Volume Two - Academic Conferences

A Framework for Decision Support for Learning
Management Systems
Phelim Murnion 1 and Markus Helfert 2
1
School of Business, Galway-Mayo Institute of Technology, Galway, Ireland
2
School of Computing, Dublin City University, Dublin, Ireland
phelim.murnion@gmit.ie
markus.helfert@computing.dcu.ie
Abstract: Learning Management Systems (LMS) provide a valuable platform for e-learning that offer great
flexibility. However, compared to traditional learning environments they are challenging and complex for decisionmakers,
both teachers and learners. At the same time, LMS environments offer opportunities for analysis by
storing large quantities of data, such as web log files and data about students and content, which are not
generally available in the traditional environment.Motivated by approaches in other domains, such as ecommerce
and clinical management, in this article we propose to relatethe complex decision environment with
the possibilities of using large quantities of data.In this paper we review relevant literature on educational data
mining (EDM) and combining that with a standard data mining methodology we propose a conceptual framework
that appropriately relates the methods of data mining to the settings of teaching and learning in a LMS
environment. In contrast to other frameworks, our conceptual framework enables EDM research to be more
integrated with the task domain. In our framework, teaching and learning activities and the decisions required to
control those activities are addressed by relating the following three elements: pedagogy; learning activities; and
decision-making. The significance of our work is that the framework enables us to compare between different
research studies as well as provide practical guidelines for developing EDM solutions. The framework also
provides a number of further directions for researchers which follow naturally from a decision-centric perspective
and from the full implementation of the contextual phases of the data mining life cycle
Keywords: educational data mining, learning management systems, decision support, programme evaluation
1. Introduction
New technologies for learning are being developed and introduced at a rapid rate. Perhaps the most
commonof these is Learning Management Systems(LMS), also known as Course Management
Systems (CMS) orVirtual Learning Environments (VLE).These systems offer a variety of tools to
enable educators to distribute information to students, produce content material, prepare
assignments, engage in discussions, and to enable collaborative learning with forums, chats, file
storage areas, and news services. LMSs such as Moodle and Blackboard have recently become
ubiquitous in tertiary/higher education, with (citing US statistics) almost 100% of institutions using
LMS technology (Green 2010), and 60% using an approved campus-wide LMS(Pam Arroway 2010).
At the same time, teaching and learning in a LMS environment presents new challenges and
opportunities. The teacher loses some of the advantages of the traditional learning environment and
while the learner gains more freedom to make their own decisions. As a result, the decision-making
environment for both teachers and learners becomes more complex. However, LMS environments
also store large quantities of data, such as web log files and data about students and content, which
are generally not available in the traditional environment. This combination of a complex decisionmaking
environment and large quantities of raw data presents problems but also opportunities for
practitioners (teachers) and researchers. Moreover, researchers in e-learning, particularly in the area
of programme evaluation, have identified the need for more research based on the data in the LMS
(Janossy 2008) to supplement traditional survey-based and experimental methods.
In other problem domains, such as e-commerce and clinical management, this combination of a
complex decision environment and large quantities of data has been addressed by information
systems researchers using data mining and other analytical approaches. Motivated by this
observation, we focus on ‘Data Mining’, which involves the automatic extraction of implicit and
interesting patterns from large data collections (Klosgen 2002). The field of e-Learning, with the large
amounts of usage data automatically stored in the LMS web log files and the difficulty of making
decisions in online learning, is well suited to data mining(Zaïane 2001). This approach, which has
become established as “educational data mining” or EDM (Castro, Vellido et al. 2007), has been
applied to a variety of problems in LMS environments. In data mining, it is an accepted principle that
an understanding of the context or setting in which data mining is deployed is important (Shearer
2000), (Lavrac 2004), (Hofmann and Tierney 2009). In the specific field of EDM several researchers
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