Abstract
In our knowledge-oriented society of the 21st century, the necessity for novel teaching and learning
paradigms is increasing and, furthermore, is leading to development streams like distance learning or e-
learning. However, these new educational approaches often fail due to reasons such as the negligence of
pedagogical principles, a lack of personal contact with the teacher or other learners, usability problems of
the learning platform, low quality of the learning content and so forth. As one possible answer to such
problematic aspects, adaptive e-learning deals with implementing typical didactical competencies within
information technology. Through these methods knowledge transfer can be improved, for example by
means of observing, assessing and adapting the learning process.
In addressing adaptive behaviour in e-learning environments, this dissertation aims to examine theo-
retical and practical aspects of adaptive e-learning and to develop a technological prototype for the AdeLE
research project. AdeLE, which stands for “Adaptive e-Learning with Eye-Tracking”, focuses on two im-
portant requirements. On the one hand, eye-tracking technology ought to be applied for enhanced learner
observation. Therefore, the prototypical solution should consider the integration of such a device, while
the usefulness of eye-tracking for adaptive e-learning is decidedly not part of this work. On the other hand,
a tool which is named “Dynamic Background Library” and realises the idea of retrieval-based instruction
should be utilised to support the adaptation of the online learning process.
From the theoretical viewpoint, the dissertation first and foremost comprises aspects of adaptation sys-
tems and technology-based learning and teaching. A formal specification describing adaptive behaviour
in e-learning systems is built up by combining these two scientific fields and surveying historical streams
and systemic types of adaptive educational systems. Continuing with practical issues, this formal model
is applied to derive requirements for standardised, adaptable courseware as well as on an ideal adaptive
e-learning environment. Consequently, the technological realisation of the AdeLE prototype is described
in consideration of these requirements. Finally, this work also evaluates the most relevant research ideas
of the AdeLE solution approach, namely the prototype itself, the usefulness of the Dynamic Background
Library and the impact of teacher-driven adaptation of online learning.
i

So long, and Thanks for All the Help!
This book was made possible with the help of many people involved in my professional and private
surrounding. To those the following words of appreciation are addressed.
First of all, I have to thank all my former colleagues at the Institute for Information Systems and
Computer Media at the Graz University of Technology, not only for enabling and supporting my research
work, but also for managing a very comfortable and productive working environment. Particularly, I am
indebted to my supervisor, Hermann Maurer, for giving me inspiration and valuable feedback. Many
thanks go to Klaus Tochtermann being second reader of my dissertation.
Additionally, I would like to offer my gratitude towards all members of the Web Application Group,
especially Christian Gu¨tl for recruiting me for the AdeLE project and facilitating my research work. Cor-
dial thanks go to Victor Manuel Garcı´a-Barrios with whom I did not only share the office, but also many
valuable ideas and the travail of writing a dissertation. It was his extraordinary way of scientific thinking
that had a strong impact on my own research activities. Furthermore, I have to thank the project leaders
Helmut Leitner and Walter Schinnerl as well as all other members of our group for their excellent team-
work and their high-quality output, which was the key factor for the successful completion of so many
projects in the last seven years.
In context of my research work, I have to acknowledge the collaboration with even more colleagues
and people from our partner institutions. Representatively for all the others, I thank Maja Pivec and Ju¨rgen
Pripfl from the Department of Information Design of the University of Applied Sciences JOANNEUM,
Dietrich Albert and Alexandra Sindler from the University of Graz, Gustaf Neumann, Fridolin Wild and
Stefan Sobernig from Vienna University of Economics and Business Administration, Barbara Kieslinger
and the other colleagues from the iCamp project, Herwig Rollett, Markus Strohmaier and all the people
from the Know Center with whom I was engaged during the APOSDLE project, Heinz Dreher from Curtin
University of Technology and Elizabeth Peterson from the University of Auckland.
Besides, I am also indebted to the University of Applied Sciences Campus02, where I held lectures
and supervised 17 diploma theses in the last 4 years. Namely, I have to thank Alfred Zindes, Franz Pucher,
Valentin Gillich and Georg Lindsberger. Besides lecturing, being lectured in a special course of studies
on didactics and pedagogy at Campus02 has had positive influence on my personal development and my
research work. Particularly, I would like to thank my diploma students for their participation and interest
and their excellent theses as well as the 38 students of the class IT02 who participated in one of my
evaluation studies.
Last but not least, I have to articulate words of thankfulness to those of my private surrounding,
precisely my mother Monika, my brother Klaus, his wife Manuela, and their child Melissa. But above
and beyond, I am deeply indebted to my companion in life, Silke Spiel, for her love and patience, for
putting up with late nights, and for even proof-reading and reviewing parts of this work. In the end, it was
primarily her credits that the quality of my English has significantly improved and that this book came to
a successful end.
In beloved memory of my father Wilhelm
Felix Mo¨dritscher
Vienna, Austria, May 2008
iii

Contents
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Objectives of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Methodology and structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
I. Theoretical Background 5
2 Adaptation Systems 7
2.1 Roots and related fields of systems theory . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Further developments in systems theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Towards adaptation and related concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4 A generic approach to adaptation systems . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3 Technology-Based Learning and Teaching 25
3.1 Relevant learning theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2 E-pedagogy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3 E-didactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4 Towards formalising e-learning and e-teaching . . . . . . . . . . . . . . . . . . . . . . . . 38
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4 Adaptive E-Learning 45
4.1 Historical streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2 Types of adaptive educational systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3 Existing theoretical models for adaptive e-learning . . . . . . . . . . . . . . . . . . . . . 52
4.4 Formalising adaptive behaviour in e-learning systems . . . . . . . . . . . . . . . . . . . . 57
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
II. Practical Aspects 61
5 Towards Standardising Adaptable Courseware 63
5.1 Standardisation of learning content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2 Requirements for standards to support adaptive e-learning . . . . . . . . . . . . . . . . . . 66
5.3 Inspection of current specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.4 A standard-based approach to adaptive e-learning . . . . . . . . . . . . . . . . . . . . . . 71
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
v

6 An Ideal Environment for Adaptive E-Learning 77
6.1 Methods and techniques for adapting the learning process . . . . . . . . . . . . . . . . . . 77
6.2 Functional requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.3 Architectural design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.4 Inspecting existing projects and solutions . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
7 Technical Realisation of the AdeLE System 91
7.1 Planning of the AdeLE prototype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
7.2 Functional units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
7.3 Implementation details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
7.4 A walk through the AdeLE system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
III. Experiences 119
8 Adaptation of the Learning Process within the AdeLE Prototype 121
8.1 Planning stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
8.2 Experiences gained . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
8.3 Other results from literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
8.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
9 Utilising a Dynamic Background Library for Adaptive E-Learning 131
9.1 Basic concept and realisation of EHELP . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
9.2 Evaluating the EHELP system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
9.3 The Dynamic Background Library for the AdeLE prototype . . . . . . . . . . . . . . . . 137
9.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
10 The Impact of a Didactical Strategy on Learning 141
10.1 Realisation of the courses regarding the learning theories . . . . . . . . . . . . . . . . . . 141
10.2 Comparison of the three e-learning strategies . . . . . . . . . . . . . . . . . . . . . . . . 143
10.3 Findings on didactical and pedagogical aspects . . . . . . . . . . . . . . . . . . . . . . . 148
10.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
11 Conclusions and Outlook 155
11.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
11.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
11.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
Bibliography 159
Index 179
vi

List of Figures
1.1 Overview of and connections between the nine chapters of this work . . . . . . . . . . . . 3
2.1 Formal description of a generic system . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Spectrum of adaptation in computer systems, adopted from [Oppermann, 1994] . . . . . . 14
2.3 Generic framework for an adaptation system . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 Formal specification of a multi-purpose adaptive system (types and variables) . . . . . . . 20
2.5 Formal specification of a multi-purpose adaptive system (operations and thread) . . . . . 21
3.1 Overview of research issues related to the learning process . . . . . . . . . . . . . . . . . 39
3.2 Formal specification of the content model . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3 Formal specification of the pedagogical model . . . . . . . . . . . . . . . . . . . . . . . 41
3.4 Formal specification of the didactical model (types and instance variables) . . . . . . . . 42
3.5 Formal specification of the didactical model (operations) . . . . . . . . . . . . . . . . . . 43
4.1 Model of adaptive instruction, adopted from [Park et al., 1987] . . . . . . . . . . . . . . . 53
4.2 Framework for adaptive e-learning, adopted from [Shute and Towle, 2003] . . . . . . . . 54
4.3 The KnowledgeTree architecture, adopted from [Brusilovsky, 2004b] . . . . . . . . . . . 55
4.4 Formal specification of the adaptation model . . . . . . . . . . . . . . . . . . . . . . . . 59
5.1 The development process of e-learning standards, adopted from [Gries, 2003] . . . . . . . 65
5.2 Enhancing SCORM’s structuring and content packaging specification . . . . . . . . . . . 72
5.3 Enhancing SCORM’s asset specification . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.4 “Semantic TAGging Editor” for inner-instructional objectives . . . . . . . . . . . . . . . 74
6.1 Architectural design of an adaptive e-learning environment . . . . . . . . . . . . . . . . . 83
7.1 Utilisation of the Tobii 1750 Eye-Tracking system [Gu¨tl et al., 2005] . . . . . . . . . . . 93
7.2 Overview of AdeLE’s architectural design . . . . . . . . . . . . . . . . . . . . . . . . . . 94
7.3 Implementation details of the Adaptive System . . . . . . . . . . . . . . . . . . . . . . . 96
7.4 Implementation details of the Modelling System, adapted from [Fro¨schl, 2005, p. 118] . . 98
7.5 Graphical user interface of the Modelling System [Fro¨schl, 2005, p. 154] . . . . . . . . . 99
7.6 Implementation details of the Concept-Based Context Modeller, adapted from [Safran,
2006, p. 84] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
7.7 Graphical user interface of the Concept-Based Context Modeller [Safran, 2006, p. 106] . . 101
7.8 Top of the Openwings Explorer displaying installed components . . . . . . . . . . . . . . 102
vii

7.9 Bottom of the Openwings Explorer displaying interfaces and services . . . . . . . . . . . 103
7.10 Sequence diagram for the scenario “learner navigates instruction” . . . . . . . . . . . . . 105
7.11 AdeLE system login dialog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.12 Registration form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.13 AdeLE prototype main menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.14 Dialog for course enrolment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.15 Overview of the learning progress for an example course . . . . . . . . . . . . . . . . . . 110
7.16 Form-based dialog to edit the user profile . . . . . . . . . . . . . . . . . . . . . . . . . . 110
7.17 Learner’s view of a course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.18 Example for an examination including an assignment task . . . . . . . . . . . . . . . . . 112
7.19 Tree-view navigation of the AdeLE system . . . . . . . . . . . . . . . . . . . . . . . . . 112
7.20 View of the navigation area with hidden elements . . . . . . . . . . . . . . . . . . . . . . 113
7.21 “Background Knowledge” section for an exemplary instruction . . . . . . . . . . . . . . . 113
7.22 “Why this way?” section for an example learner . . . . . . . . . . . . . . . . . . . . . . . 114
7.23 Form-based eye-tracking simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
7.24 Menu with additional functions for teachers . . . . . . . . . . . . . . . . . . . . . . . . . 115
8.1 Distribution of WAVI-factors given by the VICS tool (yellow triangles), students’ self-
assessment (green diamonds) and the AdeLE system after completing the course (red
squares) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
9.1 Basic functionality scheme of a DBL [Garcia-Barrios et al., 2002] . . . . . . . . . . . . . 133
9.2 EHELP viewing mode “embedded hyperlinks” [Garcia-Barrios et al., 2002] . . . . . . . . 134
9.3 Background knowledge data structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
10.1 Distribution of teacher’s (blue), students’ (pink) and overall (yellow) activities course A . 145
10.2 Distribution of teacher’s (blue), students’ (pink) and overall (yellow) activities course B . 146
10.3 Distribution of teacher’s (blue), students’ (pink) and overall (yellow) activities course C . 147
10.4 Comparison of the students’ activities for the courses A (green), B (yellow) and C (blue) . 152
viii

List of Tables
3.1 Bloom taxonomy [Bloom, 1956], adapted and extended for skills and attitudes . . . . . . 35
8.1 Characteristics of the students’ learning behaviour for each initial WAVI-group . . . . . . 125
8.2 Characteristics of the students’ learning behaviour for each pass . . . . . . . . . . . . . . 126
10.1 Statistics of the course’s educational objectives . . . . . . . . . . . . . . . . . . . . . . . 142
10.2 Characteristics of the three courses for the preparation stage . . . . . . . . . . . . . . . . 144
10.3 Characteristics of the three courses for the implementation stage . . . . . . . . . . . . . . 144
10.4 Characteristics of the three courses for the concluding stage . . . . . . . . . . . . . . . . 148
10.5 Objectives, competencies according to the Bloom taxonomy (Type: knowledge, skill, or
attitude; Level: 1-6) and the rates of attempts and successful achievements (overall and
for each course) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
10.6 Results of the post-questionnaires on the learner characteristics (each statement rated with
a number between one and five comprising the range from “absolute disagreement” to
“strong agreement”) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
10.7 Characteristics of the three courses based on the students’ ongoing documentation about
learning and raw database queries within the Moodle system . . . . . . . . . . . . . . . . 152
10.8 Results of the post-questionnaire concerning the factors relevant to learning (each state-
ment rated with a number between one and five comprising the range from “absolute
disagreement” to “strong agreement”) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
ix

1.3. Methodology and structure 3
1.3 Methodology and structure
In order to achieve these objectives, the overall work is separated in three main parts, as visualised in
figure 1.1:
• After this introductory chapter, the first part consists of three chapters on the most relevant theoret-
ical areas for this work, namely adaptation systems, technological-based learning and teaching and
adaptive e-learning.
• The second part describes practical aspects of adaptive e-learning, comprising aspects of standard-
ised learning content, general requirements for an adaptive e-learning environment as well as the
technical realisation of the AdeLE prototype.
• Finally, the third part deals with experiencing concepts of adaptive e-learning. Thus, the usefulness
and usability of the AdeLE prototype, the applicability of the Dynamic Background Library for
adapting the learning process and the impact of didactical strategies on online learning are evaluated.
Figure 1.1: Overview of and connections between the nine chapters of this work
Starting with the theoretical part of the book, chapter 2 examines the history and relevant research
streams of adaptation systems on the basis of an extensive literature survey and introduces the most

