Implementation and Evaluation of
Pedagogical Strategies in Adaptive
E-Learning Environments
Felix Mo¨dritscher

Implementation and Evaluation of Pedagogical Strategies in
Adaptive E-Learning Environments
Dissertation in fulfilment of the requirements for the academic degree
Doctor of Technical Sciences (Dr.techn.) in Computer Science
at the
Graz University of Technology
submitted by
Felix Mo¨dritscher
Institute for Information Systems and Computer Media (IICM),
Graz University of Technology
A-8010 Graz, Austria
May 2007
c© Copyright 2007 by Felix Mo¨dritscher
First reader: Univ.-Prof. Dr.Dr.h.c.mult. Hermann Maurer
Second reader: Univ.-Prof. Dr. Klaus Tochtermann
Advisor: Univ.-Ass. Dr. Christian Gu¨tl

Implementierung und Evaluierung pa¨dagogischer Strategien in
adaptiven Lernumgebungen
Dissertation zur Verleihung des akademischen Grades
Doktor der Technischen Wissenschaften
an der
Technischen Universita¨t Graz
vorgelegt von
Felix Mo¨dritscher
Institut fu¨r Informationssysteme und Computer Medien (IICM),
Technische Universita¨t Graz
A-8010 Graz
Mai 2007
c© Copyright 2007, Felix Mo¨dritscher
Diese Arbeit ist in englischer Sprache verfasst.
Erster Begutachter: Univ.-Prof. Dr.Dr.h.c.mult. Hermann Maurer
Zweiter Begutachter: Univ.-Prof. Dr. Klaus Tochtermann
Betreuer: Univ.-Ass. Dr. Christian Gu¨tl

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.

Kurzfassung
Der anhaltende Trend zur Wissensgesellschaft schu¨rt den Bedarf nach neuartigen Lehr- und Lernpa-
radigmen, was in weiterer Folge zu Entwicklungen wie dem Fernstudium oder e-Learning fu¨hrt. Aller-
dings unterliegen diese neuen Unterrichtsmethoden vielen Problemen, die durch Aspekte wie etwa dem
Vernachla¨ssigen von pa¨dagogischen Grundsa¨tzen, dem Fehlen des perso¨nlichen Kontakts zum Lehren-
den bzw. zu den Mitstudenten, Usability-Schwa¨chen der Lernplattform oder mangelhaften Lerninhalten
begru¨ndet werden. Als mo¨glichen Ausweg aus diesem Dilemma versuchen adaptive Lernsysteme, didak-
tische Kompetenzen – wie das Beobachten, Bewerten und Anpassen des Lernprozesses – technologisch
abzubilden, um einen effizienteren Wissenstransfer zu erreichen.
Die vorliegende Dissertation zielt darauf ab, theoretische und praktische Aspekte von adaptivem e-
Learning zu untersuchen und einen entsprechenden Prototyp fu¨r das Forschungsprojekt AdeLE zu ent-
wickeln. Konkret behandelt dieses Projekt, bei dem das Ku¨rzel AdeLE fu¨r “Adaptive e-Learning with
Eye-Tracking” steht, zwei innovative Ansa¨tze. Einerseits soll ein Eye-Tracking Gera¨t fu¨r eine erweiterte
Beobachtung der Lernenden eingesetzt werden, wobei der zu entwickelnde Prototyp jedoch nur die techni-
schen Rahmenbedingungen und nicht den Nutzen dieser Technologie fu¨r adaptives e-Learning adressieren
soll. Andererseits ist der Einsatz eines Werkzeugs mit dem Namen “Dynamische Hintergrundbibliothek”
zu beru¨cksichtigen, um den Lernprozess durch IR-basierte Instruktionsgewinnung zu adaptieren.
Im Theorieteil dieser Arbeit werden zuna¨chst die Bereiche der Adaptionssysteme bzw. des technologie-
basierten Lernens und Lehrens untersucht. Nach Zusammenfu¨hrung dieser zwei Fachrichtungen und einer
Literaturaufarbeitung der geschichtlichen Entwicklungen bzw. der Systemtypen von adaptiven e-Learning
wird eine formale Spezifikation, die das adaptive Verhalten in Lernumgebungen beschreibt, vorgestellt.
Dieses formale Modell dient im praktischen Teil der Dissertation zum Herleiten von Anforderungen an
standardisierte, adaptierbare Online Kurse sowie an eine idealtypische adaptive e-Learning Plattform. In
weiterer Folge wird die Entwicklung des AdeLE Systems in Anlehnung an diese Anforderungen beschrie-
ben. Schließlich werden die zentralen Ansa¨tze des AdeLE Projekts, also der Prototyp selbst, die Nutzbar-
keit der Dynamischen Hintergrundbibliothek fu¨r adaptives e-Learning sowie der Einfluss von didaktischer
Adaption des Lernprozesses auf den Wissenstransfer, evaluiert.

xii

Contents
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Objectives of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Methodology and structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Scientific contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
I. Theoretical Background 7
2 Adaptation Systems 9
2.1 Roots and related fields of systems theory . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Further developments in systems theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Towards adaptation and related concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 A generic approach to adaptation systems . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 Technology-Based Learning and Teaching 27
3.1 Relevant learning theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 E-pedagogy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3 E-didactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4 Towards formalising e-learning and e-teaching . . . . . . . . . . . . . . . . . . . . . . . . 40
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4 Adaptive E-Learning 47
4.1 Historical streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2 Types of adaptive educational systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3 Existing theoretical models for adaptive e-learning . . . . . . . . . . . . . . . . . . . . . 54
4.4 Formalising adaptive behaviour in e-learning systems . . . . . . . . . . . . . . . . . . . . 59
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
II. Practical Aspects 63
5 Towards Standardising Adaptable Courseware 65
5.1 Standardisation of learning content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.2 Requirements for standards to support adaptive e-learning . . . . . . . . . . . . . . . . . . 68
5.3 Inspection of current specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.4 A standard-based approach to adaptive e-learning . . . . . . . . . . . . . . . . . . . . . . 73
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
xv

