learning - Academic Conferences Limited

Models of eLearning: The Development of a Learner-
Directed Adaptive eLearning System
Stella Lee 1 , Trevor Barker 1 and Vive Kumar 2
1
Computer Science Department, University of Hertfordshire, Hatfield, UK
2
School of Computing & Information Systems, Athabasca University,
Edmonton, Canada
stellaylee@gmail.com
t.1.barker@herts.ac.uk
vive@athabascau.ca
Abstract: In many eLearning contexts, materials are designed to be self-paced, with the content being available
anytime, anywhere for learners to study independently. Commonly, without the presence and immediate
feedback of an instructor, distance learners are left to their own devices to negotiate their learning path and to
monitor their own progress. Furthermore, learning a complex topic structured in terms of various media and
learning materials requires learners to make certain instructional decisions concerning what to learn and how to
go about their learning. In other words, self-paced learning requires learners to self-regulate their own
learning(Hadwin & Winne, 2001). Very often, learners have difficulty regulating learning in higher education when
topics are complex and unfamiliar and it is not always clear to the learners if their instructional decisions are
optimal.(Azevedo, Cromley, Seibert, & Tron, 2003) Research into adaptive eLearning systems has attempted to
facilitate this process by providing recommendations, classifying learners into different preferred learning styles,
or highlighting suggested learning paths(Brusilovsky, 1998).The aim of this research is to explore how learners
can self-directed and self-regulate their online learning both in terms of domain knowledge and meta knowledge
in the subject of computer science with a flexible and adaptive eLearning system. Two educational theories:
experiential learning theory (ELT) and self-regulated learning (SRL) theory are used to aid learners’ in their
learning paths. As a result, changes in domain-knowledge, meta-knowledge, learner experience, learner
satisfaction, perceived controllability, and system usability are being measured. All in all, this paper sums up the
research work being done on the initial development of the system, instructional design framework based on the
two theories, experimental design plan and course material examples as well as related issues.
Keywords: adaptive systems, eLearning, instructional design, learning design
1. Introduction
eLearning has become so prevalent in higher education and corporate training that we have seem
every effort being made to reshape and digitized contents online in the past decade or so. ELearning
uses the internet to deliver instructions to the learner and it may be argued that one of the most
popular forms of eLearning is web-based distance learning (Koohang & Du Plessis, 2004) In many
cases, these web-based learning materials are designed to be self-paced, with the content being
available anytime, anywhere to allow learners to study independently. Commonly, without the
presence and immediate feedback of an instructor, distance learners are left to their own devices to
negotiate their learning path and to monitor their own progress. Furthermore, learning a complex topic
structured in terms of various media and learning materials requires learners to make certain
instructional decisions concerning what to learn and how to go about learning. In other words,
eLearning requires learners to self-regulate their own learning(Hadwin & Winne, 2001). Very often,
learners have difficulty regulating learning when topics are complex and unfamiliar and it is not always
clear to the learners if their learning decisions are optimal(Azevedo et al., 2003). Research into
adaptive eLearning systems has attempted to facilitate the learning process by providing
recommendations with respect to classifying learners into preferred learning styles and by associating
recommended learning paths with these learning styles(Brusilovsky, 1998). Indeed, research has
shown the importance of adapting online course material to support learners with different
background knowledge and skills(Brusilovsky, 1998; Weber, 1999). Another work has described a
user modeling approach that is beneficial to learners who are interacting with complex learning
applications in an online environment(Adisen & Barker, 2007).
Broadly speaking, user modeling is a technique employed to provide users with options with respect
to performing tasks and interacting with systems differentially. The common variables to model
include the user’s personalities, abilities, prior knowledge, preferences, performances and
intentions(Barker & Adisen, 2005). User modeling can also be defined as a model of users residing
inside a system or computational environment(Fischer, 2001). However, many of these models
attempt to “match” students with a certain learning styles or learner characteristics that it falls short on
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