4 1. Introduction
relevant concepts in this area. Further, a general model for adaptation systems is developed. Due to the fact
that e-learning comprises human-system-interaction, the literature survey and the theoretical framework
include, amongst others, aspects of systems thinking, problem situations or human systems.
Chapter 3 deals with technology-based learning and teaching. Therefore, the three relevant learning
theories, i.e. Behaviourism, Cognitivism and Constructivism, are briefly highlighted. Further, pedagogical
aspects such as those factors which influence learning or the learner characteristics are pointed out. After
discussing didactical issues like defining competencies and learning objectives for a course or determin-
ing appropriate teaching and assessment methods, this chapter concludes with an overview of important
concepts and a formal model of technology-based learning and teaching.
In chapter 4, the central core of the book, a generic framework for adaptive e-learning, is introduced.
After reviewing historical approaches and adaptive educational systems, existing theoretical models are
introduced and inspected on the basis of findings on adaptation systems and technology-based learning
and teaching. Finally, prior to the conclusion of the theoretical part of this work, a new attempt towards
formalising adaptive e-learning is presented.
After these theoretical chapters, the practical part of this work starts with chapter 5 which deals with
standardised adaptable courseware. Thus, the standardisation process within the scope of e-learning
is outlined and requirements for standards to support adaptive e-learning are manifested. Subsequently,
the current specifications and standards are inspected and a selection of specifications enabling adaptive
e-learning is established for the AdeLE project.
Thereafter, chapter 6 highlights commonly-known adaptation methods and techniques and derives
concrete requirements for an ideal adaptive e-learning environment from the generic framework of the
theoretical part. Further, an architectural design for this system is presented and existing projects as well
as commercial products are examined according to the requirements stated in this chapter.
In chapter 7 the realisation of the AdeLE prototype is summarised from the planning stage to a
detailed description of the system. In addition to outlining the special requirements given by the eye-
tracking device and the Dynamic Background Library applied in this project, all functional units of the
system are pointed out. Further, relevant implementation details are explained, and the adaptation model
is described using a walkthrough of the prototype.
After pointing out the technical realisation of the AdeLE system, the third part of the book attempts
to prove the concept of “adaptive e-learning”. Therefore, chapter 8 starts with an evaluation of the
adaptation of the learning process within the AdeLE prototype. After describing the setup of an
evaluation study, experiences gained through this study are summarised and improvements of the AdeLE
prototype are presented. Further, on the basis of a literature survey, benefits of various adaptive e-learning
techniques and solutions are highlighted.
In chapter 9, the usefulness of a Dynamic Background Library is evaluated within the scope of
adaptive e-learning. Hereby, the basic concept of a dynamic background library, which comprises the
idea of retrieval-based instruction, is introduced and the applicability of the prototypical implementation
is examined by a case study. The chapter then describes experiences gained by this study in order to
redesign and realise the idea of a Dynamic Background Library for the AdeLE system.
Finally, chapter 10 describes another case study on the comparison of three different e-teaching
methods. After pointing out the strategy of the case study and showing the implementation of the different
courses, the implementation of the e-learning phase is depicted in detail and experiences about the need
to adapt teaching methods are highlighted. Concluding the study and the experiences with adaptive e-
learning, the book is summed up and an outlook for further work is presented.

I. Theoretical Background
5

Chapter 2
Adaptation Systems
“ To improve is to change;
to be perfect is to change often. ”
[ Winston Churchill ]
Adaptation is definitely not a new concept, in particular when it comes to considering related terms
derived from a great variety of different research areas such as biology, climatology, cybernetics, infor-
mation theory, system theory and so forth. Therefore, a system might be able to adapt its behaviour in
some way to fulfil a certain purpose, for instance to compensate for environmental changes [Ashby, 1960,
p. 207], to accommodate changes in the dynamics, parameters or disturbances of processes [Ogunnaike
and Ray, 1994, p. 1087], etc.
As a basis for further treatment with adaptive e-learning, this chapter deals with systems adapting parts
of themselves in general. Therefore, section 2.1 discusses some ideas from systems theory, philosophy
and methodology. Further, section 2.2 points out the relevant developments of systems theory. Section
2.3 defines the basic terms which are necessary in the context of adaptation systems and examines these
concepts and systemic characteristics by means of a theoretical model. Finally, section 2.4 introduces a
formal approach for an adaptation system which is based on the theoretical findings of this chapter.
2.1 Roots and related fields of systems theory
According to [Banathy and Jenlink, 2004], systems inquiry consists of three interrelated research areas:
systems theory, systems philosophy and systems methodology. While systems theory is about devel-
oping concepts and principles concerning systems in various sciences, systems philosophy deals with the
reorientation of thought and worldview. Systems methodology comprises models, strategies and tools
applying systems theory and philosophy in practice according to the analysis, the design, the development
and the management of complex systems.
7

8 2. Adaptation Systems
2.1.1 History of systems theory
Tracking back the roots of system theory, in the middle of the 20th century some basic principles and
concepts of a general system theory were set up by researchers coming from different sciences. Yet, the
history of systems theory has started much earlier as the following review shows:
• Some of the key concepts of systems theory – such as composing various interacting parts into
a whole, the separation from its environment and so forth – were firstly defined by the German
philosopher Georg Wilhelm Friedrich Hegel in the 18th century.
• [Francois, 1999] reports that the French psychologist Claude Bernard who had worked on the
internal milieu of living beings from 1854 to 1878 stated that there is a “difference between the
procedures inside a system and its environment”. Further, in 1866, the de Cyon brothers discovered
the first example of a self-regulating system, “the countervailing action of the accelerator and the
moderator nerves of the heart”.
• According to [Banathy and Jenlink, 2004], the term “general theory of systems” was first used by
the Hungarian philosopher and scientist Bela Zalai who developed his theory in the years 1913 and
1914. In addition, in his research work carried out from 1921 to 1927, the Russian scientist Alexan-
der Alexandrovich Bogdanov dealt with the field of Tektology, “a dynamic science of complex
wholes concerned with universal structural regularities, general types of systems, the general laws
of their transformation and the basic laws of organisation” [Bogdanov, 1984, p. ii (by Gorelik)].
• In 1932, the French scientist Walter Cannon introduced the biological concept of homeostasis,
which can be interpreted as an extension of Bernard’s idea of stability in the internal milieu. Be-
ginning with 1942, the French biologist Pierre Vendryes studied the feature of regulation in living
and non-living systems, extensively developing the concept of autonomy.
• The fields of Cybernetics and System Theory, two disciplines with a large history and often seen as
the roots of fields like Artificial Intelligence or Control Systems, were represented in the 1940’s by
a melting pot of research groups, where ideas matured, a general set of vocabularies for engineering
and physiology were introduced and the basic terms of system theory – such as learning, regulation,
adaptation, self-organisation, perception, memory and so forth – were created. Referring [Wiener,
1948], the American mathematicianNorbertWiener founded the discipline of Cybernetics in 1948.
• In the 1950s, the well-known Austrian-born biologist Ludwig von Bertalanffy introduced the idea
of a General System Theory (GST) redefining the boundaries of other disciplines such as mathe-
matics, biology, biophysics, psychology etc. [VonBertalanffy, 1956] defines systems as “complexes
of elements standing in interaction” and deals with concepts like organisation, wholeness, direc-
tiveness, teleology, control, self-regulation, differentiation and so forth. As a consequence, this
approach justifies the existence of general systems properties as well as organisation of complexity.
• In 1954, along with the American economist Kenneth Boulding, the American neurophysiologist
Ralph Gerard and the Russian-born mathematician-biologist Anatol Rapoport, von Bertalanffy
founded the Society for General System Theory – later renamed to International Society for the
Systems Sciences (ISSS). Without using the term GST, a similar theory was developed by the British
psychiatrist William Ross Ashby during the years 1945 and 1947, as published for example in
[Ashby, 1960].
Overall, systems theory is an important movement to develop and evaluate systems with respect to
a certain behaviour given by requirements, relevant properties and interaction with its environment. As
shown later on, adaptation systems require various concepts which are outlined in the (incomplete) histor-
ical overview above.

2.2. Further developments in systems theory 9
2.1.2 Systems philosophy
Dealing with aspects of systems theory’s benefits, [Banathy and Jenlink, 2004] state that “systems philos-
ophy is concerned with a systems view of the world and systems thinking as an approach to theoretical
and real-world problems”. Therefore, philosophical aspects are worked out in three directions: Firstly,
the ontological approach describes the systems view of the world. Secondly, epistemology researches the
system-internal representation of the world’s view. And finally, axiology is directed to the study of value,
ethics and aesthetics guided by the moral question about its meaning.
While the axiological concern of systems philosophy aims to ensure that systems inquiry is moral
and ethical and that the participants in system inquiry, i.e. developers, are constantly questioning im-
plications of their actions, ontological and epistemological aspects deal with researching and realising
system-internal models about the real world.
2.1.3 Systems methodology
Systems methodology can be considered as an important part of systems inquiry and, referring to [Banathy
and Jenlink, 2004], holds two important domains: “the study of methods by which we generate knowledge
about systems in general” as well as “the identification and description of strategies, models, methods
and tools for the application of systems theory”. Thus, systems methodology can be seen as a set of
methods and tools relevant to three application areas: (1) the analysis of systems and problems concerned
with systemic aspects, (2) the design, development, implementation and evaluation of systems and (3) the
management of systems and changes in systems.
Using systems methodology, four steps have to be regarded: First of all, the problem situation has
to be identified, characterised and classified. Secondly, the problem context must be determined and
described. Thirdly, the type of system in which a certain problem situation is embedded has to be located
and specified. And finally, methods for specific strategies, tools for the given problem situations as well
as the type and the context of a system must be selected. Nowadays, the appropriate methodology can
be chosen from a wide range of methods and tools that are available. Exemplarily, the RESCUE process
introduced by [Jones and Maiden, 2005] can be mentioned here as a methodology to develop and evaluate
socio-technical systems such as e-learning environments.
Summarising this section, it has to be stated that systems philosophy, systems theory and systems
methodology are applied in the functional context of systems. Systems philosophy comprises methods
for defining and organising the principles constituting systems theory. Moreover, systems theory pro-
vides guidance for selecting, developing and organising approaches, methods and tools given by systems
methodology. As new application areas – for example approaches in the field of adaptive e-learning –
continue to make ever greater demands on systems and increase the complexity of them, researchers have
to consider systems theory and other research fields dealing with systems.
2.2 Further developments in systems theory
This section introduces selected research fields which were originated on the basis of the General System
Theory and are relevant to this work. With respect to adaptation systems and, further, to adaptive e-
learning, these six main areas are examined in the following subsections: (1) Hard-systems science, (2)
cybernetics, (3) systems thinking, (4) human systems, (5) problem situations and (6) systems design.

2.2. Further developments in systems theory 11
the perceptions of human actors using a system and structure groups of people working with a system on
the basis of their activities. Checkland defined a human activity system as a “notional system that expresses
some purposeful human activity that could be found in the real world”.
[Banathy, 1988] reports on a classification scheme for HAS on the basis of four dimensions: (1) the
openness of human systems, (2) their mechanistic vs. systemic nature, (3) the unitary vs. pluralistic
definition of their purpose and (4) the degree and nature of their complexity. As a result, the following
types of systems can be identified:
• Rigidly Controlled Systems are rather closed systems, have a simple structure and consist of only
a few elements with limited interaction between them. Normally, they fulfil one single purpose, like
assembly-line or man-machine systems, for example.
• Deterministic Systems are systems which can be defined as more closed than open and have clearly
defined goals. Their complexity ranges from simplicity to detail. Within such a system, a user has
a limited degree of freedom in the choice of the methods. Examples of deterministic systems are
bureaucracies or instructional systems.
• Purposive Systems, on the other hand, are defined as more open than closed, but are unitary. They
react to environmental influences in order to maintain their viability and allow people to select
operational means and methods completely freely, but with respect to the system’s purpose. Corpo-
rations, social service agencies or public education systems can be stated as examples of purposive
systems.
• Heuristic Systems formulate their own goals with respect to policy guidelines. They are open to
changes and even initiate such changes. Thus, they are based on a dynamic complexity, while their
internal arrangements and operations are systemic, as exemplified with innovative business ventures
or alternative educational systems for example.
• Purpose-Seeking Systems seek their own ideals and are guided by their own rules and vision. Being
considered as open, they evolve while co-operating with their environment. They are characterised
by a dynamic complexity and systemic behaviour as they constantly seek new purposes and search
for new niches in their environment. Examples are cutting-edge R&D agencies or communities
seeking to integrate their development with other organisations.
In the context of this work, classification of human systems is useful to assess and describe the extent
by which a system can be adapted or can adapt itself. An information system in general can be considered
to be deterministic, but an adaptation system might be classified as a purposive, heuristic or even as a
purpose-seeking system. As a conclusion, openness, complexity and purposes are important issues for
adaptation systems.
Addressing methodological aspects of HAS, the development of such systems requires socio-technical
approaches like RESCUE [Jones andMaiden, 2005] or, within the scope of e-learning in Small-to-Medium
Sized Enterprises (SMEs), the E-Learning Ecosystem (ELES, [Chang and Gu¨tl, 2007]) which comprises
an ecological, application-based approach to design e-learning environments. Overall, developing HASes
is a rather complex process due to the nature and complexity of human-system-interaction.
2.2.4 Problem situations and systems design
Referring to human systems, the area of problem situations which deals with systems comprising similar
problems can be identified. Although problems are unbounded and may be regarded to be subjective,
[Ackoff, 1981, p. 79ff] suggests that “a set of interdependent problems constitutes a system of problem”,

