Volume Two - Academic Conferences

Phelim Murnion and Markus Helfert
study defines the context differently and as a result each has a unique definition of problem definition
and deployment.
However, a feature common to all three studies is their operational orientation. An educational system
clearly is a set of operations (in which content, learner, and task interact) resulting in outcomes. But
decision support approaches like data mining are not designed to support operations. Another
element is required which relates operations to outcomes. This concept is addressed in another EDM
study, (Gaudioso and Talavera 2006), already mentioned in the related work. The context of the study
is collaborative learning;which is described using an appropriate theoretical context. Based on that
context the operations (the problems to be solved) are described in terms of the creation of virtual
communities. The critical difference between this and other EDM studies is the next element. The
authors describe a separate process called adaptive collaborative support. (ACS): the mechanism for
ensuring that the virtual communities operate in a manner which meets the goals of collaborative
learning theory, thus relating operations to outcomes. The data mining intervention is addressed, not
directly at the operations of the collaborative groups, but to provide decision support for the ACS
function.
Motivated by this example, we suggest that a general model for our framework can be extrapolated
from this particular study. Three elements are necessary in an educational system: an underlying
pedagogy (theory); a set of teaching and learning activities (operations) and a related set of control
decisions.It is towards the controls decisions that EDM should be directed. This perspective of
educational decision making and control allows us to extend the model described in table 1 by
providing a definition for each element, resulting in table 2.
Table 2: Framework elements and definitions
Element Definition
Task Domain Learning Theory
Problem definition Teaching & Learning Activities
Deployment Control Decisions
Learning theory constrains the kind of learning and teaching activities that should occur and also
provides the goals/outcomes which decide what control decisions to make. Learning and teaching
activities generate the data which EDM approaches can turn into knowledge for decision-making
(Romero and Ventura 2007).This control systems view has been examined in the educational
literature from a number of different perspectives; learner control (Williams 2001), learning
cybernetics (Liber 2003) and soft systems methods (Warwick 2008).
This specification can be combined with the standard data mining cycle to provide a
specificframework for this task domain, the Educational Data Mining Cycle in figure 5.Using this
model, the deployment phase for EDM consists of providing knowledge fordecision making
processes. The decision making processes are part of systems which control the teaching and
learning activities identified in problem definition.
5. Discussion and concluding remarks
The proposed framework makes a number of contributions. Firstly, the framework addresses the
thesis raised at the start of this paper; providing a way to integratethe methods of data mining withthe
context of teaching and learning in a LMS environment. Secondly, it provides a bridge between EDM
approaches and general educational theories. This enables EDM research to increase the impact on
and relationship with other areas of educational technology research, a problem identified in an earlier
review paper(Baker and Yacef 2009).The framework also provides a reference model for constructing
further EDM interventions (based on the dashed triangle in the framework diagram, figure 5)that EDM
designers can use to support the implementation of the data mining cycle in education.Finally the
framework provides further directions for researchers that follow from a decision-centric and control
systems perspective. For example, EDM research has tended to focus on gathering knowledge to
direct attention to a problem. In other problem domains, decision support tools are considered more
useful when deployed at later steps in decision making such as when devising or evaluating
solutions(March and Hevner 2007).
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