6 An Ideal Environment for Adaptive E-Learning 79
6.1 Methods and techniques for adapting the learning process . . . . . . . . . . . . . . . . . . 79
6.2 Functional requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.3 Architectural design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.4 Inspecting existing projects and solutions . . . . . . . . . . . . . . . . . . . . . . . . . . 87
6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
7 Technical Realisation of the AdeLE System 93
7.1 Planning of the AdeLE prototype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
7.2 Functional units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7.3 Implementation details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
7.4 A walk through the AdeLE system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
III. Proof of Concept 121
8 Adaptation of the Learning Process within the AdeLE Prototype 123
8.1 Planning stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
8.2 Experiences gained . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
8.3 Other results from literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
8.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
9 Utilising a Dynamic Background Library for Adaptive E-Learning 133
9.1 Basic concept and realisation of EHELP . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
9.2 Evaluating the EHELP system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
9.3 The Dynamic Background Library for the AdeLE prototype . . . . . . . . . . . . . . . . 139
9.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
10 The Impact of a Didactical Strategy on Learning 143
10.1 Realisation of the courses regarding the learning theories . . . . . . . . . . . . . . . . . . 143
10.2 Comparison of the three e-learning strategies . . . . . . . . . . . . . . . . . . . . . . . . 145
10.3 Findings on didactical and pedagogical aspects . . . . . . . . . . . . . . . . . . . . . . . 150
10.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
11 Conclusions and Outlook 157
11.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
11.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
11.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
Bibliography 161
Index 181
xvi

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 . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 Spectrum of adaptation in computer systems, adopted from [Oppermann, 1994] . . . . . . 16
2.3 Generic framework for an adaptation system . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4 Formal specification of a multi-purpose adaptive system (types and variables) . . . . . . . 22
2.5 Formal specification of a multi-purpose adaptive system (operations and thread) . . . . . 23
3.1 Overview of research issues related to the learning process . . . . . . . . . . . . . . . . . 41
3.2 Formal specification of the content model . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.3 Formal specification of the pedagogical model . . . . . . . . . . . . . . . . . . . . . . . 43
3.4 Formal specification of the didactical model (types and instance variables) . . . . . . . . 44
3.5 Formal specification of the didactical model (operations) . . . . . . . . . . . . . . . . . . 45
4.1 Model of adaptive instruction, adopted from [Park et al., 1987] . . . . . . . . . . . . . . . 55
4.2 Framework for adaptive e-learning, adopted from [Shute and Towle, 2003] . . . . . . . . 56
4.3 The KnowledgeTree architecture, adopted from [Brusilovsky, 2004b] . . . . . . . . . . . 57
4.4 Formal specification of the adaptation model . . . . . . . . . . . . . . . . . . . . . . . . 61
5.1 The development process of e-learning standards, adopted from [Gries, 2003] . . . . . . . 67
5.2 Enhancing SCORM’s structuring and content packaging specification . . . . . . . . . . . 74
5.3 Enhancing SCORM’s asset specification . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.4 “Semantic TAGging Editor” for inner-instructional objectives . . . . . . . . . . . . . . . 76
6.1 Architectural design of an adaptive e-learning environment . . . . . . . . . . . . . . . . . 85
7.1 Utilisation of the Tobii 1750 Eye-Tracking system . . . . . . . . . . . . . . . . . . . . . 95
7.2 Overview of AdeLE’s architectural design . . . . . . . . . . . . . . . . . . . . . . . . . . 96
7.3 Implementation details of the Adaptive System . . . . . . . . . . . . . . . . . . . . . . . 98
7.4 Implementation details of the Modelling System, adapted from [Fro¨schl, 2005, p. 118] . . 100
7.5 Graphical user interface of the Modelling System [Fro¨schl, 2005, p. 154] . . . . . . . . . 101
7.6 Implementation details of the Concept-Based Context Modeller, adapted from [Safran,
2006, p. 84] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
7.7 Graphical user interface of the Concept-Based Context Modeller [Safran, 2006, p. 106] . . 103
7.8 Top of the Openwings Explorer displaying installed components . . . . . . . . . . . . . . 104
xvii

7.9 Bottom of the Openwings Explorer displaying interfaces and services . . . . . . . . . . . 105
7.10 Sequence diagram for the scenario “learner navigates instruction” . . . . . . . . . . . . . 107
7.11 AdeLE system login dialog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
7.12 Registration form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
7.13 AdeLE prototype main menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.14 Dialog for course enrolment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.15 Overview of the learning progress for an example course . . . . . . . . . . . . . . . . . . 112
7.16 Form-based dialog to edit the user profile . . . . . . . . . . . . . . . . . . . . . . . . . . 112
7.17 Learner’s view of a course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.18 Example for an examination including an assignment task . . . . . . . . . . . . . . . . . 114
7.19 Tree-view navigation of the AdeLE system . . . . . . . . . . . . . . . . . . . . . . . . . 114
7.20 View of the navigation area with hidden elements . . . . . . . . . . . . . . . . . . . . . . 115
7.21 “Background Knowledge” section for an exemplary instruction . . . . . . . . . . . . . . . 115
7.22 “Why this way?” section for an example learner . . . . . . . . . . . . . . . . . . . . . . . 116
7.23 Form-based eye-tracking simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
7.24 Menu with additional functions for teachers . . . . . . . . . . . . . . . . . . . . . . . . . 117
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) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
9.1 Basic functionality scheme of a DBL [Garcia-Barrios et al., 2002] . . . . . . . . . . . . . 135
9.2 EHELP viewing mode “embedded hyperlinks” [Garcia-Barrios et al., 2002] . . . . . . . . 136
9.3 Background knowledge data structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
10.1 Distribution of teacher’s (blue), students’ (pink) and overall (yellow) activities course A . 147
10.2 Distribution of teacher’s (blue), students’ (pink) and overall (yellow) activities course B . 148
10.3 Distribution of teacher’s (blue), students’ (pink) and overall (yellow) activities course C . 149
10.4 Comparison of the students’ activities for the courses A (green), B (yellow) and C (blue) . 154
xviii