12 2. Adaptation Systems
namely a mess. A mess has, as any other system, properties that none of its parts has and, additionally, is
lost when the system is taken away or when the system is separately considered.
[Ulrich, 1983, p. 308ff] points out the difference between ill-defined (or ill-structured) problems and
well-defined problems in which the initial constitutions, the goals and the necessary operations are spec-
ified. As a consequence, science should only deal with well-structured or well-defined problems. Nev-
ertheless, many problems – in particular social problems – are naturally non-deterministic. Thus, every
solution to these problems can be considered to be incomplete. Ill-defined problems require continuous
systems design until the problem situation is clearly defined.
With the consideration of a design problem to be ill-structured, [Checkland, 1981, p. 149ff] states
that hard systems thinking fails, if systems design lacks the naming and definition of its objectives, while
human systems deal with valuing of individuals, collectives and the role of technology. Further, the
design approach focuses on several various segments of the system, in particular on problem situations
and solutions. [Churchman, 1971, p. 4] outlines that, if decision rules affect the state of the whole system,
the designer should not separate parts of it merely for stability reasons. Overall, systems design can be
comprised as a hard process including a large amount of communication. Successful design implies that
knowledge can be transferred into action, into another design for example.
Furthermore, [Checkland, 1981, p. 163ff] describes the development of a Soft Systems Methodology
(SSM) for the work with human activity systems and the use of system ideas to define basic mental pro-
cesses: (1) perceiving, (2) predicating, (3) comparing and (4) deciding to take action. This methodology
is derived from the concept of human activity systems by means of the attributes which are important for
all human activities. To summarise systems design, it has to be stated that designers have to consider an
entity to be designed as a whole in terms of the synthesis of the interaction of its parts. Therefore, all parts
of the system need to be designed interactively and at the same time, which requires co-ordination and
design for interdependency across all system levels that need to be integrated.
Generally, this section outlined six important mainstream developments founded on the basis of sys-
tems theory and relevant to adaptation systems. While hard-systems science deals with the design of the
system itself, cybernetics, systems thinking and human systems can be used to characterise and understand
adaptation processes within a system, for example by defining its complexity, examining information pro-
cessing processes and units and so forth. Finally, an analysis of problem situations as well as the aspects
of systems design that apply might be necessary to avoid ill-defined systems and to guarantee important
system properties such as the stability, the usefulness, etc. Although there are much more research fields
relevant to adaptation systems – for example human-computer-interaction, software development, etc.,
these areas are omitted in this work, because they are not necessary for the theoretical model described in
the next two sections.
2.3 Towards adaptation and related concepts
After summing up important concepts of systems theory and related mainstream developments, this sec-
tion defines and explains the most important terms in connection with adaptation systems to provide a
basic vocabulary for the other chapters of this book. Following systems methodology, the formal specifi-
cation language VDM++ (see [Fitzgerald and Larsen, 1998]) is applied to build up a theoretical model of
a general system and examine aspects of adaptation within this system.

2.3. Towards adaptation and related concepts 13
According to [Kossiakoff and Sweet, 2003, p. 3], a system can be defined as “a set of interrelated
components working together toward some common objective”. This definition implies that a set of inter-
acting parts collectively perform a significant function. Generally, a system can formally be described as a
collection (map) of components. Further, a component consists of a unique entity (within the system) and
owns a certain state (see figure 2.1). Although this specification appears to be rather simple to examine all
aspects of systems theory – it does not allow overlapping components for example – it suffices to describe
adaptation systems as demonstrated in this section.
Figure 2.1: Formal description of a generic system
Based on the formal description of a generic system the following subsections deal with basic con-
cepts, components, models and system properties relevant to adaptation systems. Moreover, the concept
of adaptation is examined in connection with users interacting with the system.
2.3.1 Adaptation, adaptability and adaptivity
[DeJong, 1975, p. 5] defines adaptation as “a strategy for generating better-performing solutions to a
problem by reducing the initial uncertainty about the environment via feedback information made avail-
able during the evaluation of particular solutions”. In the context of systems, the concept “adaptation”
describes the process of modifying a system in some way to reach a certain goal. According to the formal
model of a generic system, adaptation within a system can be defined as changing the state of at least one
of the system’s components. As a result, the overall system, or parts of it, could behave in a different way
and, further, system properties might change as well.
In this context, adaptation systems comprise all systems providing the possibility that the state of at
least one component can be modified. Exemplary systems allowing the adaptation of internal states can
be found in a broad range of fields, reaching from microcontroller over computer systems to organisations
in any kind of application area. A state-based approach to systems theory, like the Turing Machine or the
Von Neumann Machine described in [Mills, 2006, p. 17ff], may be of relevance for adaptive e-learning
environments, as will be outlined later.

14 2. Adaptation Systems
[Oppermann, 1994] introduces a “spectrum of adaptation in computer systems” and differentiates
between adaptivity (adaptive) and adaptability (adaptable). As both concepts deal with adaptation
systems, adaptability comprises the idea that the user initiates the adaptation, while adaptivity is about
systems automatically adapting themselves according to some prescription. As shown in figure 2.2, vari-
ous steps between full adaptive behaviour and pure adaptability can be identified.
Figure 2.2: Spectrum of adaptation in computer systems, adopted from [Oppermann, 1994]
With respect to the formal description of a generic system, adaptability addresses the adaptation trig-
gered by a user, while adaptivity implies that at least one component is able to modify the state of at least
another component of the system. According to [Brusilovsky, 2003a], meta-adaptation implies that a
user or the system itself adapts the components which are responsible for automatically modifying states
of other components.
2.3.2 Necessary components and models
The concepts of adaptation, adaptability and, particularly, adaptivity require a number of special com-
ponents within a system. Concerning adaptation in general, the set of adaptable objects comprises all
components which the state can be altered for. Thus, an adaptation system must have at least one of these
components.
To realise real adaptive behaviour within a system, two important systemic parts are required: On
the one side, an adaptive component, which can be defined as a component adapting the state of other
components, is necessary. In addition, an adaptive engine (or “adaptive system”) comprises all adaptive
components within a system. On the other side, a system-driven adaptation of components requires various
models to decide, which components of the system have to be adapted on the basis of which information,
when, in which way and why (see also [Brusilovsky, 1996] or [Specht, 1997, p. 14ff]):
• First of all, adaptation information determines what is used as a source of adaptation. Users or
the knowledge about users are often considered to be relevant to adaptation, as can be concluded
for example by [Benyon, 1993]. However, adaptation can be based on any state in the system’s
environment. Therefore, an adaptation system includes at least one component observing its envi-
ronment and assessing the relevant states. The set of these components defines the environmental
model, which might, for instance, be a user model.
• Secondly, adaptation rules are necessary for the decision to start the adaptation process. These
rules based on the adaptation information can be regarded as the trigger for adaptation. With re-
spect to the formal description of a generic system, this rule engine could be realised as an own
component.

16 2. Adaptation Systems
valuable adaptation. An ill-defined adaptation process might lead to non-deterministic situations,
such as confusing the user, worsening the systems usability or even cancelling its usefulness, as
stated by [DeBra, 2000].
As a result of these considerations, systemic characteristics which are relevant to adaptation systems
are examined according to the formal model of a generic system and defined as follows:
• As mentioned above, the complexity of a system is determined by the internal structure, for instance
according to von Bertalanffy’s GST, as well as by its behaviour, with respect to cybernetics for
example. Other properties like the system’s flexibility, its reliability or its overall behaviour highly
depend on this internal structure and the interaction between the components.
• Another characteristic relevant to adaptation systems is the concept of self-organisation which de-
scribes a system’s ability to reorganise its components and their interactions. As a result, a system
might provide another or even a new structure or systemic behaviour, as stated for example in [Ger-
shenson and Heylighen, 2003]. Concerning adaptation, self-organising systems can be determined
as adaptive systems (see also [Heylighen, 2003]).
• According to [Skyttner, 2001, p. 58], openness defines how a system is dependent upon its environ-
ment, to exchange matters, energy and information for example. Thus, an open system allows itself
to be controlled by external information or fuelled from outside by some form of energy, while a
closed system does not provide interfaces to its environment. Moreover, autonomy in this context
means that a system acts on its own, without being dependent on other environmental entities.
• As part of a system’s openness, observability comprises the concept that the system’s behaviour
is observable from the outside in some way. To realise an observable system, it needs to be open
to enable insights into its functionality. Yet, this information channel into the system might not
provide the possibility to control the system. As explained in the next subsection, observability is
of particular importance to user-adaptive systems.
• Then again, controllability also deals with openness, but requires an information channel control-
ling the system. Again, this systemic property is relevant to adaptable systems, i.e. to allow a user
modifying a component’s state, as well as for adaptive behaviour to let the user the control the
adaptation process.
• Purposiveness sums up all purposes a system serves. Although this term is rather a philosophical
one – as stated for example by [Baz 2005] – it can be used to characterise a system according to its
purposes. Such a classification scheme can reach from a single-purpose over a multi-purpose up to a
purpose-seeking nature. In the context of adaptivity, meta-adaptivity can be seen as purpose-seeking
behaviour.
• Intelligence and learnability describe two further closely related terms. As these two characteris-
tics are often associated with humans, intelligent or learnable systems require that adaptation targets
are considered in the future systemic behaviour. Similarly to the idea of a purpose-seeking nature,
learnable systems such as Intelligent Tutoring Systems (ITS) are hard to be realised and often work
in a very restricted domain or context only, as stated for example by [Park and Lee, 2004].
• The concept of feedback addresses two important issues of adaptation systems. On the one side,
feedback deals with controllability of systems which is particularly necessary for adaptable systems.
On the other side, feedback also comprises the assessment of states of a system’s environment. As
already mentioned, feedback is needed to guarantee accurate and timely models of the adaptation
information and, if available, the adaptation targets.

2.3. Towards adaptation and related concepts 17
• In the other direction, feedforward describes the adjustment of a system’s output according to an
ideal model. In certain situations adaptive systems could also implement some kind of feedforward
strategy.
In addition to these systemic attributes, there are a great variety of other concepts and characteristics,
in particular about evolutionary, structural or behavioural aspects of systems (e.g. stability, vulnerability,
sensitivity, etc.). As these aspects are not principally relevant to this work, they are not discussed any
further. However, in practice, adaptation systems often deal with user-centred issues, which are examined
in the following subsection.
2.3.4 Adaptation towards users
Many approaches and solutions within the scope of adaptation systems aim to adapt to users, i.e. often
described with concepts like personalisation, customisation, user-adaptive systems or even adaptive sys-
tems. In addition, [Weibelzahl, 2003, p. 18f] itemises typical functions of adaptive systems – for example
supplying to find information, tailoring information, recommending digital artefacts, adapting the user in-
terface, etc. [Dreher et al., 2004a] – each one dealing with the user as the adaptation target and adaptation
information at the same time.
Moreover, some researchers consider personalisation to be a synonym for adaptivity, as outlined by
the following exemplary statements: [Benyon and Murray, 1993] define an adaptive system as a system
“which can alter aspects of their structure, functionality or interface in order to accommodate the differing
needs of individuals or groups of users and the changing needs of users over time”. [Jameson, 2001, p. 4]
considers a user-adaptive system to be an “interactive system which adapts its behavior to each individual
user on the basis of nontrivial inferences from information about that user”.
Contrary to these viewpoints, the formal model of a generic system (see figure 2.1) and the definitions
of former subsections allow a precise definition of concepts related to user-based adaptation:
• As mentioned above, adaptability means that the user modifies states of a system’s components.
Concerning the adaptation target, this kind of adaptation might have an effect on general aspects,
for example the system’s internal structure, data persistence processes, etc., or on aspects concern-
ing the user, such as the system’s usefulness or usability. The last case comprises the concept of
customisation, which can be seen as a user-driven adaptation in order to accommodate needs of
one self or of other users.
• Similarly, personalisation describes the process of automatically adapting systemic states towards
the needs of a user. Contrary to adaptivity, personalisation implies that the user is the adaptation
target, while adaptive behaviour might aim at any other issue which is not relevant and visible for
the user of the system. The personalisation process requires a user model which has to be applied
as adaptation information and might also be utilised to model the adaptation targets for evaluation
reasons.
In the context of user-centred adaptation, two important characteristics can be outlined as follows:
Scrutability, as mentioned and recommended by [Kay, 2000], comprises the extent of systemic observ-
ability necessary for a user to understand the personalisation process which is based on an internal user
model. On the other side, controllability – as defined in the last subsection – has to include aspects of
personalisation, so that the user has sufficient control over the adaptation process. However, controllability
of a system itself could be an adaptation target as well. At least, the degree of controllability ought to be
determined carefully, as shown in a study by [Jameson and Schwarzkopf, 2002].

2.4. A generic approach to adaptation systems 19
simply adjust these adaptable objects manually. On the other side, an adaptive component could observe
different models and initiate the adaptation process on the basis of rules and by means of executing so-
called adaptors.
Figure 2.3: Generic framework for an adaptation system
The left side of figure 2.3 shows the adaptive behaviour which consists of different models, a trigger
and a set of adaptors implementing the adaptation itself. The models can be seen as internal representations
of the real world and might be located within the adaptive component, but also be provided by other
components, by an own modelling component or even by an external system for example. The models
have to be updated according to states from the real world, by environmental or user states or even states
of systemic components for example. Applying a user model as adaptation information would lead to
personalisation, as already stated in the last section.
The adaptive component consists of the internal models, a trigger applying the adaptation rules and
the adaptor implementing the adaptation procedures. Further, the adaptors are responsible for carrying out
the adaptation process by modifying the systemic states of the adaptable objects. As a matter of course,
an adaptor might also be a part of the set of adaptable objects, which would describe the concept of meta-
adaptivity. Moreover, adaptation information or adaptation targets can also focus on systemic states, to
adapt according to some systemic aspect or to measure the adaptation effects for example.
The right side of the framework shown in figure 2.3 describes the process of adaptability and cus-
tomisation. If a user is allowed to modify systemic states without having a benefit, the system can be
considered to be adaptable. Additionally, if user-triggered adaptation of systemic states also changes the
usefulness or usability, a system is also customisable.
2.4.2 Formal model of a multi-purpose adaptive system
In the context of the generic framework for adaptation systems, the adaptive component is of particu-
lar relevance. While adaptability and customisation can be realised easily, adaptivity and personalisa-
tion requires an adaptive component, which is more complex, because adaptive behaviour requires more
functions as outlined in the last section. Considering purposiveness, this subsection describes a generic
framework for a multi-purposive adaptive system by utilising the formal specification language VDM++
as addressed for example in [Fitzgerald and Larsen, 1998].

