Volume Two - Academic Conferences

Phelim Murnion and Markus Helfert
have identified the need for a better integration with the teaching and learning context (Gaudioso and
Talavera 2006), (Romero, ventura et al. 2008).
Theobjective of this paper is to address decision support for LMSsby describing a conceptual
framework that enables the integrationof data mining methods with the context or settings of teaching
and learning in a LMS environment. The main contribution is to address a limitation in existing EDM
research with a framework which can guide future research and practice. The remainder of the paper
is structured as follows. In section two, we develop a description of the problem of integrating EDM
with the teaching and learning context.Based on that description, we propose a set of categories for
analysingEDM research. Using these categories we present in section three a detailed analysis of the
EDM literature. Drawing from this analysis, in section four we propose and describe a conceptual
framework for an improved integration of EDM research with the learning context followed by a
discussion and comment on the proposed framework.
2. Related work
In order to develop an appropriate basis for the analysis of the limitations in EDM research,in this
section we review existingwork, first by describing the EDM research field in general and then by
examining relevant related work in data mining methodologies. The general perspective provides us
with an overview of the problem of integrating EDM research with the teaching and learning context.
The work on data mining methodologies allows us to convert that overview into a more detailed
model, which provides the basis for the analysis in section three.
2.1 Educational Data Mining
Data Mining (also known as knowledge discovery from data) is a well-established approach for
extracting patterns from large quantities of data (Berry and Linoff 2004), using a variety of statistical,
machine-learning and other data-mining algorithms, in order to explore and understand the
phenomena underneath the data or to support decision making (Peng, Kou et al. 2008). It has been
applied successfully to a number of scientific and commercial domains (Lavrac 2004). Due to its
origins in data analysis and machine learning, data mining research has tended towards a technical
orientation, focussing on techniques/tasks such as: data clustering, classification, association rule
mining and sequential analysis (Peng, Kou et al. 2008). Educational data mining (EDM) is the
application of the data mining approach to the different types of educational data (Romero and
Ventura 2010), (Baker and Yacef 2009).It has been recognized that the field of eLearning in a LMS
environment is well suited to the data mining approach (Zaïane 2001), due to the two features of large
amounts of raw data(stored in the LMS web log files) and the complexity of the decision making
problems (Peng, Kou et al. 2008).This EDM research approach(Castro, Vellido et al. 2007)has
included investigations of a wide variety of situations in the LMS environment including: evaluating
learner activity (Zaïane 2001), (Muehlenbrock 2005), (Pahl 2006); providing support to educators
(Gaudioso and Talavera 2006) and recommending student actions(Sacin, Agapito et al. 2009). The
above-mentioned technical orientation is reflected in EDM as well, where papers have tended to focus
on the application of these techniques to educational data.
Despite the strongly technical orientation of EDM research there have been a number of
recommendationsfor more work on integrating data mining with the context of teaching and learning.
An early paper in the field describes the concept of “integrated web usage mining” (Zaïane 2001), in
which a data mining system would be integrated into the eLearning system. A review paper in 2007
describes a model in which “data mining in educational systems is an iterative cycle of hypothesis
formation, testing, and refinement. Mined knowledge should enter the loop of the system and guide,
facilitate and enhance learning as a whole. Not only turning data into knowledge, but also filtering
mined knowledge for decision making” (Romero and Ventura 2007). Our paper emphasises this view.
However, the impact of these recommendations on the research work is not clear. Frequently EDM
research has been focussed on the techniques of data mining with little or no reference to the details
of the learning context (Zaïane 2001), (Etchells, Nebot et al. 2006). In contrast some researchers
have based their EDM interventions on a comprehensive model of the learning context using
established pedagogic theory on (for example) learner-content interaction (Pahl 2006), and group
work(Perera, Kay et al. 2009). Of particular significance is the work of Elena Gaudioso and colleagues
(Talavera and Gaudioso 2004),(Gaudioso and Talavera 2006) on collaborative learning, which
describes not only a detailed educational context but a purpose and role for the results of the EDM
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