List of Tables
3.1 Bloom taxonomy [Bloom, 1956], adapted and extended for skills and attitudes . . . . . . 37
8.1 Characteristics of the students’ learning behaviour for each initial WAVI-group . . . . . . 127
8.2 Characteristics of the students’ learning behaviour for each pass . . . . . . . . . . . . . . 128
10.1 Statistics of the course’s educational objectives . . . . . . . . . . . . . . . . . . . . . . . 144
10.2 Characteristics of the three courses for the preparation stage . . . . . . . . . . . . . . . . 146
10.3 Characteristics of the three courses for the implementation stage . . . . . . . . . . . . . . 146
10.4 Characteristics of the three courses for the concluding stage . . . . . . . . . . . . . . . . 150
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) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
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”) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
10.7 Characteristics of the three courses based on the students’ ongoing documentation about
learning and raw database queries within the Moodle system . . . . . . . . . . . . . . . . 154
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”) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
xix

Chapter 1
Introduction
“ A master can tell you what he expects of you.
A teacher, though, awakens your own expectations. ”
[ Patricia Neal ]
Experienced teachers tend to possess the ability to awake the learners’ interests and keep them mo-
tivated in dealing with the content of a course. In addressing recent streams of educational trends, such
as distance learning, these approaches lack such pedagogical competencies in the learning phase and,
therefore, often fail in practice.
1.1 Motivation
The shift from a production-centred to a knowledge-centred society, as outlined for example by [Probst
et al., 2000, p. 1], had a deep impact on education in general and cognitive science in particular. As knowl-
edge turns into a valuable asset for both companies and humans [Maurer, 1998], research and development
focus on new educational methods. Technology-based learning and teaching can mainly be identified as
one of the emerging areas, as shown by means of concrete numbers in [Brennan, 2003] or [Hasebrook
and Maurer, 2004, p. 7ff]. Furthermore, [Dietinger, 2003, p. 21f] points out several application scenarios
which manifest the necessity of these new approaches of teaching and learning for educational institutions
as well as for companies.
Nevertheless, e-learning initiatives often fail due to problematic aspects, like high costs or 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. To overcome such prob-
lems, ideas and solutions are being developed from two directions: On the one hand, cognitive psychol-
ogy is heading towards new pedagogical approaches, like shifting to constructivistic learning [Lennon
and Maurer, 2003], promoting collaboration and communication [Hooper and Hannafin, 1991], Web 2.0
approaches [Kolbitsch andMaurer, 2006] and so forth. On the other hand, technologists are bringing up in-
novative concepts and methods, such as game-based learning [Prensky, 2001], virtual campus approaches
like ViKar [Kuhn and Gudjonsdottir, 1999] or automatic adaptation of the online learning process.
Against this background, many scientific and commercial activities address this research and devel-
opment stream called “adaptive e-learning” [Shute and Towle, 2003]. [Brusilovsky, 2004a] states that
adaptive educational hypermedia (as one part of adaptive e-learning) is relatively new and started around
1990. Nevertheless, as a result of inconsistent definitions of terms as well as missing links between techni-
cal approaches and pedagogical basics, the history of commonly-known principles and technical solutions
1

4 1. Introduction
most 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 dissertation, 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 dissertation
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 proof-of-concept for adaptive
e-learning, the dissertation is summed up and an outlook for further work is presented.

I. Theoretical Background
7

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.
9

10 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 11
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 13
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”,

14 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 dissertation. Following systems methodology, the formal
specification 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 15
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.

18 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 19
• 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 dissertation, 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].

20 2. Adaptation Systems
To characterise user-centred adaptation, [Garcia-Barrios et al., 2005] identified the following five di-
mensions of personalisation:
• Explicit vs. implicit personalisation: Explicit personalisation describes the adaptation according
to a concrete user model. For instance, a system could provide tailored information by using a
specific user profile. Implicit personalisation is about adaptation resulting from a certain context
(situation or environment) and without utilising user information explicitly, like adapting the user
interface according to the output device just used by the user for example.
• Perceivable vs. hidden personalisation: Personalisation is called perceivable, if a user recognises
the results of personalisation. For instance, a system may show or hide control elements, such as
a tree-view visualising the structure of the information space according to user-specific cognitive
traits. Hidden personalisation does not affect the user interface or the presented content at all.
Taking the following example: If it is intended to update a user model according to some adaptation
rule, the user may not recognise the result of this personalisation step.
• Predictive vs. deterministic personalisation: Predictive personalisation comprises adaptation
steps executed in advance. For instance, rearranging the sequence of information chunks to be pre-
sented next is not only hidden, but also predictive. Deterministic personalisation takes place within
one adaptation step, if chosen information chunks are aggregated to one page which is immediately
displayed for example.
• Controlled vs. uncontrolled personalisation: Controlled personalisation enables and enhances
the system’s scrutability and, further, describes the idea that the user may take control of adaptation
processes at any time. Considering the simple case that a system cannot decide the adaptation step,
the user could be asked to determine the adaptation, by providing a set of suggestions for example.
Uncontrolled personalisation would not allow the user to influence the adaptation process.
• Individual vs. stereotyped personalisation: Individual personalisation comprises personalisation
towards one specific person, while stereotyped personalisation deals with personalisation towards
groups or anonymous users.
Considering these dimensions of personalisation might be useful for planning and realising person-
alisation features. Overall, this section examined the basics of adaptation systems and defined relevant
concepts and terms as consequently used in this dissertation. Furthermore, a generic framework for adap-
tation system being based on these fundamentals is introduced in the following section.
2.4 A generic approach to adaptation systems
As a conclusion from the theoretical part about adaptation systems, the idea of a multi-purpose adaptive
component and the feasibility of its realisation are discussed in this section. In addition, it is explained how
various concepts of systems theory and adaptation systems are considered within this approach. Finally,
technological aspects and open issues of the multi-purpose adaptive component are addressed before this
chapter is concluded.
2.4.1 A framework for an adaptation system
Combining the theoretical concepts of the last section into one piece may lead to a framework as shown
in figure 2.3. In general, an adaptation system can be understood as system providing the possibility that
some internal states can be modified in one of the following two ways: On the one side, a user could