24 2. Adaptation Systems
generic system, the most relevant concepts of adaptation systems – adaptation, adaptability and adaptiv-
ity – were defined and elements necessary for the adaptation process were described. Moreover, related
concepts like meta-adaptivity and user-centred adaptation were also examined.
Based on the historical review of systems theory and the definition of basic concepts in the scope of
adaptation systems, this section attempted to build up a formal framework for a multi-purpose adaptive
system, which can be applicable for planning or evaluating adaptation systems. As adaptation systems are
not the main issue in this book, the formal approach is not evaluated any further, but utilised for examining
theoretical and practical issues in the scope of this work. Therefore, the next section gives an overview
of technology-based learning and teaching to provide important basics for the central part of this work,
which deals with adaptive e-learning in theory and practice.

Chapter 3
Technology-Based Learning and Teach-
ing
“ To lecture, or to be lectured: that is the question. ”
[ Shakespeare’s Hamlet, freely adapted by the author ]
E-learning – as a synonym for technology-based learning and teaching – is identified as one of the
emerging areas, as shown by means of concrete numbers in [Brennan, 2003] and has turned out to be
important for educational institutions as well as for companies, as highlighted by concrete application
scenarios in [Dietinger, 2003, p. 21f]. Nevertheless, various problematic aspects such as higher costs and
political influence [Noble, 2001], the focusing on technology and the negligence of pedagogical principles
[Park et al., 1987], usability problems of e-learning systems [Ardito et al., 2004], etc. were reported.
According to [Gunawardena and McIsaac, 2004], a shift from technology to pedagogy-based research can
be observed within the field of distance learning. Educators have become more interested in examining
pedagogical themes and strategies within online courses instead of experimenting with new technologies.
[Jain et al., 2002, p. xi] states that e-learning concerns learning as well as teaching. Therefore, this
chapter contributes to the application of technology for distance education along three dimensions. First
of all, section 3.1 examines generally accepted learning theories in the area of distance learning. There-
after, section 3.2 comprises pedagogical issues, like factors influencing the learning process or learner
characteristics and section 3.3 deals with didactical aspects of online courses, such as the definition and
assessment of learning objectives. Concluding this chapter, section 3.4 introduces a framework giving an
overview of relevant concepts and a formal model technology-based learning and teaching.
3.1 Relevant learning theories
With respect to [Oblinger and Hawkins, 2005], “e-learning” is currently used for different educational
scenarios in literature. Therefore, at this point, this term has to be defined by characterising it according
to the following scenario: E-learning deals with running an online course entirely virtually over a certain
period and aims at mediating a set of competencies by means of objectives, learning materials and instruc-
tions. All interactions between the learners (students) and the instructor (teacher) are accomplished online
utilising an e-learning platform. Further, the assessment of the knowledge transfer as well as grading is
also conducted online.
Implementing e-learning courses can be seen as a complex process going beyond systematically ex-
ecuted steps within an instructional design model. Among a large number of critical aspects, [McLeod,
25

28 3. Technology-Based Learning and Teaching
To sum up this subsection, cognitive psychology focuses on learners’ receiving and processing of
information to transfer it into long-term memory for storage. Therefore, instructional designers have to
consider different aspects beginning with chunking the learning content into smaller parts and supporting
different learning styles up to higher concepts such as motivation, collaboration or meta-cognition. Al-
though the cognitive-focused approach is well suited for reaching higher-level objectives, a major weak-
ness can be identified, if a learner lacks relevant prerequisite knowledge. To account for this, a course
designer has to ensure that the instructions are appropriate for all skill levels and experiences, which is
evidently costly and time-consuming.
3.1.3 Constructivism
The constructivist school of learning suggests that “learners construct personal knowledge from the learn-
ing experience itself” [McLeod, 2003]. Thus, learning can be seen as an active process and knowledge
cannot be received from someone else or from outside. According to [Duffy and Cunningham, 1996],
learners should be motivated to construct knowledge rather than being taught through instructions. Fur-
thermore, constructivists emphasise situated learning, which emphasises learning within a certain context
and suggests strategies promoting multi-contextual learning to make sure that learners can apply knowl-
edge broadly.
With respect to [Dimock and Boethel, 1999, p. 5f], the following assumptions can be made regard-
ing this learning theory: Learning is an adaptive activity and situated in the context where it occurs.
Knowledge is constructed by the learner who also deals with resistance to change. Experiences and so-
cial interactions play a role in the learning process. By deriving implications for creating instructions for
online learning, the following statements have to be made up:
• Learning should be an active process. Therefore, students should carry out high-level activities,
such as asking learners to apply information in practical situations, discussing topics within a group,
asking for personal interpretation of learning content and so forth.
• To enforce learners constructing their own knowledge, instructors have to provide professionally
created, interactive instructions, since students have to show initiative to learn, to interact with
others and to control the learning agenda [Murphy and Cifuentes, 2001]. Contrary to traditional
lecture where instructors contextualise and personalise information to meet their needs, students
have to experience the learning content at first-hand.
• As stated for example in [Hooper and Hannafin, 1991], collaboration and co-operation should be
encouraged in the learning process. Students experience a real-life situation while working with
others, which facilitates the usage and improvement of their meta-cognitive skills in the following.
Grouping learners for a collaborative work should be according to expertise levels and learning
styles, so that team members can benefit from one another’s strengths, as also approved with the
importance of the expert role in online discussions [Hasebrook and Maurer, 2004, p. 103].
• Learners should have control of the learning process. Besides, some kind of guided discovery
should be provided in order to allow learners to make their own decision on learning goals, but also
to require the instructor’s guidance and feedback.
• In the learning process, students should have enough given time and opportunity to reflect on
the learning content. Questions on the content embedded in the course can be used to encourage
reflection and processing of the information.
• Learning should be made meaningful and illustrative by including examples and use cases for
theoretical information. Besides, activities should enforce learners to apply and personalise the
learning content offered.

30 3. Technology-Based Learning and Teaching
content itself, for example by pointing out the relevance for an instruction or including multimedia and
interactive elements such as games and simulations, as shown by the TRIANGLE software [Holzinger
et al., 2006]. Furthermore, it is advantageous to create competition within a learner group and adapt
to pre-knowledge in the subject domain to prevent the students from being unchallenged. For instance,
[Astleitner and Keller, 1995] describe a framework for adapting instruction to the learner’s motivational
state in computer-assisted instructional environments.
Thirdly, emotions have, similarly to motivation, a strong impact on the learning process, as outlined
by [Hasebrook and Maurer, 2004, p. 32]. [Tobias, 1987] points out findings on students’ performance
depending on anxiety, in particular test anxiety and proposes special methods for dealing with such prob-
lems. On the other side, an emotion – no matter whether a negative and positive one – may influence
learning due to its special nature. With respect to [Paulsen, 2005], “emotion is an unconscious arousal
system that alerts us to potential danger and opportunities”. Thus, addressing a learner’s emotional chan-
nel can be seen as a key cognitive process for transferring data into the short or even long-term memory.
Within e-learning the improvement of the learning process can be realised through emotions, for example
by storytelling, provocation, emotional figures and animations, group work, enabling confidence in the
learning content, etc.
Fourthly, knowledge transfer can be improved if learners can tie up to prior knowledge either in
the same domain or in a similar context. [Slavin, 2006, p. 181] states that “interference happens, when
information gets mixed up with, or pushed aside by, other information”. At the beginning, the degree of
mastery of the original subject influences the learning process [Bransford et al., 2000, p. 53]. In particular,
an adequate level of initial learning is required. Learners can then construct new understanding by tying
up to previous experience which may not have been activated yet. In this way, learners become capable
of understanding conceptual changes, adopt knowledge regarding their culture or everyday life and even
improve meta-cognitive abilities.
Research findings have shown that the higher the level of prior achievement within a domain or a
context, the less instructional support is required to accomplish a task [Tobias and Ingber, 1976]. Referring
to [Tobias, 1994], prior knowledge strongly relates to interest in the subject. Considering prior knowledge
within online courses, the macro-adaptive instructional approach described in [Park and Lee, 2004] deals
with the necessity to determine learning objectives, to define dependencies between instructional units, and
to assess the students’ competencies to grant access to restricted instructions. These aspects are highly
dependent on the learning content so that well-established e-learning standards – such as the specifications
of SCORM [ADL, 2004] – fulfil these requirements.
3.2.2 Learner characteristics
Drawing conclusions from the last subsection, a strong impact on learning is given by the individual
differences among learners, as stated for example by [Cronbach, 1957]. According to literature, each
learner differs from another by means of the following aspects, so-called learner characteristics:
• First of all, each learner has a unique profile of intellectual capabilities, which can be characterised
by Gardner’s Multiple Intelligences [Gardner, 1993] or various types of cognitive abilities described
in [Corno and Snow, 1986]. Education deals with the theory of multiple intelligences in two ways:
On the one side, teachers devise curricula addressing different intellectual capabilities. On the other
side, educators focus on the development of specific intelligences, of intra or interpersonal skills for
example. Although it is rather unmanageable to consider the learners’ intellectual abilities within
the classroom or e-learning situation, [Kelly and Tangney, 2003] applied Gardner’s theory within
an intelligent tutoring system named EDUCE.
• Secondly, learning preferences usually result from predispositions or orientations to learning and

3.2. E-pedagogy 31
can be seen as influences by the context [Jarvis and Woodrow, 2001]. [Dunn et al., 1989] classify
preferences by four different areas: (a) environmental, (b) emotional, (c) sociological and (d) phys-
ical. Preferences are considered by many e-learning environments in various ways, for example
by adapting the language or presentation of the learning content, group models, etc. Exemplary
systems can be found i.e. in the field of adaptive educational systems [Brusilovsky, 1996].
• Thirdly, researchers in the field of learning and teaching introduced so-called cognitive and learn-
ing styles which are somehow related to intellectual capabilities and preferences. Both kinds of
styles try to provide more practical models for teachers. Cognitive styles, such as field-dependence,
reflectivity versus impulsivity, haptic versus visual and so forth, characterise modes of perceiving,
remembering, thinking and decision making. Learning styles like holist versus serialist, percep-
tion styles, concept formation approaches, etc. try to describe the connection between instructional
presentation and materials with a student’s preferences and needs [Schmeck, 1988]. Overall, many
practical models like the WAVI model by [Riding, 1991] – for example applied within the AdeLE
project [AdeLE, 2006] – or the learning styles by [Kolb, 1984] – realised in the AHA! System [Stash
et al., 2004] for example – have been developed in the last decades.
• Fourthly, [Mo¨dritscher et al., 2004c] highlight constitutional attributes and states of learners,
which may deal with physical properties of the body like disability, age, amblyopia, etc. as well
as with short-term states of students, such as tiredness, concentration, emotional and motivational
states and the like. Both directions are already well-examined and various systems try to consider
aspects of physical properties – for instance disabilities as stated in [Sanchez and Flores, 2004] – or
constitutional states of learners such as the learner’s attention [Ueno, 2004].
• Fifthly, self-efficacy and meta-cognition influence the learner’s achievement in the learning pro-
cess [Bandura, 1982]. Self-efficacy comprises a student’s evaluation of the ability to perform a
given task through different senses. Furthermore, meta-cognition stands for the awareness of the
process of learning and consists of two basic processes (see [Nelson and Narens, 1994], [Winn and
Snyder, 1996] or [Hasebrook and Maurer, 2004, p. 96]): (a) monitoring the learning process and
(b) adapting the learning strategy. According to [Park and Lee, 2004], meta-cognitive abilities ex-
amined by various researchers within the area of aptitude-treatment interaction (ATI) are closely
related to the learners’ experiences and have an impact on different variables, such as the degree of
feedback and tutoring, the locus of control, personality attributes and so forth. In particular, various
systems in the scope of adaptive hypermedia – for example by methods like adaptive navigation
support [Brusilovsky, 1996] – focus on learner control.
• Sixthly, the background knowledge of a learner comprising language and computer skills as well
as experience on a related situation by means of a familiar context may also have an impact on
learning. For example, [Campbell et al., 2004] report that students from abroad may have problems
with understanding the language. [Felder and Henriques, 1995] examined learning styles within the
scope of language education and found out connections with learning styles. Thus, [Mo¨dritscher
et al., 2005] suggest providing translations for problematic phrases to support the learning process.
Anyway, various approaches in the field of e-learning focus on experience of work in related areas,
the user’s profession, experience of using the platform (e.g. see [Brusilovsky, 1996]) as well as on
foreign language students (e.g. see [EPHRAS, 2007]). Further, [Akhras and Self, 2000] introduce
the INCENSE system offering the ability of identifying and analysing different learning situations
and, if necessary, automatically switching among them.
• Finally, the last and most relevant characteristic of learners involves the user’s prior knowledge
and experience in the domain. [Vassileva, 1996] differentiates between experience and real knowl-
edge about a topic, where experience determines the user’s model of a knowledge space, the way

