLearning Web sites, usage data provides insights to understand students learning
behaviour. There are specific requirements, mainly the need to take into account pedagogical aspects
of the learner and of the system (Romero and Ventura 2007). The ultimate goal differs from ecommerce
sites, i.e. “turning learners into effective better learners” (Zaiane 2001). Although the ways
to achieve such goal are similar, there are several important differences between the two areas. Main
differences may be structured in terms of (Romero and Ventura 2007): domain; data; objective and
techniques. The domain differs due to the purpose of guiding learning. The data in eLearning
comprises more information about student’s interaction and the user model is different (e.g. users are
not anonymous). Besides, learning sessions are longer (may span many access sessions and spread
over various days). The objective of improving learning is considered more subjective and more subtle
to qualify/quantify (Zaiane 2001) and the process of learning more complex than shopping (Pahl
2004). At the last, the techniques, DM and WUM were not tailored to eLearning, but the methods are
general enough and may bring benefit to these platforms. However, some can be adapted and other
cannot. Our view is that the extracted knowledge will be very helpful and that common features
number is higher, particularly the ones regarding KD process characteristics.
Web based eLearning systems provide usage data to analyse and turn discoveries into actions.
Though, this approach has some other real limitations. Data mining today is at best a semi-automated
process. A fundamental challenge is to develop KD systems easier to use, even by casual users. KD
and specially WUM are very useful but even more complex processes. ELearning sites administrators
and promoters, usually, are not skilled WUM users. Additionally, we point the unavailability of
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