2.5. Conclusions 25
When applying the framework for adaptation systems, in practice the following types of systems can
be evaluated and characterised with the approach in this section:
• On the lowest level of adaptation, systems without any feature of adaptivity or adaptability can be
described. In that case, the formal model would be reduced to the one of a generic system given
in figure 2.1. While the actions “observe” and “adapt” as well as adaptation rules and adaptors are
redundant, assessment of real-world states might be necessary. A typical example of such a system
would be a user profiler which only tracks information about the user without deriving or providing
any models [Gu¨tl and Garcia-Barrios, 2005b].
• On the next evolutionary step the formal approach is applicable for all systems providing some
adaptability. Thus, the assessment of environmental and user states as well as the adaptation is
triggered by the user. Generally, all systems providing some kind of user settings – for example for
customisation reasons – can be assigned to this systemic type.
• Further, adaptation systems with simple and static adaptation rules have to be mentioned here. For
instance, a system enabling such behaviour might provide automatic adaptation of a visual element
according to the brightness of the environment’s backlight.
• The main group of systems of interest to the formal model of this section comprises so-called single-
purpose solutions, which is a valid term for many adaptation systems. Examples are, amongst a large
set of existing systems, A-MEDIAS (an adaptive event notification service [Hinze, 2003, p. 129ff]),
solutions in the scope of adaptive hypermedia [Brusilovsky, 1996] and various system types in the
field of adaptive e-learning as shown in the upcoming chapters of this work.
• As already mentioned in this subsection, AI-methodologies can be described with the formal model
of a multi-purpose adaptive system, whether by masquerading the AI-method with adaptation rules
or by defining meta-adaptive behaviour.
• Finally, aspects of user-centred adaptation have to be outlined here. On the one side, customisation
can be described as realised on the basis of systemic adaptability, i.e. by allowing users to customise
a system according to their preferences. On the other side, automatic adaptation towards a user leads
to the commonly-known application area of personalisation and recommendation systems, as often
dealt with in fields like knowledge management or technology-based learning (see also [Hicks and
Tochtermann, 2001] or [Pivec and Baumann, 2004]).
Overall, the generic approach to adaptation systems in this section is very suitable to evaluate or plan
any kind of adaptable or adaptive feature within a system. As a consequence, this theoretical framework
is going to be applied in the field of adaptive e-learning in chapter 4.
2.5 Conclusions
Adaptation systems are, as outlined in this section, strongly related to and a part of systems theory. In
the context of sciences like philosophy, psychology, biology and so forth, researchers started to deal with
aspects of systemic organisation, behaviour and characteristics more than 100 years ago, which resulted
in different areas, such as hard-systems science, cybernetics, systems thinking, human systems or systems
design. Additionally, philosophical and practical issues arose in the scope of systems theory.
Against this background, aspects of adaptation systems can be identified in all of these research fields,
beginning with systemic characteristics like complexity, self-organisation, openness, observability, con-
trollability, etc. over philosophical aspects such as internal models of the real-world up to systems method-
ology for realising such systems. In accordance with these findings and based on a formal model of a

28 3. Technology-Based Learning and Teaching
2003] suggests instructors consider principles of learning by means of historically grown learning theo-
ries. Thus, it is possible to reuse certain procedures, for instance pre-defined instructional components as
stated in [Merrill, 2001]. Within the e-learning situation, three learning theories – the Behaviourism, the
Cognitivism and the Constructivism – are of importance as shown in [Cooper, 1993], [Dietinger, 2003,
p. 30ff], etc. In the following, these three theories are described in short and implications for realising
online courses are derived.
3.1.1 Behaviourism
The behaviourist school of thought influenced by researchers like Pavlov, Thorndike, Watson and Skinner,
who outlines that “learning is a change in observable behaviour caused by external stimuli in environ-
ment” [Skinner, 1974, p. 2]. Behaviourists see “the mind as a black box, in the sense that a response
to a stimulus can be observed quantitatively, totally ignoring the effect of thought processes occurring
in mind”. [Atkins, 1993] highlights four aspects relevant to realising online courses with respect to the
behaviourist school:
• The learning material should be divided into smaller instructional steps being delivered in an in-
tuitive way by starting with a theoretical entity (a definition, category, rule, formula or principle),
giving positive examples to reinforce mastery if the subject and showing negative examples to es-
tablish conceptual boundaries.
• Course designers have to define sequences of instructions using conditional or unconditional
branching to other instructional units and pre-determining choices within the course. In general,
activities are sequenced by means of increasing difficulty or complexity. Further, a learner should
control the sequence and pacing through the materials.
• To maximise learning efficiency, learners should be routed to leave out or repeat instructions based
on their performance, which can be assessed by diagnostic tests within the sequence of learning
activities. Nevertheless, the instructional designer may also allow a learner to choose the next
instruction out of a set of activities, giving the learner more control over the learning process and,
thus, shifting learning to the paradigm of Cognitivism.
• The behaviouristic paradigm of learning suggests to demonstrate the required operation, procedure
or skill and to break this instruction down into small parts, enriched with appropriate explanation.
Thus, learners are expected to master the desired behaviour and acquire knowledge as well as skills
from frequent review or revision and by applying check tests or repeating practice with feedback.
Instructional design focus on a low error rate and the usage of loops back through material if neces-
sary. Furthermore, reinforcement messages should be used to maintain motivation.
Overall, behaviourists recommend a structured, intuitive approach to designing an online course, so
that basic concepts, skills and factual information can rapidly be acquired by the learners. Further im-
plications regarding online learning can be summarised by the concept of drill and practice, portioning
materials and assessing learner’s achievement levels and giving external feedback. However, the appli-
cation approaches considering behavioural design is unproven or ineffective for higher-order learning
activities or for meta-cognition.
3.1.2 Cognitivism
Cognitivists consider learning as “an internal process that involves memory, thinking, reflection, abstrac-
tion, motivation, and meta-cognition”, as outlined by [Ally, 2004]. Cognitive psychology comprises the

30 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.