3.3. E-didactics 33
should be limited, so that the learner is sufficiently challenged and self-efficiency is able to increase.
Thus, it is particularly important for e-learning to plan the time allocated for learning and the time really
spent on learning. As pointed out in [Dietinger, 2003, p. 31], it is one of the advantages of e-learning
that learners can go through the course materials at their own pace. Thus, deadlines must be realistic in
order to avoid frustrating of the students. The possibility to define and manage deadlines for instructional
units is provided by the commonly-known specifications for e-learning content and nearly each learning
management systems, even by open source solutions such as Moodle [Moodle.org, 2007].
Depending on the given learning objectives, issues like feedback and tutoring might be of relevance
for the learning process. In particular, if a course aims at mediating high-level objectives, skills or a certain
behaviour, it is important for successful learning to give immediate feedback (e.g. see [Thorndike, 1913]).
With respect to [Park and Lee, 2004], various research areas, such as aptitude-treatment interaction or the
micro-adaptive instructional approach, deals with giving feedback and technical solutions like intelligent
tutoring systems or techniques for natural language dialogues. Furthermore, [Mo¨dritscher and Sindler,
2005] suggest the application of methods such as simulations, games, automatically essay grading, quizzes
created with professional authoring software and so forth.
Finally, learning is also affected by the context in which knowledge transfer takes place. According
to [Bransford et al., 2000, p. 43], learners might be able to learn in a certain context, but fail to learn in
another one or to transfer the experiences gained to other contexts. Contextualised knowledge is regarded
only by few e-learning environments – one of them is the INCENSE mentioned in the last subsection. As
this issue is closely related to constructivistic theory, new paradigms are of importance nowadays. One
idea in this scope is the application of a Dynamic Background Library [Dietinger et al., 1999] to support
context-driven learning [Mo¨dritscher et al., 2005].
Concluding this section, it has to be pointed out that the issues depicted so far comprise just the most
relevant and learner-centred factors of the learning process. A full overview of the complexity of learning
can be read in [Bransford et al., 2000, p. 31ff], for example. Nevertheless, it can be stated that the most
critical factor for successful learning is the learner. The most important difference between the classroom
situation and e-learning can be outlined with the statement that a teacher can adapt the learning process
much more effectively by holding a lecture in the class, since communication in both directions – from the
teacher to the learners and vice versa – is faster and more effective. Contrary to this, it is much harder to
evaluate a factor of the learning process or learner characteristic and react to it via an e-learning platform.
Therefore, research streams such as adaptive instructional systems or adaptive e-learning [Park and Lee,
2004] deal with aspects of adaptation in e-learning environments to improve the learning process.
3.3 E-didactics
Beside the pedagogical issues within the learning process, the viewpoint of teachers also plays an impor-
tant role for implementing online courses. Although cognitivists and particularly constructivists do not
believe that learning can be driven from outside (see also section 3.1), educators are able to give stimuli to
effect changes in knowledge, skill or attitudes of learners, as outlined at the beginning of the last section.
Moreover, didactics do have an impact on the learning process, as indicated with the learning theories
relevant for e-learning at the beginning of this chapter and shown by means of a case study in chapter 10.
Therefore, this section deals with didactical aspects of e-learning.
Teaching itself – no matter if in the classroom or via a learning platform – is a very complex task.
[Bransford et al., 2000, p. 191f] states that a teacher must not only cope with a course’s subject matter,
but also master different didactical skills to plan and run a course successfully. Backed up by literature
[Bransford et al., 2000, p. 131ff] and practical guidelines [IDS, 2002], the simplified didactical process
in this work consists of the following four stages: (1) the didactical planning, (2) the implementation of
a course, (3) the assessment of the learning process and (4) the course evaluation and revision. In the

34 3. Technology-Based Learning and Teaching
following subsections, these four stages are examined for the online teaching process by comparing them
with teaching in the classroom situation.
3.3.1 Didactical planning
Planning an online course comprises different tasks, beginning with collecting and assembling learning
materials, analysing the target group, defining learning objectives as well as determining learning activities
and assessment methods. Learning objectives are important to specify which competencies should be
mediated to the learners and to which extend should these competencies be mastered.
Learning objectives mainly depend on two issues: On the one side, it is necessary to consider param-
eters given by the organisation, for example the title of the course or crossovers to other courses. On the
other side and in accordance with [IDS, 2002, part II], the prior and the background knowledge of the
learners should be addressed by means of a didactical analysis on the basis of aspects of the last section,
particularly the learner characteristics.
With respect to didactics, the first step of the teaching process deals with the kind of competencies
to be mediated to the students. Therefore, [Durand, 2000] made up a theory on the basis of Howard
Gardner’s Multiple Intelligences and describes three main classes of competencies:
• First of all, knowledge can be seen as a kind of mental model about parts of the real world. In
other words, knowledge corresponds to a number of facts stored in an individual’s memory and
connected to other pieces of assimilated information. This dimension can be denominated as the
cognitive dominion.
• Second, a competency can also be a skill, which is related to the capacity of applying and using
acquired knowledge. According to [Bloom, 1956, p. 38f], a skill can be seen as process where
an individual uses appropriate techniques and information in order to examine or solve a problem.
Skills can be divided into intellectual skills, which are about mental processes manipulating infor-
mation and psychomotor skills, where a neuromuscular coordination is performed.
• Finally, attitudes are concerned with social or affective aspects. [Petry et al., 1987, p. 15] consider
attitudes to be “complex mental states of human beings that affect their choice of personal action
towards people, things and events”. An attitude can be seen as a feeling, emotion or a degree of
acceptance or rejection of a person to other persons, objects or situations.
A competency in practice is supposed to consist of more than one of these classes. In most cases
a strong focus on one of these three classes can be recognised, but there can also be adequately mixed
competencies.
After considering what should be taught within a course, it is important to decide to which extent and
under which circumstances the competencies should be mastered by the students [Hasebrook and Maurer,
2004, p. 134]. Therefore, a teacher has to define learning objectives following some kind of taxonomy, for
instance the one by [Bloom, 1956]. For the cognitive domain, the different levels of objectives are the
following ones:
• The lowest level of objectives is about recognising and recalling assimilated information.
• Based on these abilities, a student can comprehend and explain what he internalised.
• In the next step, the gained knowledge can be applied in new situations.
• At the analysis level, the student is able to analyse, structure and organise the facts and concepts.

3.3. E-didactics 35
• Synthesis describes the ability to reassemble the pieces of assimilated information to create new
knowledge.
• At the highest level, a student can even evaluate the value of ideas and cognitive materials.
Level Cognitive Domain Psychomotoric Domain Affective Domain
I Knowledge Imitation Receiving
II Comprehension Manipulation Responding
III Application Precision Valuing
IV Analysis/Synthesis Articulation Organisation
V Evaluation Automation Practicing what you preach
Table 3.1: Bloom taxonomy [Bloom, 1956], adapted and extended for skills and attitudes
These different levels of learning objectives can be also made up for skills and attitudes, as shown in
table 3.1. Furthermore, it is possible to define additional conditions for each objective, such as the usage
of a tool or the time extent. In general, the procedure for creating the learning objectives for a course starts
with defining very abstract objectives which are broken down to the detail subsequently.
In practice, cognitive psychologists examine learning on the basis of competencies, relations between
them and learners’ knowledge states. For instance, [Albert and Hockemeyer, 1997] highlight hypertext-
based learning with respect to the so-called “Knowledge Space Theory”, a formal approach to model
competencies and their interdependencies. [Albert and Hockemeyer, 2002] report on applying the knowl-
edge space theory for creating and adapting the course structure, for adaptive assessment of the learners’
knowledge state and for adaptive training.
The stage of didactical planning is necessary and equivalent for both traditional teaching and dis-
tance education. Moreover, learning objectives have an impact on the instructional design as well as on
assessment of the learning process as pointed out in the next two subsections.
3.3.2 Implementation of a course
After the didactical planning of the course, a teacher has to deal with the following tasks. On the one
side, organisational and administrative requirements, such as the arrangement of the infrastructure, the
determination of the schedule, the provision of the learning materials, etc., have to be considered. On the
other side, a teacher has to select and determine the course’s curriculum. Contrary to didactical planning,
this stage is characterised by significant differences between traditional teaching and online courses.
As already indicated with the instructional strategies based on the commonly-known learning theories
in section 3.1, instructional design is a very broad field of study and can be found in various other sources,
for example in [Bransford et al., 2000, p. 131ff], [Jonassen, 2004], [IDS, 2002, part III] and so forth.
Nevertheless, a few statements about instructional design have to be manifested here:
• An instruction generally consists of a task assignment (activity) and learning material (knowledge
artefact). Instructions can be created by a teacher or a professional content creator, for example
applying own tools, or retrieved from a certain repository, for instance from a learning content
repository or the Internet. Often, teachers prefer creating their own instructions, although it is more
costly and time-consuming than reusing existing instructions or delegating this task to a professional
content creator, as stated for example by [Lennon and Maurer, 2003].

36 3. Technology-Based Learning and Teaching
• Instructions are delivered according to a sequence given by the teacher or randomly accessed by
the learner. There might also be dependencies between instructions, i.e. a learner has to complete
some instruction to access another one.
• The implementation of an online course – particularly the instructional design and sequencing
– mainly depends on the learning objectives determined within the didactical planning stage.
While [Bransford et al., 2000, p. 134] differentiates between learner, knowledge, assessment and
community-centred learning design, younger research streams address the relation between com-
petency types (learning objectives), learning activities and instructional strategies, which led to the
following approaches: (1) guidelines for choosing appropriate teaching methods according to given
learning objectives [Bransford et al., 2000, p. 136], (2) standardisation efforts for learning design
and didactical patterns [IMS, 2007d] and (3) workflow-based learning processes [Helic, 1995].
• From a technological viewpoint, this stage – as well as the assessment of the learning process
examined in the next subsection – is implemented within a learning platform. Therefore, it is rec-
ommended that such a system, a so-called Learning Management System (LMS) [Dietinger, 2003,
p. 41ff], provides many features in order to support a wide range of didactical strategies.
Holding lectures virtually offers a few advantages. Amongst others, [Dietinger, 2003, p. 23ff] outlines
that the learning process can be enhanced using visual and interactive content. Moreover, an online in-
structor might not require all the soft skills which are necessary for a teacher in the classroom situation.
However, an e-teacher has to deal with other competencies, like the usage of the learning platform, media
skills, etc. Further, [Mo¨dritscher et al., 2006b] showed that it is difficult to mediate affective, psychomotor
and any kind of higher learning objectives via distance learning. Finally, the assessment of the learning
process is harder within the e-learning situation as shown in the following.
3.3.3 Assessment in the e-learning situation
[IDS, 2002, part IV] points out the necessity of assessment which should be executed not only to grade
students, but also to measure the learning process. In addition, assessment methods have a great impact
on the students’ learning behaviour as stated by [Scouller, 1998]. Generally, the assessment of learning
is divided into two processes: On the one side, teachers should apply methods of formative assessment
in order to enhance the learning process and evaluate the factors of learning mentioned in the last section
[Bransford et al., 2000, p. 19]. On the other side, the achievement of the learning objectives defined by
the teacher has to be assessed by means of determining a mark (summative assessment).
Within traditional educational styles, a teacher might apply various methods, beginning with oral
questionnaires up to written exams. For online courses, assessment is often reduced to limited-choice or
open-ended questions. Limited-choice questions such as multiple choices are applied to reach lower-level
objectives like recalling facts. Open-ended questions like sentence completion, short answers, essays etc.
require students to formulate their own answers which do not have to be pre-determined. Referring to
[Scouller, 1998], open-ended questions can be used to evaluate higher-level objectives like applying or
evaluating assimilated knowledge.
Teachers have to consider which type of question they use for assessment depending on the level of
learning objectives, size of the class, reliability in grading, prevention of cheating, exam construction and
grading time and several other criteria. When examining the didactical aspects treated so far, the following
problematic areas can be identified within the e-learning situation:
• All kinds of competencies – knowledge, skills and attitudes – may be mediated within an e-learning
environment. Therefore, it is possible to create learning content including facts relevant to a learner,
instructions how to achieve a skill, or information about an expected behaviour. Thus, technology

38 3. Technology-Based Learning and Teaching
• [Lennon and Maurer, 2003] describe several approaches beginning with the usage of professional
authoring software up to a shift to the constructivistic learning paradigm. On the one side, auto-
matically generated crossword puzzles may be an enabler for the students’ interest and motivation
and have positive effects on assessing low to medium-level objectives of the cognitive domain. On
the other side, applying constructivistic learning methods requires a high level of students’ self-
motivation, but can reach high-level objectives in all domains as shown in the next few paragraphs.
• One aspect of constructivism deals with collaborative learning. In particular, group activities
requiring students to discuss a topic are a powerful element to extend the possibilities of e-learning
as outlined by [Piaget, 1977, p. 157]. As a conclusion, students may treat open-ended questions,
when they are working in groups.
• Another interesting concept of constructivism is a so-called peer assessment, which was applied as
one assessment method in the case study comprised in the next section. As described in [Bhalerao
and Ward, 2001], peer assessment may reach high-level objectives for all possible domains and
provide other advantages, such as using natural language processing, lowering the effort for the
teacher, etc.
• Finally, [Gredler, 2004] reports on applying games and simulations for e-learning which can be a
successful approach to reach high-level objectives, in particular for intellectual skills, but also for
mediating knowledge or internalising value systems.
Nevertheless, assessment in the classroom is much more efficient, as the teacher can easily realise
some method and adapt the learning process according to the learners’ feedback. Thus, researchers in
the field of technology-based learning and teaching try to implement such behaviour within e-learning
systems, as shown in the next chapter.
3.3.4 Evaluation and revision of courses
After completing a course and marking the learners’ achievements, a teacher has to evaluate and revise
it in order improve their own teaching skills as well as the upcoming courses. With respect to [IDS,
2002, part V], this stage of the teaching process can be realised by conducting formative and summative
evaluations, for example by using questionnaires, surveys, minute papers or other methods to get the
learners’ feedback. Evaluation and revision of courses are equal for both traditional teaching and e-
learning and can be seen as an important issue within any kind of educational organisation for quality
assurance reasons.
3.4 Towards formalising e-learning and e-teaching
Concluding this chapter, this section sums up relevant research issues relevant to technology-based learn-
ing and teaching. Therefore, an overview of these issues is given and a formal framework for learning con-
tent, pedagogical issues and didactics is built up as a basis for examining the field of adaptive e-learning
in the next chapter.
3.4.1 Overview of technology-based learning and teaching
As a consequence of the first three sections, the primary focus of technology-based learning and teaching
is, as with traditional teaching, the learning process which is often also named “knowledge transfer”.

3.4. Towards formalising e-learning and e-teaching 39
Figure 3.1 visualises that learning depends not only on certain factors, such as the motivation, emotional
states, the learner’s attention, etc., but also on certain characteristics of a learner and the didactical strategy.
Examining the learning process, for example towards its efficiency, is comparable to systems theory in the
way that the teacher can measure the learning outcome, i.e. by applying assessment or evaluation methods,
in relation to the input, by means of the efforts for planning and implementing a course.
Figure 3.1: Overview of research issues related to the learning process
Besides evaluating the learning process from a didactical viewpoint, it is also interesting how cer-
tain factors of learning or characteristics of the learner are related to or can be influenced by didactical
strategies. Such research questions are examined not only by psychologists or pedagogues, but also in
technological driven streams like adaptive e-learning, as shown in the next chapter. Moreover, chapter 10
reports on a study about the pedagogical and didactical impact of different e-teaching strategies.
However, at this point three relevant models for technology-based learning and teaching are intro-
duced. The following section therefore deals with learning materials by means of the course’s content,
while the further sections try to formalise pedagogical and didactical aspects of e-learning.
3.4.2 Content model
The learning content given as a set of artefacts, for example by a learning content repository, can be seen
as the basis of the content model. These atomic entities of the content – whether available in digital form
or as a reference to a real object – can be connected to certain domains and appear within determined
contexts. With respect to [Gu¨tl and Garcia-Barrios, 2005a] a domain can be modelled using concepts,
while a context is defined as a set of situations. To keep the content model simple, a domain consists of
a set of concepts, a context of a set of situations and learning content of a set of artefacts (see figure 3.2).
This model does not care about structuring a domain, a context or the content. Furthermore, artefacts
cannot be divided into sub-entities.

