Volume Two - Academic Conferences

Phelim Murnion and Markus Helfert
intervention within that context. On this basis we postulate a spectrum of EDM research in terms of
integration with the educational context from studies with little or no integration with the context to
studies which are highly integrated. However, the level of integration is unlikely to be a simple scalar
variable. Any analysis of an ‘integration’ construct within the EDM literature requires a more concrete
version of this construct. Since the relationship of data mining to the domain context is a feature of the
data mining methodology adopted, an examination of data mining methodologies is a necessary next
step.
2.2 Data Mining methodologies
Data mining arose from a number of related computational and statistical approaches, which form a
set of data mining methodologies. The methodologies can have an emphasis on technical aspects
rather than the processes and the relationship to the task domain(Peng, Kou et al. 2008). However,in
order to structure the methodologies,research and practice in data mining has expanded in
perspective to include steps along a data mining process or data mining model (figure 1). This model
can be described as the ‘technical perspective’ on data mining and has been commonly used in EDM
research(Luo 2001), (García E. 2007).
Figure 1: Data Mining Model: Technical perspective
As data mining matured as a discipline and as data mining applications were implemented in a wider
variety of problem domains the technical steps were subsumed into a more comprehensive
methodology known as the data mining cycle (e.g. figure 2).A number of data mining
methodologies/cycles have been developed but the process of developing these methodologies has
exhibited two common features(Hofmann and Tierney 2009): the replacement of a sequence of steps
with a cyclical, iterative process, and a greater focus on the connections between the data mining
process and the underlying problem context.The most widely accepted model, known as the CRISP-
DM (CRoss Industry Standard Process for Data Mining) Cycle(Shearer 2000),exhibits both features
(figure 2).
Figure 2: CRISP-DM cycle (Shearer 2000)
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