3.2. E-pedagogy 31
• Instructors should focus on interactive learning activities to promote social presence and higher-
level learning and to help develop personal meaning. As learning focuses on developing new knowl-
edge, skills and attitudes, e-learning faces the problem that psychomotor, affective and higher-level
objectives are hard to reach within online learning phases. Therefore, [Mo¨dritscher and Sindler,
2005] suggest providing other ways – such as social or interactive activities, context-based learning,
assessment through open-ended questions, etc. – to realise such didactical aspects.
Examples of constructivist learning can be found within the scope of experiential learning, self-
directed learning, context-aware learning and reflective practice. Despite a variety of advantages to Con-
structivism, like the presentation of content from multiple perspectives, the active knowledge construction,
the development of meta-cognitive strategies, this learning theory also faces a few disadvantages, such as
problems in adequately evaluating the learning process, lack of instructional resources to respond to the
multitude of student interests or higher effort to create context-based learning content, restrictions on driv-
ing the learning process to a certain direction given for example by science, higher drop-out rate due to a
lack of extrinsic motivation for students with low capabilities for self-directed learning, etc.
These three commonly-known learning theories are of central relevance for examining pedagogical
issues and the implementation of different e-learning strategies, as shown in the following two sections.
3.2 E-pedagogy
Referring to [Knowles et al., 1998, p. 10], education can be understood as “activity undertaken or initiated
by one or more agents that is designed to effect changes in the knowledge, skill, and attitudes of individ-
uals, groups, or communities”. On the contrary, the term “learning” emphasises the person in whom the
change occurs or is expected to occur. Thus, learning comprises “the act or process by which behavioural
change, knowledge, skills, and attitudes are acquired” [Boyd and Apps, 1980, p. 100f]. Tying up to this
definition, the following subsections deal with the psychological and learner-centred aspects of the tra-
ditional learning process – such as relevant factors for learning, characteristics of learners and further
influences on learning – and examine them within the context of e-learning.
3.2.1 Factors relevant to the learning process
Drawing conclusions from [Bransford et al., 2000, p. 51ff], four factors can be outlined as significantly
important for the learning process: (1) attention, (2) motivation, (3) emotions and (4) experiences of the
learner.
First of all, the focus of attention determines if a student mentally follows a lecture and, therefore,
if the intended behavioural change affects a learner at all. E-learning particularly requires a strategy for
getting and keeping the learner’s attention. Thus, it is necessary to consider cognitive processes such as the
learner’s selection of incoming data into the sensory memory, organising and integrating this information
by building connections in short-term memory and encoding it by transferring it to long-term memory.
Thus, it is recommended to apply certain principles for instructional design, for example the ones by
[Fleming and Levie, 1993].
Secondly, the motivational states of students are of importance when questioning how the stimuli
given by the teacher promotes the learning process. [Bransford et al., 2000, p. 60] state that “motivation
affects the amount of time that people are willing to devote to learning”. Yet, this willingness to learn is
caused by different motives, beginning with the intention of achieving something while competing against
colleagues, or helping other people, up to emotional factors like anxiety. [Entwistle, 1981] classified
three motivational orientation styles: (a) meaning-oriented, (b) reproducing-oriented and (b) achieving-
oriented motives. Considering motivational aspects for e-learning is mainly dependent on the learning

3.2. E-pedagogy 33
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 35
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 dissertation 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

3.3. E-didactics 39
can be seen as an enabler for these types of competencies, because information can be enriched with
multimedia assets [Gunawardena and McIsaac, 2004], practicing skills can be supported by using
interactive elements or tutoring systems and the behaviour of a student can be observed within the
context of the e-learning system in terms of the micro-adaptive approach for e-learning [Park and
Lee, 2004]. In fact, it is easier to mediate knowledge through e-learning environments, while the
effort for teaching skills or attitudes is much higher as shown in the case study in the next section.
• Within an e-learning system, objectives need to be defined regarding the target group. With respect
to the standardisation process in the field of e-learning, specifications such as SCORM already
allow description of objectives as meta-information for the course [ADL, 2004]. Nevertheless, an
objective specified with SCORM can be seen as a state within the system and does not tell anything
about the level of the learning objective. Furthermore, it is hardly possible to reach high-level
learning objectives for all three types of competencies within a pure e-learning situation as stated
later in the study.
• Learning objectives which are defined by a teacher always have to be evaluated in some way – to
grade the students and to improve the quality of the course for future sessions. Considering the
possibilities of e-learning, it is well documented that knowledge acquisition can be assessed by us-
ing limited-choice questions like quizzes or multiple-choice questions. Nevertheless, for most areas
and, in particular, to reach high-level learning objectives it is necessary to examine students by ask-
ing open-ended questions, as reasoned for example by [Scouller, 1998]. Furthermore, the answers
to such questions have to be interpreted and evaluated by experts. Researchers try to imitate such
expertises using artificial intelligence methods within intelligent tutoring systems, but the results are
still rather limited [Park and Lee, 2004]. In terms of skills, the learning results cannot be measured
by technology-based methods without hard efforts.
It has to be outlined that the assessment of high-level objectives can be realised in many different ways.
With respect to the assessment methods focusing on didactical aspects such as defining competencies and
evaluating the learning process according to the determined learning objectives, the following possibilities
for implementing assessment in the e-learning situation can be found in the literature:
• First of all, most e-learning systems offer the possibilities to create and provide limited-choice
questions. Although quizzes can save a lot of time to grade a large amount of students and [Scouller,
1998] reports on good results for low-level objectives of the cognitive domain, they show a worse
performance for the employment of deeper learning strategies and higher levels of cognitive pro-
cessing.
• Therefore, [Scouller, 1998] states that it is necessary to implement open-ended questions within
the e-learning situation, for instance by tasks like writing essays or submitting some sort of project
work. It is obvious that the evaluation of such tasks is extremely time-consuming for a teacher.
Therefore, it is recommended to apply supporting methods such as automated grading, for example
using the Markit c© system introduced in [Williams and Dreher, 2004].
• As an extension of automated essay grading, intelligent tutoring systems may provide some kind
of expertise within a domain and allow fully automated teaching and assessment, as stated in [Park
and Lee, 2004]. Yet, this kind of system is hard to realise, often restricted to a certain domain and,
thus, to a few learning objectives. An example of a rather complex system in this area is INCENSE
providing different scenarios for teaching of the software engineering process [Akhras and Self,
2000].