42 3. Technology-Based Learning and Teaching
3.4.4 Didactical model
The didactical model introduced in this subsection tries to consider the most relevant aspects of e-teaching
outlined in section 3.3. Therefore, the model deals with determining learning objectives, instructional
design as well as assessment methods for e-learning courses. Nevertheless, some simplifications pointed
out in the next few paragraphs had to be made due to the high degree of complexity of teaching, which
requires not only fundamental knowledge of the course’s domain, but also a large set of practical skills.
Figure 3.4: Formal specification of the didactical model (types and instance variables)
In detail, e-didactics consists of learning content, pedagogical aspects (both given by the models from
the last two subsections), a set of learners, the features provided by the learning platform, a sequence of
objectives, a sequence of instructions (the course itself) as well as information about mastering learning
objectives, the learning progress and the relevance and suitability of instructions (see figure 3.4). Having
these sets of basic instance variables allows description of an online course from a didactical viewpoint.
An exemplary scenario would be a course on a certain topic and realised for a group of learners using
an e-learning system. The online course would consist of a sequence of instructions and a set of objectives
to achieve, by means of a certain mastery level, whereby each instruction is relevant to one or more
objectives. Within the online learning process the progress of the learners is measured according to their
mastery of the learning objectives. Pedagogical issues and their suitability for instructions are ignored for
this simplified example.

46 4. Adaptive E-Learning
4.1.1 Macro-adaptive approach
First of all, the macro-adaptive approach which can be traced back to the 1970s is about adapting in-
structions on a macro-level by allowing different alternatives in selecting a few main components such
as learning objectives, levels of detail, delivery system, etc. In this approach, instructional alternatives are
selected mostly on the basis of the student’s learning goals, general abilities and achievement levels in the
curriculum structure. As adaptation decisions are determined before instruction – for example on the basis
of rules – the macro-adaptive instructional approach can be characterised by the concept of adaptability.
[Corno and Snow, 1986] provide a taxonomy for systematic guidance in selecting instructional
meditation – activating, modelling, participant modelling or short-circuiting knowledge – depending on
learning objectives – developing new skills or compensating students’ weaknesses – and student apti-
tudes – intellectual abilities and prior achievement, cognitive and learning styles, academic motivation
and personality.
[Glaser, 1977] introduces a more practice-oriented model for a macro-adaptive e-learning system
which supports defining preconditions for learning content, developing the appropriate competencies,
adapting to the students’ learning styles and achieving different types of instructional objectives accord-
ing to individual needs or abilities. On the other side, Glaser identified several conditions for a successful
implementation of this approach, which partially explains, why macro-adaptive instructional systems have
not been as popular as hoped.
4.1.2 Aptitude-treatment interaction
The second approach comprises adaptation of instructional procedures and strategies to specific stu-
dent’s characteristics. As suggested by [Cronbach, 1957], an e-learning environment serving a wide
range of students requires a wide range of environments suited for optimal learning of the individual. This
strategy termed as “aptitude-treatment interaction” (ATI) proposes different types of instructions or even
different media types for different students. Several studies have been conducted to find linkages between
learning and aptitudes. The most important classes of learner characteristics can be summarised with the
following ones: intellectual abilities, cognitive styles, learning styles, prior knowledge, anxiety, achieve-
ment motivation and self-efficiency (compare with the learner characteristics in section 3.2). Due to many
studies about measuring intellectual abilities, only a few experiences about the benefit for e-learning are
researched.
[Tobias, 1989] pointed out a number of difficulties for this approach like the dependency on the subject
area, the poor applicability to actual classroom situations, growing abilities during learning process, etc.
Therefore, he proposed an alternative model, the achievement-treatment interactions, to reduce some of
the difficulties. This model focuses on task-specific variables relating to prior achievement and subject-
matter issues. However, the fluctuating abilities and characteristics of the learner – a major problem of
the ATI approach – still cannot be solved by idea of achievement-treatment interaction. Furthermore, this
model also has the problem that useful information may be lost by not observing possible influences of
factors like intellectual abilities, cognitive styles, anxiety and motivation.
Another important concept of adaptive instruction is learner control which deals with supporting the
learning process according to different abilities of the students by giving them full or partial control over
the style of the instruction or the way through the course content. Therefore, [Snow, 1980] defines three
levels of control: (1) complete independence, (2) partial control within a given task scenario and (3) fixed
tasks with control of pace. Concerning learner control, it is proven that the success of different levels of
learner control is strongly dependent on the students’ aptitudes, for example it is better to limit the control
for students with low-prior knowledge.
Despite the problems of the ATI approach pointed out here, faith in this approach is still alive and the

4.2. Types of adaptive educational systems 51
4.2.4 Adaptive hypermedia
Starting about 1990, the field of Adaptive Hypermedia (AH) which is inspired by intelligent tutoring
systems arose. Adaptive Hypermedia Systems (AHS) try to combine hypermedia-based and adaptive in-
structional systems, where adaptive and personalising interfaces were integrated into hypermedia systems.
Functional aspects of AHS mean components that may not be visibly, such as an altering behaviour of the
“next” button of the interface. An AHS should “be based on hypertext link principles, have a domain
model and be capable of modifying some visible or functional parts of the system on basis of the informa-
tion contained in the user model” [Eklund and Sinclair, 2000]. AHSes have been employed for educational
systems, e-commerce applications, online information systems and online help systems. Because of its
popularity and accessibility, most adaptive educational systems have focussed on the Internet since 1996.
Adaptive hypermedia methods can mainly be divided into two areas of adaptation, the content-level
adaptation or adaptive presentation, where the content is assembled or presented in different ways or orders
[DeBra, 2000] and the link-level adaptation or adaptive navigation support, where links are generated
according to different methods like direct guidance, adaptive sorting, adaptive annotation and link hiding,
disabling and removal [Brusilovsky, 2000]. As an example of direct guidance, the system ELM-ART
generates additionally dynamic links to connect to the next most relevant node to visit. Contrary to this,
the HYPERTUTOR system hides links which are not relevant to the user’s current task. InterBook and
AHM are further examples of hypermedia systems applying the annotation technique, where links are
named according to the user’s knowledge.
Adaptive hypermedia systems include models for a user’s goals or tasks, domain and background
knowledge, preferences, etc. Consequently, these models are utilised for adaptation decisions. Further-
more, it is possible to assess the long-term interests as well as the short-term goals of users [Fink et al.,
1998]. In systems that consider these aspects, the user’s interests serve as a basis for recommending rel-
evant content. Moreover, individual traits, such as cognitive factors, personality and learning styles are
of importance, although researchers disagree about which characteristics can and should be used. Finally,
adaptation to the user’s environment is a new kind of adaptation fostered by web-based systems which has
become an important issue due to the usage of different hardware, software and platforms.
The introduction of hypermedia has had a great impact on adaptive instructional systems. While other
kinds of adaptive systems cannot be realised without programming skills, the adaptive courses for AHS
can be created with recent authoring tools, for example SmexWeb. However, there are limitations to AHS:
Often they are theoretically or empirically not well founded. In particular, the evidence of effectiveness
of AHS is shown only for some few aspects [Specht and Oppermann, 1998]. Moreover, [DeBra, 2000]
outlines that missing or omitted prerequisite relationships in AHS may guide the user to pages not relevant
or not understandable for them. Further, assessing learners’ knowledge states is the most critical factor for
a successful implementation of an AHS.
4.2.5 Other technologies in the field of adaptive e-learning
As new pedagogical approaches and technologies came up, adaptive e-learning was extended by innova-
tive systems which will be discussed within this subsection. First of all, the paradigm of constructivis-
tic learning brought up systems like Intelligent Constructivistic Environment for Software Engineering
learning (INCENSE) described by [Akhras and Self, 2000]. INCENSE offers features to analyse a time-
extended process of interaction between the learner and a set of software-engineering situations and to
provide a learning situation based on the learner’s goals to support further processes of learning experi-
ences rather than acquisition of target knowledge. As described by [Dietinger et al., 1999] and [Garcia-
Barrios et al., 2002], a Dynamic Background Library can be used in terms of constructivistic learning to
provide dynamically retrieved and up-to-date knowledge managed by experts.

52 4. Adaptive E-Learning
[Wood, 2001] reports on examples of tutoring systems based on Vygotsky’s zone of proximal devel-
opment. For example, ECOLAB, which helps children aged 10 to 11 years learn about food chains and
webs, provides challenging activities and the right quantity of assistance. Other examples of Vygotsky’s
approach are QUADRARIC, DATA and EXPLAIN. [Gredler, 2004] gives an overview of games and sim-
ulations, which can be used to mediate a model to the learner or provide a journey through a domain on a
playful way. Adaptation can be realised by different levels of complexity, levels of speed, or even tutoring
components. As examples like Underwater SeaQuest or SimCity points out, these kinds of elements are
not only applicable to children, but also to adults. Additionally, [Kuhn and Gudjonsdottir, 1999] report
about a virtual campus approach by means of a 3D multimedia educational environment named ViKar,
which improves online learning through providing a virtual reality with multi-user communication and
collaboration.
Systems considering the motivational state of the learner try to incorporate gaze, gesture, nonverbal
feedback, etc. to detect and increase students’ motivation. For example, COSMO includes a pedagogical
agent that can adapt its facial expression, its tone of voice, its gesture and the structure of its utterances
during its interactions with learners. Another system named MORE can detect the student’s motivational
state and reacts to motivate the distracted, less confident or discontented student [DuBoulay and Luckin,
2001]. An example of focusing and improving meta-cognitive skills is the Geometry Explanation Tutor
programme [Aleven et al., 2001]. As an effective meta-cognitive strategy, this system explains examples
or problem-solving steps to help students learn with greater understanding. The tutor is even able to
respond to incomplete statements in the student’s explanations.
Finally, [Park and Lee, 2004] report on systems implementing adaptive collaborative e-learning.
Such systems can be classified in terms of their application as follows:
• Computer-based collaborative tasks (CBTC) like the Envisioning Machines support group learning
and group activity by presenting a task for the group and providing collaboration via intelligent
coaching.
• Co-operative tools (CT) like the Case-Based Reasoning Tool or the Writing Partner describe sys-
tems taking over some of the burden of lower-order tasks, while students work with higher-order
activities.
• Furthermore, intelligent co-operative systems (ICS) like DSA, PeoplePower or the Integration-Kid
system can be seen as an intelligent co-operative partner, a co-learner or a learning companion.
• Computer-supported collaborative learning systems (CSCL) serve as a communication interface
such as a chat tool or a discussion group supporting collaboration between learners. Such systems
provide the least adaptability to learners.
Although these four kinds of collaborative systems are in an early developmental stage, they can be
considered as important aspects of adaptive e-learning, because they do not only facilitate group activities,
but also help educators gain further understanding of group activities and determine collaborative tools for
learning.
4.3 Existing theoretical models for adaptive e-learning
As adaptive e-learning has a rather long history, there already exist many theoretical frameworks and even
formal approaches. In this section the main categories for these models are examined by pointing out the
most prominent examples of each category.

54 4. Adaptive E-Learning
Furthermore, [Shute and Towle, 2003] comprise a framework for two types of assessment. As shown
in figure 4.2, adaptation may be related to the domain-dependent and domain-independent learner model.
Therefore, this approach differentiates between adaptation and instruction, while adaptation rules aim
to select an appropriate learning object and instructional rules deal with adapting the course structure.
Analysing this models, to some extent analogies to section 3.4 (content, pedagogical and didactical model)
can be found, although the last chapter did not deal with adaptation in e-learning environments so far.
Figure 4.2: Framework for adaptive e-learning, adopted from [Shute and Towle, 2003]

58 4. Adaptive E-Learning
a consequence, adaptive e-learning environments can mainly be classified as deterministic or heuristic
systems. In the case of meta-adaptation, such a system might also be a purpose-seeking one.
From the viewpoint of systems theory, adaptive e-learning systems can be seen as second-order cyber-
netics consisting of an observable part (user interface and presentation layer) and an observer (adaptive
component). Addressing systems thinking, an adaptive e-learning environment can be characterised as in-
telligent, if it implements meta-adaptive behaviour. Finally, it has to be stated that the adaptive behaviour
of an e-learning system can be described utilising a formal specification language like VDM-SL.
4.4.2 Influences of technology-based learning and teaching
Addressing aspects of e-learning and e-teaching examined in chapter 3, the following conclusions can
be drawn for the field of adaptive e-learning: First of all, two adaptation processes can be identified, a
didactical one and a pedagogical one. While didactical adaptation deals with domain-specific aspects
like the learner’s pre-knowledge or knowledge states only, pedagogical adaptation comprises the idea of
adapting the learning content according to certain learner characteristics, for example by means of the
learning styles or intellectual capabilities.
Secondly, didactical adaptation of the online learning process – which seems to be more important
from the viewpoint of a teacher – are closely related to e-teaching dealing with didactical input and out-
put, for example on the basis of the determined learning objectives and assessment of the learning process.
In this context, researchers in the field of adaptive e-learning are interested in the following issues: (1)
dependency between learning objectives and instructional design, (2) effective assessment methods for
different competency types and (3) creating and exploiting domain models in order to improve learn-
ing. Overall, e-didactics aims at optimising the instructional sequence, so that learners achieve the given
learning objectives in the most effective way.
Thirdly, the adaptation of learning with respect to pedagogical aspects is about delivering appropriate
instruction to the learner and adapting the user interface accordingly. Thus, this adaptation process can be
characterised as personalised content delivery and presentation. Research comprises the field of aptitude-
treatment interaction, for example the examination of certain learning characteristics such as cognitive or
learning styles, as well as the influences of such factors on instructional design and learning. Further, the
assessment of learner characteristics as well as didactical issues by using new methods or technologies is
currently a favourite subject of research.
Concluding these influences of technology-based learning and teaching, adaptive e-learning can be
understood as an attempt to implement didactical competencies, because teachers also have to assess the
learning process and adapt it to certain factors or learner characteristics. Therefore beside fundamen-
tal mastery of subject matter, teachers have to apply different skills, for example classroom assessment
methods, in order to improve the knowledge transfer within the classroom situation. As the realisation
of online courses differs in the way that the learning process and the teaching process take place asyn-
chronously, adaptive e-learning is one possible way to compensate the problem that the teacher cannot
influence learning directly and immediately.
4.4.3 Model for adaptation of the learning process
Formalising adaptive e-learning requires, as already shown in the last chapter and the inspection of existing
theoretical approaches, the following models: (1) a content model, (2) a pedagogical model and (3) a
didactical model can be described. These three models were already introduced in section 3.4. As adaptive
e-learning was defined as an attempt to implement didactical skills, adaptive behaviour of a learning
management system can be described by simply extending the didactical model with respect to possible
adaptation methods, which is done for example in figure 4.4. The three models of the last chapter together

66 5. Towards Standardising Adaptable Courseware
domain knowledge, calculation of the best-fitting path through the course, providing background
information and so forth.
• Fourthly, a trend towards adaptation of learner-centred aspects is recognisable in the field of e-
learning. For example, [Stash et al., 2006] extended the AHAM reference model as well as the
AHA! system with pedagogical aspects like the learning style. Therefore, specifications about the
learners – such as IMS “Learner Information Packaging” (LIP) [IMS, 2007c] or IEEE “Public and
Private Information” (PAPI) [IEEE, 2001] – as well as connections between learning content and
learner characteristics are targeted by research projects.
As a conclusion it can be stated that many pedagogical and didactical aspects can be described with
the standards and specification drafts available at this time. Nevertheless, there still are a lot of open
research issues and, additionally, more development work has to be done on e-learning specifications in
order to fulfil requirements of adaptive e-learning. Thus, the following section outlines those requirements
relevant to adaptation of online learning.
5.2 Requirements for standards to support adaptive e-learning
In accordance with the formal model of adaptive e-learning introduced in the theoretical part of the book,
four main categories for requirements for adaptive e-learning standards can be identified. The first one
comprises all aspects of describing the learning content itself. The second one deals with pedagogical
issues, while the third one addresses didactics. The fourth and final category is about adapting the learning
process according to the three methods introduced in section 4.4.
5.2.1 Requirements for learning content
The first part of the FORMABLEmodel dealt with learning content. Generally, learning content comprises
assets – atomic elements like a picture, a paragraph, etc. – and learning objects which define a digital
resource – an asset or an aggregated object – that is used to support learning [DCMI, 2002]. As the
FORMABLE model is not aware of aggregating learning objects from assets, atomic parts of the content
are subsumed under the term “knowledge artefacts”.
Yet, the content model of FORMABLE pointed out, that content can be seen as a set of artefacts and
is related to a certain context and a certain domain. Therefore, it is necessary to define the relevancy of
artefacts for given situations and concepts, in practice they are linked to learning objectives, which can be
understood as tuples of a situation and a concept. Moreover, a learning objective can also be dependent
on others, as teachers have to define and consider didactical dependencies. As a result, artefacts of the
learning content also underlie dependencies, as is formally described for example by knowledge space
theory and its application [Albert and Hockemeyer, 1997].
Derived from these theoretical considerations, the following concrete requirements for learning con-
tent can be manifested here:
(A1) Defining different types of assets (e.g. text, picture, audio, video, a hyperlink or even a link to a
knowledge domain or concept)
(A2) Supporting different types of learning objects (e.g. content, exercises, examination, etc. and any
combination of these types)
(A3) Providing different levels of detail for a learning object (e.g. to address different levels and types of
learning objectives)

5.2. Requirements for standards to support adaptive e-learning 67
(A4) Separating content and presentation for a learning object and offering different visual representation
variants (e.g. for a certain device, browser or bandwidth)
(A5) Creating a learning object (knowledge artefact) through aggregation of different assets
(A6) Modelling knowledge domains and their concepts including overlapping domains or concepts
(A7) Modelling contexts and their situations including overlapping contexts and tasks
(A8) Modelling tuples of concepts and situations and dependencies between them, regarding the preven-
tion of a dependency loop
(A9) Mapping a learning object to concepts of domains and contextual situations
Aspects of structuring the course, for example into modules, as well as the visualisation of such
structures are, from the viewpoint of adaptive e-learning, not of primary interest and, thus, treated as an
LMS feature (see also next chapter). On the other side, these requirements are primarily focussed on
standards and specifications describing learning resources, such as IEEE LOM, Dublin Core Metadata
[DCMI, 2007], IMS Content Packaging [IMS, 2007a], etc. and content models, like IMS Competency
Definition [IMS, 2007f] or specifications from the field of concept modelling and topic maps.
5.2.2 Pedagogical requirements
As the second part of FORMABLE comprises the pedagogical model, standards have to consider such
aspects. This model deals mainly with learner characteristics, but the environment could also be of inter-
est. Consisting of a mapping from learners, concepts and situations to states, the pedagogical model looks
rather simple, but allows the definition of any possible combination of these three dimensions. Further-
more, this approach also points out that assessment of such pedagogical states is the primary target in the
field of pedagogy, which of course is not always possible or easy, as can be manifested for online learning.
From the more practical viewpoint, the following requirements for e-learning specifications can be
derived from FORMABLE’s pedagogical component:
(B1) Defining static and dynamic information attributes of a learner
(B2) Providing management (like storage, deletion or update) of attributes in real-time, for example the
actual constitution
(B3) Supporting an enhanced learner tracking and modelling (e.g. observing the learning process, the
paths through the courses, all learning objects and assets viewed or the learner’s constitution)
(B4) Mapping a learning object to a learner’s characteristics (e.g. language, accessibility, learning style,
multiple intelligence, etc.)
Overall, pedagogical requirements for standards mainly deal with user profiling specifications, such
as IMS LIP or IEEE PAPI. Nevertheless, the second and third requirement also is a recommendation for
XML-based formats due to the fact that they support the provision of a data object model for real-time
operations. Further, environmental states are also often included within such user profiling specifications.
Concluding this subsection, it has to be outlined that pedagogical aspects described in the user profile are
often utilised as adaptation information, for example by means of a learner model, in the field of adaptive
e-learning.

68 5. Towards Standardising Adaptable Courseware
5.2.3 Didactical requirements
As LMS-centred aspects of FORMABLE’s didactical model are examined in the next chapter, the fol-
lowing requirements for standards are focussed on here: On the basis of the content and the pedagogical
model, standards need to deal with describing objectives, learning activities and instructional sequences.
Furthermore, FORMABLE also comprises the idea of determining the relevance of instructions for given
learning objectives, defining the suitability of instructions for learners and assessing the learning progress
according to given mastery levels.
Therefore, from a didactical viewpoint a specification has to fulfil the following requirements:
(C1) Allowing to change the order of the instructional sequence (of all instructions not visited yet)
(C2) Providing different types to sequence instructions (e.g. linear, conditional branches, loops, etc.)
(C3) Allowing the insertion of instructions into the instructional sequence (again under the restriction
that this new instruction cannot be inserted right after one already visited by the learner)
(C4) Defining pre and post-conditions for instructions (e.g. according to learner characteristics, certain
situations and concepts or a given learning objective)
(C5) Assessing mastery level of learners applying adequate activities (e.g. quizzes, submission tasks,
etc.)
(C6) Mapping instructional sequences to learning objectives (e.g. didactical strategies along a learning
paradigm)
(C7) Mapping instructional sequences to pedagogical states (e.g. learning units suitable for different
learning styles)
As can be concluded so far, the FORMABLE model is useful to describe the first five requirements
on the basis of the instance variables and the last two ones on level of whole courses. In practice, the
didactical requirements concern specifications like IMS Learning Design. Yet, there are also requirements
which build up on the last three subsections and focus on adaptation of the courseware.
5.2.4 Requirements concerning the adaptation process
This category of requirements for adaptive e-learning standards is about the three methods to adapt the
online learning process outlined in section 4.4: (1) adaptation of instructions, (2) adaptation of the instruc-
tional sequence and (3) adaptation through providing additional instructions. Therefore, requirements for
standards mainly refer to the requirements of the last subsections and can be summarised as follows:
(D1) Defining rules observing pedagogical and didactical states and models (A6-A9, B1-B4, C4-C5) and
triggering adaptation of instructions (A1-A5)
(D2) Defining rules observing pedagogical and didactical states and models (A6-A9, B1-B4, C4-C5) and
triggering adaptation of the instructional sequence (C1-C2)
(D3) Defining rules observing pedagogical and didactical states and models (A6-A9, B1-B4, C4-C5) and
triggering the insertion of new instructions (C3)
These three types of rules already point out which requirements concern the adaptation information
(mappings, models and state assessments) and which requirements deal with the so-called adaptors. Ex-
amples of specifications in this scope resulted by various projects and researchers, for instance in the
MUSE project [Carmagnola et al., 2005], the KOD project [Sampson et al., 2002b], by IMS Learning
Design [IMS, 2007d] or the LMML specifications [Su¨ß and Freitag, 2002].

5.3. Inspection of current specifications 69
5.3 Inspection of current specifications
On the basis of the requirements manifested in the last section, existing standards and specifications are
examined on fulfilling or missing these requirements. Therefore, selected examples of standards and
specifications are inspected for four commonly-known areas. The first one is about describing learning
content on the level of assets. The second one deals with user profiling specifications in the field of e-
learning. The third one comprises specifications describing didactical aspects. Finally, the fourth one aims
at specifying adaptation rules.
5.3.1 Content packaging
Two prominent examples of describing assets and learning objects are the Dublin Core metadata element
set as well as the IEEE Learning Objects Metadata standards. As metadata standards for digital resources
were targeted years before e-learning specifications arose, both DC and LOM are already approved as stan-
dards (see [NISO, 2001] and [IEEE, 2002]). Therefore, these two standards are, after a short introduction,
evaluated on the basis of the requirements for learning content.
The Dublin Core metadata [DCMI, 2007] set can be seen as a standard for describing cross-domain
information resources, i.e. by means of a convention for metadata for digital objects. The standard consists
of elements on two levels: On the one side, the simple element set comprises attributes like title, creator,
subject, description, publisher, etc. On the other side, the qualified DC element set stands for an ongoing
development process to extend or refine element set.
Concerning the requirements for learning content from the last section, DC metadata would allow
the definition of different types of assets (A1) and learning objects (A2), using the type-field and custom
values. Further, determining between different levels of detail (A3) would also be realisable, extending
the element set or misusing a simple field, while the separation between content and presentation (A4)
could only be implemented using a relation between two objects described by DC. Contrary to this, this
mechanism (defining relations between objects) would enable the aggregation of learning objects (A5),
any kind of model, for example a domain, a context or a skill model (A6-A8) and even the mapping
between skills and learning objects (A9). Yet, describing skills would require other specifications, like
IMS Competency Definition [IMS, 2007f].
The Learning Objects Metadata comprises XML or RDF-based data model for the description of
learning objects. The standard itself arose from the Learning Resource Meta-data specification of the IMS
Global Learning Consortium (IMS LRM) and was influenced by the ARIADNE research project [Duval
et al., 2001]. [IEEE, 2002] summarises the nine categories of the LOM standard, namely the General,
Lifecycle, Meta-Metadata, Technical, Educational, Rights, Relations, Annotations and Classification cat-
egory. Nowadays, LOM is the state-of-the-art standard to be considered by learning objects repositories,
as shown for example by [EducaNext, 2007], [Singer, 2005] or [C2k, 2003]. Further, LOM is also part of
commonly-known sets of e-learning specifications, like SCORM.
As the LOM standard is very similar to Dublin Core Metadata – there is also a mapping of elements
between these two standards – the requirements A1 to A9 are fulfilled according to the DC standard.
Moreover, LOM elements are structured much better, with the nine categories mentioned above and it
does not have to be extended to fulfil a requirement (A3). Although LOM allows the mapping between
resource and skills, it misses, adequately for DC, a way to describe competencies. As a conclusion, LOM
provides nearly all necessary elements to allow adaptation of the level of instructions. For missing aspects,
other specifications like IMS Competency Definition can be applied.

5.4. A standard-based approach to adaptive e-learning 73
Instructional sequencing [d] allows the adaptation of a given path through the course, for example on
the basis of objectives or pedagogical states. In any of these cases, connections to a learner profile or
pre-defined competencies could allow an adaptive component to adapt the courseware on the basis of the
standards and specifications of the commonly-known SCORM. Overall, the suggestions mentioned here
would enhance the adaptability of courseware towards some requirements for learning content (A5-A9)
and all pedagogical (B1-B4) and didactical requirements (C1-C7), all introduced in section 5.2.
5.4.3 Enhancements in assets
Describing assets in SCORM is based on the IEEE LOM specification. Therefore, it is possible to use all
attributes of the nine categories of LOM. The educational elements are of particular interest for adaptation
aspects, as they include didactical and also pedagogical issues like the resource type, interactivity level,
difficulty level, the context and so forth. Further, it is also possible to describe relations between assets,
such as “is part of”, “is related to”, etc. Therefore, it is possible to define the format of an asset, as shown
by mark [a] in figure 5.3. On the other side, assets can also be modelled as a set of documents. In the case
of mark [b] it is also thinkable that the visual representation of an instruction can be adapted by combining
a XML-resource with different XSLT-files.
Figure 5.3: Enhancing SCORM’s asset specification
To fulfil the other requirements for learning content (A1-A4), an extension of assets is not necessary
at all. Yet, it would be required to utilise the possibilities of LOM, particularly for describing relations
between resources and exploit these content model. As LOM mainly focuses on didactical aspects, ped-
agogical issues can be realised with other mechanisms, like applying IMS Learning Design. Generally,
these two layers of the SCORM specifications already fulfil important requirements for standards to sup-
port adaptive e-learning.
5.4.4 Further suggestions for the AdeLE approach
As SCORM does not support user profiling so far, it is necessary to adopt an existing XML-based spec-
ification like IEEE PAPI or IMS LIP. According to the pedagogical requirements, the chosen specifica-
tions have to consider domain-specific information like domain knowledge, records of learning behaviour,
records of evaluation and assessment [Brusilovsky, 1994] plus domain-independent information like cog-
nitive attitudes, motivational states, background knowledge, multiple intelligences, learning styles, etc.
[Lane, 2000]. Furthermore, it is important that this specification is performs well to allow real-time user
tracking.