3.4. Towards formalising e-learning and e-teaching 43
ples of a content model would be a conceptual space of the given knowledge artefacts or also dependencies
between artefacts by means of a recommended chronological sequence to go through them.
3.4.3 Pedagogical model
The pedagogical model deals with factors influencing the learning process as mentioned in section 3.2.
The specification shown in figure 3.3 allows defining user and environmental states that are fully dependent
on a learner, a concept and a situation as well as any combinations of these three factors. Thus, it is
possible to determine states completely independent of the learner (domain or context-related states) or
fully dependent of the learner (characteristics and user states).
Figure 3.3: Formal specification of the pedagogical model
The mapping in the pedagogical model can be generated and modified in the following way: On
the one side, environmental and learner-relevant factors have to be determined a priori with respect to
the needs of an e-learning system. On the other side, the mappings might be adapted later on. The
pedagogical model can be used to characterise or adapt the learning process, for example by applying
appropriate sensors, measuring certain states (using the operation “AssessState”) and reacting somehow
to modified states. Considering all relevant factors of the learning process, pedagogy can be seen as the
union of environmental and learner-specific states.
To give an example of a pedagogical aspect, environmental properties might be defined by a mapping
from the triple “any student”, “any concept” and “a specific property of the surrounding” (e.g. the level
of the background noise) to a certain level (e.g. given in dB). Prior knowledge of a student in a certain
domain can be seen as mapping of the triple consisting of “a student”, “a certain concept” and “the specific
situation” (e.g. “Student X can explain concept Y”) to the state “mastered” or “not mastered”. Intellectual
abilities of a learner might be given as mapping of the student to the results of an IQ test.
Although pedagogy should consider all these aspects, the assessment of pedagogical states as well as
the learning platform’s possibilities are part of the next model, the didactical model.

44 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.

4.1. Historical streams 49
research is on-going. [Carrier and Jonassen, 1988] proposes an eight-step model to provide practical guid-
ance for applying the ATI model to the design of courseware. According to this model, the course designer
has to identify objectives for the courseware, specify the tasks, define the relevant learner characteristics
with regard to the target group, determine how to adapt the instructions and design alternative treatments.
As important learner characteristics influences the learning process, several methods like remedial, capi-
talisation, preferential, compensatory and challenge for instructional adaptation are recommended.
[Park and Lee, 2004] states that this model seems to be the most practice-oriented within the ATI
research, the other ATI approaches are considered to be very theoretical, problematic or time-consuming.
Finally, it is also important to mention that an ATI-based system may not produce results differing from
non-adaptive instructional systems without coherent and traceable rules to link the different learner and
learning variables to different tasks and instructional strategies.
4.1.3 Micro-adaptive approach
The third main approach to adaptive instructional learning is about adapting instructions on a micro-
level by diagnosing the student’s specific learning needs during instruction and providing instructional
prescriptions for these needs. While the benefit of ATI research is either poor or not proven, several
studies have shown that aptitude constructs are relevant to instructional and learning strategies.
Therefore, researchers developed micro-adaptive instructional models which use on-task instead of
pre-task measures. [Federico, 1983] states that monitoring the learner’s performance and behaviour, for
example by means of response latencies, response errors, emotional or motivational states, etc., can be
exploited for manipulating and optimising instructional treatments and sequences on a much more refined
scale. The oldest model for the micro-adaptive approach is the idea of programmed instruction, originally
implemented within a mechanical assessment device by [Pressey, 1926].
Adaptive e-learning in terms of the micro-adaptive approach is comparable to one-on-one tutoring
and has to be separated in two main processes: The first part can be characterised as a diagnostic process
assessing learner characteristics, such as aptitudes or prior knowledge and indices of the task, for instance
difficulty level, content structure or conceptual attributes [Rothen and Tennyson, 1978]. Referring to the
principles of adaptation systems in section 2.3, this observation process comprises the concept of assessing
and observing the adaptation information.
Based on the assessment of such on-task factors and states, the second part of micro-adaptive instruc-
tion can be described as a prescriptive process optimising the interaction between the learner and the
task by automatically adapting the composition and sequencing of instructions according to the students’
aptitudes and recent performance. Thus, it is necessary to define a strategy for selecting the appropriate
amount of instruction and time to achieve a given learning objective. Examining this process within the
scope of adaptation systems, the prescriptive rules would comprise the so-called adaptation procedures,
while the systematically adaptation would be initiated on adaptation rules triggered by certain states in the
adaptation information.
From the technological viewpoint, a number of micro-adaptive instructional models have been de-
veloped. These models differ from programmed instruction in the way that they implement a particular
model or theory of learning. Such a micro-adaptive model uses the time-dependent states of learner abil-
ities and characteristics, especially the dynamically changing ones. As outlined by [Suppes et al., 1976],
micro-adaptive instructional models often utilise a quantitative representation in order to determine and
adjust learning contents during instruction in an accurate way. With respect to existing models, such as
the mathematical model, the trajectory model, the Bayesian model, the algorithmic approach and so forth,
micro-adaptive instructional learning mainly deals with adapting few instructional variables, for example
the amount of content to be presented or the presentation sequence of the content.
Another aspect of micro-adaptive instructional learning is response sensitivity, where computer-based

4.3. Existing theoretical models for adaptive e-learning 55
4.3.1 Informal frameworks
The first category deals with informal models for adaptive e-learning. Amongst them, [Park et al., 1987]
describe a powerful theoretical framework considering aspects of micro and macro-adaptive instruction on
the basis of learner characteristics. As shown in figure 4.1, this conceptual model presents necessary func-
tional entities for the adaptation process in the field of learning. Macro-adaptive instructional behaviour is
implemented by the outer feedback cycle between input and output, while micro-adaptation of the learn-
ing process is realised within transactions, based on diagnostic and tutorial rules to adapt the knowledge
and instructional presentation. Referring to adaptation systems, this approach can even be characterised
to be meta-adaptive, because the refinement of tutorial rules is considered.
Figure 4.1: Model of adaptive instruction, adopted from [Park et al., 1987]

56 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
Inspecting both these models, it can be stated that these approaches focus mainly on the sequencing of
learning content. [Lin et al., 2002] even compare the learning process to workflows of learning activities
and, therefore, utilise workflow management technologies. Nevertheless, these models lack aspects like
adapting the user interface, aggregating or visualising instructions in different ways or adding instructional
units on demand.
4.3.3 Formal and logic-based models
The application of formal approaches is common in other areas, for example in the field of adaptive
systems as shown by [DeJong, 1975, p. 5ff]. In the field of adaptive e-learning, a few researchers also
tried to describe the systemic behaviour of e-learning environments providing adaptivity and adaptability
in some way. Two of them are highlighted in this subsection.
The Adaptive Hypermedia Application Model (AHAM) by [DeBra et al., 1999b] is based on the
Dexter model and, therefore, focuses on hypertext and hypermedia systems. AHAM allows specifying
relevant didactical issues as well as pedagogical rules. Yet, this formal model restricts the possibilities of
teachers, for example by merely addressing the cognitive domain (read, learned) or not separating between
learning materials and learning activities. On the other side, pedagogical aspects are linked to attributes of
the user-model, so that it is not possible to define pedagogical rules depending on the context or domain
of a course. Finally, AHAM deals with architectural issues to a great extent, which is not a requirement
for designing and evaluating adaptive behaviour within an e-learning environment.
Another very powerful formal approach to characterisation of adaptive educational hypermedia is
the one by [Henze and Nejdl, 2004]. Contrary to other models, this one comprises a first-order logic
rather than some theoretical framework for a component-based architecture or for a certain adaptation
method. Thus, it is possible to evaluate and compare adaptive e-learning environments with respect to
their behaviour instead of their architecture or features. In detail, the logic-based model by Henze and
Nejdl are based on the definition of a document space, a user model, observations and an adaptation
component. Further, different predicates – like “part-of”, “prerequisite”, “is-a”, “is-dependent”, “has-
property”, etc. – are used to build up the first three sub-models. Subsequently, the adaptation component
consists of rules describing how adaptation is performed within a system.
In accordance with section 2.4, this first-order logic can be seen as the most powerful way to describe
adaptive behaviour of systems. Yet, in the field of adaptive e-learning practitioners might be lost having
only guidance through some exemplary systems in [Henze and Nejdl, 2004]. Although it is possible to
describe all pedagogical and didactical issues – as given in the previous chapter for example – it would
be helpful to have a conceptual framework for adaptive e-learning, which is attempted by section 3.4
(content, pedagogical and didactical model) and the next section (adaptation model).
4.3.4 Other theoretical frameworks
Another framework realised with UML is the so-called Munich Reference Model introduced by [DeKoch,
2000, p. 73ff]. Again, this model comprises adaptive hypermedia applications based on the Dexter Hy-
pertext Reference Model [Halasz and Schwartz, 1994] from a more component-based viewpoint. There-
fore, its applicability for designing and evaluating adaptive e-learning environments from the behavioural
viewpoint is rather restricted, and it can be considered to be the theoretical background to the SmexWeb
[DeKoch, 2000, p. 287ff].
Furthermore, [Tochtermann and Dittrich, 1996] introduces the Dortmund Family of Hypermedia Mod-
els (DFHM), another formal approach to adaptive hypermedia which is even based on VDM-SL. Neverthe-
less, this model deals rather with an object-oriented, component-based architecture than with a systemic
behaviour and, further, addresses primarily hypermedia, not educational hypermedia.

4.4. Formalising adaptive behaviour in e-learning systems 61
with the one in this section are, for reference reasons, subsumed under the name FORMABLE, which is
the abbreviation for “FORmal Model for Adaptive Behaviour in e-Learning Environments”.
Figure 4.4: Formal specification of the adaptation model
In this formal model the following aspects have to be outlined here:
• First of all, this approach is based on the fact that two adaptation processes exist, a didactical and
a pedagogical one. From the viewpoint of knowledge transfer, the didactical one is of primary
importance, because it includes didactical planning, instructional design and assessment.
• Second, FORMABLE is aware of three kinds of adaptation categories: (1) adapting instructions,
(2) adapting instructional sequences and (3) inserting additional instructions. These three types of
operation are sufficient to realise different kinds of adaptation methods as shown in the practical
part of this dissertation. Adaptation of instructional level requires that there are alternative instruc-
tions, which are relevant to a certain didactical objective or suitable for a certain pedagogical state.