74 5. Towards Standardising Adaptable Courseware
Regarding requirements for learning content, a standard needs to support modelling of knowledge
domains and contexts for e-learning. Therefore, another specification like the IMS Competency Definition
[IMS, 2007f] has to be adopted. Furthermore, the specifications for learning content and sequencing ought
to be connected to the learner’s characteristics, knowledge domains and contexts. Again, SCORM does
not include these aspects yet, so a specification like IMS Learning Design is required.
One very special requirement of the AdeLE project, which is derived from the application of eye-
tracking technology, deals with defining learning objectives for elements within an instruction. This re-
quirement could have been realised by describing assets and aggregating them to instructions. As the
AdeLE prototype does not include an aggregation engine for instructions, an own tool was developed in
order to define inner-instructional objectives.
Figure 5.4: “Semantic TAGging Editor” for inner-instructional objectives
The so-called “Semantic TAGging Editor” (STAGE) which was mentioned firstly in [Garcia-Barrios,
2006] and is implemented as a Firefox plugin allows the selection of HTML-elements and the annotation
of them with states like “to read” or “to learn”. Figure 5.4 displays the STAGE tool in front of an opened
instruction, which was partially annotated with elements to be learned (red colour) and elements to be read

5.5. Conclusions 75
(yellow colour) by the learner. Contrary to all other adaptation information, these objectives are not stored
as metadata, but within the instruction itself. How it is utilised is shown in chapter 7, which describes the
AdeLE prototype in detail.
Concluding this section, two further issues have to be outlined here: On the one side, the SCORM
manifest file is based on XML, which enables that the extensions suggested in this section can be easily
realised by modifying the mark-up definition, for example by adding entries of the IMS Learning Design
or IMS LIP into SCORM’s manifest file. Further, XML is also advantageous for building up a data object
model (DOM) for real-time data processing. On the other side, the standard-based approach introduced
here also supports retrieval-based instructional creation, as shown with the idea of a Dynamic Background
Library in chapters 7 and 9.
5.5 Conclusions
Against the background that standardisation is a contradiction to personalisation this chapter pointed out
the necessity of utilising standards and specifications and, therefore, suggests a standard-based approach
towards adaptable courseware. After giving a short overview of the standardisation process in the field of
e-learning and outlining a few speculations on further development of such standards, this chapter proved
the usefulness of the FORMABLE model of the book’s theoretical part for the first time. In the context of
the rather practical approach to define requirements for e-learning specifications to support learner-centred
adaptivity, FORMABLE was applied to derive these requirements.
As a result, four categories of such requirements were identified. The first one focuses on the learning
content, suggesting the description of instructional content, the knowledge domain and learning contexts.
Secondly and thirdly, pedagogical and didactical issues have to be described in order to adapt the learning
process to these aspects. Fourthly, requirements concerning adaptation rules deal with adaptation methods,
such as adaptation of instructions, the instructional sequence or inserting new instructions. All in all,
specifications have to fulfil these requirements in order to support adaptive e-learning.
An inspection of current e-learning standards outlined that commonly-known sets of specifications,
like SCORM, do not fully support aspects of adapting the online learning process. Thus, the AdeLE
project team attempted an approach to utilise the specifications and standards of SCORM and consid-
ered missing functions with other specifications, for example IMS LIP or IMS Learning Design, and
workarounds, so that the final prototype implements as many features of the FORMABLE model as pos-
sible. In addition to adaptable courseware, the learning management system also plays an important role
if the learning process is to be adapted. The next chapter therefore deals with an ideal adaptive e-learning
environment.

76 5. Towards Standardising Adaptable Courseware

Chapter 6
An Ideal Environment for Adaptive E-
Learning
“ Idealism increases in direct proportion to one’s distance from the problem. ”
[ John Galsworthy ]
To describe an ideal adaptive e-learning environment, this chapter is structured as follows: First of all,
section 6.1 summarises commonly-known methods and techniques to adapt the online learning process.
Then, an ideal model of an adaptive e-learning environment is depicted on the basis of functional require-
ments (section 6.2) and an architectural design (section 6.3). In these three sections, the FORMABLE
model is utilised to deduce various facts. Concluding this chapter, existing projects and solutions are
inspected in section 6.4 with respect to the ideal adaptive e-learning environment.
6.1 Methods and techniques for adapting the learning process
According to [DeKoch, 2000, p. 19], an adaptation method is determined “by an adaptation idea defined
at conceptual level”, while a technique is defined “by a user model representation and an adaptation algo-
rithm”. Methods and techniques for adaptation in e-learning environments mainly deal with the elements
of the online learning process which can be adapted. Referring to FORMABLE, adaptation methods
can be divided into three categories: (1) the ones adapting the instructions itself, (2) the ones adapting
instructional sequences and (3) the ones adapting an online course by inserting new instructions.
6.1.1 Adaptation of instructions
This category of methods and techniques comprise the idea that an instruction itself or parts of it can
be adapted in some way. Therefore, it has to be distinguished between fragment and page variants. On
the other side, researchers differentiate between content adaptation and adaptive presentation. These two
dimensions serve as a basis for giving an overview of methods and techniques in the scope of adaptive
e-learning as well as for categorising them into this or into one of the next two subsections.
With respect to [Brusilovsky, 1996], content adaptation methods on an instructional level comprise
additional, prerequisite and comparative explanations, explanation variants as well as sorting, hiding and
annotating instructional fragments. Yet, it has to be stated that additional explanations have to be treated
only on a fragmental level to fit into this category. Further, adaptive presentation with methods like multi-
languages or layout variants can also be categories here. These methods can be of benefit for the learning
process if applied on the basis of didactical or pedagogical principles.
77

6.2. Functional requirements 79
by relevance) and link hiding (removing irrelevant links). The frame-based technique by [Henze, 2000,
p. 15] would also fit into this category, if treated at page level. Generally, these techniques can be realised
by re-ordering the instructional sequence, which has a further effect on the user interface, for example on
navigational elements.
Finally, it has to be mentioned that adapting the sequence of instructions is only possible for instruc-
tions not visited yet. Past instructions should not be re-ordered at all, so that the adaptation process fulfils
the requirement of scrutability and the learning history is reproducible for both the learner and the instruc-
tor. This fact is considered by FORMABLE by the pre-condition in the two methods referred to in this
subsection.
6.1.3 Adaptation through insertion of new instructions
The last category of adaptation methods and techniques deals with the idea that for different reasons new
instructions can be inserted in the course sequence. Possible reasons can be derived from didactics, for
example a learner requires another explanation, or from pedagogy, for instance instructional alternatives
are added in order to meet the principle of the dual coding theory.
The FORMABLE model fulfils this requirement with the two methods “InsertInstructionByDidactics”
and “InsertInstructionByPedagogy”. As these methods could also deal with repeating instructions, pre-
conditions about forbidding the insertion of past instructions is allowed. Contrary to re-ordering the
instructional sequence, this instruction is duplicated and not moved.
Methodologically only a few concepts can be identified for this category, all given for didactical rea-
sons. The method of additional explanation at page level was already excluded in the former subsection,
but fits for this one. Another approach comprises didactical strategies for repeating instructions, for exam-
ple because certain activities have to be experienced multiple times, or assessment identified knowledge
gaps.
From the viewpoint of techniques, the insertion of instructions can reach from an instructor’s providing
static links to the idea of retrieving all instructions from one or more learning object repositories, as seen
for example by the Knowledge Sea portal in [Brusilovsky, 2004b]. Another interesting approach in this
context is the idea of the Dynamic Background Library, described in detail in [Dietinger et al., 1999] and
[Mo¨dritscher et al., 2005] and evaluated in chapter 9 of this work. The technique used in this approach
can be positioned in the middle of static link-lists and courses consisting fully of retrieved instructional
content.
As a conclusion to this section, it can be stated that from the viewpoint of research on adaptive e-
learning it is obviously much more relevant how the system behaves, on the basis of which information
adaptation takes place and what the didactical result of the adaptation is. While adaptation methods are
slightly related to the theoretical models behind an adaptive e-learning environment, techniques are only
interesting, if they are evaluated for a certain didactical or pedagogical model, which often is not the case
as stated for example by [Park and Lee, 2004]. Thus, the FORMABLE model can be understood as a
theoretical framework for examining exactly these dependencies between techniques and the didactical
model within an adaptive e-learning environment.
6.2 Functional requirements
While aspects of adaptable courseware were already examined in the last chapter, this section deals with
functional requirements of e-learning systems in order to adapt online courses.

80 6. An Ideal Environment for Adaptive E-Learning
6.2.1 State-of-the-art e-learning features
From a traditional viewpoint of e-learning a lot of research work has been done already and, further,
various existing platforms document which features and user requirements are necessary to realise online
courses. For instance, [Dietinger, 2003, p. 41ff] groups functional requirements for e-learning systems into
three major groups: (1) learning management systems (LMS), (2) learning content management systems
(LCMS), (3) learning and tutoring support management (LTSM).
Learning content management comprises all functions to create and maintain courseware. Thus, these
requirements mainly deal with authoring tools, learning object repositories, e-learning standards and col-
laborative features to create content. As these aspects are not of relevance to adapting the learning process
and e-learning specifications and standards encapsulate the authoring process of learning content, learning
content management will not be treated closer here.
On the other side, LMS and LTSM primarily focus on the learning process, whereby learning man-
agement sums up all functions concerning the learner interacting with the course material and learning
and tutoring support is about communication and collaboration amongst learners and with the teacher.
Features of an ideal LMS already include particularly important aspects of adapting the learning process:
(A1) The learner portal stands for the user interface of an e-learning platform. Adaptability of the user
interface, i.e. the ability to customise the portal, can be considered to be state-of-the-art.
(A2) A registration module allows learners to access courses. Further, it handles billing issues and pro-
vides notifications, policies and information on the competencies to be achieved by the courses.
(A3) The learner profile component is aware of features to manage information about the learner. In
accordance with the pedagogical requirements of section 5.2, a learner profile comprises static in-
formation (the learner records), tracking the learning history, personal performance and skill gap
analysis and different reporting functions for teachers.
(A4) The course presentation engine delivers the instructions to the learner and, ideally, provides different
navigational elements for information visualisation purposes. The most commonly-known elements
are, beside suspending or exiting a course, a previous and a next button as well as a tree-view for
visualising the course hierarchy.
(A5) An assessment component allows the assessment of the learning process, for example by instruc-
tions like quizzes or tasks, form-based questionnaires or other activities.
(A6) Administrative features deal with typical tasks for course administrators or teachers, for instance
the course management, system configuration or determination of different roles.
Additional to LMS features, it is recommended to implement the following features of learning and
tutoring support management:
(A7) Communication tools allow learners to communicate with each other or with the teacher. Examples
of asynchronous features are emails, discussion groups, etc., while synchronous communication
could be realised with instant messaging or video conferencing [Rollett, 2003, p. 105ff].
(A8) The collaborative group of requirements sums up all learning activities which are interesting for
group work. For example, groupware tools like task lists, a calendar, shared workspaces, etc. are
mentioned here. Younger streams also comprise social software [Farzan and Brusilovsky, 2006] or
concepts of Web 2.0 [Rollet et al., 2007].

82 6. An Ideal Environment for Adaptive E-Learning
examined and applied as well, mostly under the term constructivistic or exploratory learning or in the field
of adaptive hypermedia [Brusilovsky 2001]. Exemplary solutions are the Knowledge Sea [Brusilovsky
2004] or the idea of the Dynamic Background Library [Dietinger et al., 1999] which is described closely
in chapter 9.
6.2.3 Non-functional requirements
Beside these functional requirements for adaptive e-learning environments, there are also other features
which do not directly deal with adapting the learning process, but can be derived from literature or, for
instance, from the field of adaptation systems. In the following, these requirements are briefly outlined:
(C1) From the viewpoint of adaptation systems, it is important that the adaptive behaviour can be under-
stood by the user. Therefore, concepts towards scrutability have to be implemented. For example,
an adaptive e-learning system could visualise or point out adaptation decisions to the user.
(C2) Another systemic attribute concerns controllability. As adaptivity might be provided by an own
specialised component, like a multi-purpose adaptive system indicated in section 2.4, a learning
management system has to provide an interface, which can be used by such an adaptive component
to adapt the learning process automatically. This interface comprises not only the possibility to
exchange data, but also some kind of API to control the behaviour of the LMS.
(C3) In order to evaluate the effects of adaptation, a logging mechanism and intelligent analysis methods
are required. Adaptation decisions and the actual state of the adaptation information have to be writ-
ten to log-files, so that researchers can evaluate the usefulness of the system’s adaptive behaviour.
In literature, some other requirements also relevant to adaptive e-learning systems can be observed. On
the one side, [Dietinger, 2003, p. 47ff] outlines various technical requirements like performance, security,
support of standards, usability, etc. On the other side, [Conlan, 2005, p. 97ff], amongst others, addresses
the idea of a distributed e-learning environment based on a service-based architecture. Such an approach
strengthens the idea that e-learning can be understood as tool repository for technology-based learning
and teaching [Mo¨dritscher et al., 2006a]. Thus, the following section presents an exemplary architecture
for an adaptive e-learning environment.
6.3 Architectural design
Tying up to the trend of encapsulating functions into components leads to an architecture for an adaptive
e-learning environment as shown in figure 6.1. This approach aims at developing such technological
entities providing a certain and well-defined functionality. Moreover, this architecture would also allow
the distribution of these components on different computer systems, for example for performance reasons.
In this concrete case a separation of a unique server-sided system and a client-sided part, which can exist
once per learner, is already indicated.

90 6. An Ideal Environment for Adaptive E-Learning
content and didactical-based adaptation, while younger approaches aim to adapt on the basis of pedagog-
ical states within a well-defined scope. As a conclusion, the influences on free and commercial solutions
can be identified by the fact that nearly all systems include content-based adaptation of the learning pro-
cess, while the application of standards is addressed primarily by commercial products and real adaptive
e-learning behaviour is implemented only by a very few companies.
Nevertheless, many prototypes resultant from research deal with adaptive e-learning, one of them is
the AdeLE system briefly introduced in the previous and described in detail in the following chapter.