62 4. Adaptive E-Learning
Aggregation of instructions is only supported by fully exchanging an instruction with another one
due to the fact that knowledge artefacts are atomic entities. Adapting the instructional sequence
can be characterised in the way that, beginning with a certain instruction, the sequence of the re-
maining instructions is re-ordered with respect to didactical dependencies. Finally, the insertion of
a new instruction also requires that its relevance for the course’s learning objectives as well as the
pedagogical suitability is already given.
• Third, these three main operations for adaptation in e-learning environments do not so far differ-
entiate between adaptability and adaptivity. Real adaptive behaviour would require some thread-
mechanism assessing didactical and pedagogical states and the triggering of one of these three
operations (as compared with the formal model of a multi-purpose adaptive system).
• Finally, FORMABLE does not consider whether adaptation takes place before or within instruction.
Yet, it is possible to determine which learner-dependent or learner independent states are assessed
and which elements of the learning process – parts of the user interface or the learning content – are
adapted.
Overall, FORMABLE serves as a theoretical basis for adaptive e-learning and allows designing and
evaluating methods, features and environments for adaptive e-learning, as shown in the practical part of
this dissertation.
4.5 Conclusions
Summarising the theoretical part of this work, adaptive e-learning is considered to be related to two im-
portant areas: Adaptation systems as well as technology-based learning and teaching. On the one side,
systems theory and adaptation systems provide the basics, how adaptation in learning environments works,
which components are necessary and how adaptivity can be realised. From a systemic viewpoint, this re-
search field can be of importance for defining adaptive behaviour of e-learning system, for example by
focussing the designer’s attention to relevant systemic characteristics or special development streams of
systems theory.
On the other side, e-learning and e-teaching deal primarily with research questions about how instruc-
tional design and assessment should be determined according to learning objectives, which factors and
learner characteristics should be assessed and how the learning process can be adapted in an appropriate
way. Pedagogy and didactics must particularly answer whether the implementation of adaptation methods
for e-learning has a positive effect on the students’ learning and, therefore, is profitable by means of effort
and results.
As pointed out in this chapter, adaptive e-learning deals a lot with compensating disadvantages of
online learning, i.e. that the teacher cannot assess and adapt learning in real time. Based on an extensive
literature survey, the theoretical model in this work, as with other frameworks and approaches, attempts to
describe adaptive behaviour within e-learning environments in order to support the design and evaluation
of such systems. The applicability of FORMABLE is demonstrated in the practical part of this work,
for instance in the next chapter which examines requirements for standards and specifications to support
adaptive e-learning.
Drawing a final conclusion from the theoretical part, it has to be stated that adaptive e-learning can be
generally seen as one possible approach to implementing pedagogical competencies of didactical experts
within information technology.

II. Practical Aspects
63

Chapter 5
Towards Standardising Adaptable Cour-
seware
“ The nice thing about standards is that there are so many of them to choose from. ”
[ Andrew S. Tanenbaum ]
As the theoretical issues given in the last three chapters were rather abstract, the second part of this dis-
sertation aims to examine practical aspects from theory in order to show how the formal model of adaptive
e-learning can be applied in practice. Therefore, this and the next two chapters deal with more pragmatic
topics. This chapter examines aspects of standardised adaptable courseware, the next one attempts to de-
scribe an ideal adaptive e-learning environment and the final chapter of the practical part summarises the
technical realisation of the AdeLE prototype [AdeLE, 2006], a system which resulted from one research
project in the field of adaptive e-learning.
Addressing adaptation within the online learning process, the FORMABLE model states that two
major components of online courses can be adapted. The first one comprises the learning management
system, by means of varying learning activities as well as adapting the user interface. Considering an ideal
adaptive e-learning environment, such issues are examined in the next chapter. The second component fo-
cuses on adaptation of the learning content, which is particularly relevant to didactical considerations.
Standardisation might be particularly problematic in the context of adaptive e-learning due to the asym-
metric nature of the online learning [Jain et al., 2002, p. xxvii].
Therefore, this chapter concentrates on aspects of standardising adaptable courseware. Beginning
with section 5.1, a short overview of the ongoing standardisation efforts and a few selected standards and
specifications in the field of e-learning is given. Thereafter, section 5.2 derives requirements for such an
ideal standard from the theoretical part of this dissertation. Furthermore, in section 5.3 restrictions of
current specifications are pointed out, before section 5.4 introduces a standard-based approach to adaptive
e-learning utilising a commonly-known set of specifications, namely SCORM.
5.1 Standardisation of learning content
Standardisation is targeted in many areas, such as for digital libraries [Paepcke et al., 1998], workflow
management [VanDerAalst, 1998], museum information systems [Moen, 1998], the World Wide Web
[Berners-Lee, 1996] or e-learning [Duval, 2001]. Due to the necessity of high-quality courseware, in-
teroperability issues like transferability and reusability of content as well as the usage of learning object
repositories have to be considered [Qu and Nejdl, 2002]. Therefore, the standardisation process in the
65

66 5. Towards Standardising Adaptable Courseware
field of e-learning has lasted about two decades, but is still in progress and misses different requirements
– particularly concerning adaptive e-learning – as pointed out later in this chapter.
5.1.1 The past
Referring to [Dietinger, 2003, p. 51], the oldest standardisation consortium in the area of e-learning is the
Aviation Industry CBT Committee [AICC, 2007b] examining and developing specifications for describing
aspects of online courses since 1988. According to [AICC, 2007a], the AICC subcommittees aim to
create guidelines and specifications for the following seven issues: Computer Managed Instruction (see
also section 4.2), Communication, Digital Electronic Library Systems (DELS), Independent Test Lab,
Management and Processes, Training Infrastructure as well as Training Technology.
Beside the Dublin Core Metadata Initiative [DCMI, 2007] which focuses on adopting and developing
specialised metadata, the IEEE Learning Technology Standards Committee [IEEE, 2007] can be particu-
larly outlined as one of the pioneers of the standardisation process for learning content. One commonly-
known output of this approach is the so-called Learning Objects Metadata (LOM) standard allowing the
description of instructional resources on the basis of nine categories [Duval, 2001]. For adaptation of the
online learning process particular the educational section of the LOM standard is of interest, as shown in
chapter 7 of this work.
Thirdly, the Instructional Management Systems (IMS) Global Learning Consortium [IMS, 2007i] ini-
tiated in 2001 has a deep impact on standardisation of courseware. Starting with a few specifications only,
[IMS, 2007h] nowadays provides a set of 17 specifications, including all relevant aspects of e-learning,
for example accessibility, competency, digital repositories, e-portfolios, resource list and much more. Be-
sides, IMS also examines abstract frameworks for learning platforms, toolkits or vocabularies in the field
of technology-based learning and teaching. Although many scientific institutions participate in the efforts
of this consortium, IMS follows rather pragmatic principles and contributed several specifications to other
projects and initiatives, for example the SCORM specification set.
With respect to [ADL, 2007a], the Sharable Content Object Reference Model (SCORM) by Advanced
Distributed Learning (ADL) is meant to be a “collection of standards and specifications adapted from
multiple sources to provide a comprehensive suite of e-learning capabilities that enable interoperability,
accessibility and reusability of Web-based learning content”. As mentioned before, SCORM includes
some specifications of the IMS Global Learning Consortium, but also for example the LOM standard.
Overall, ADL tries to exploit the results of various consortia and projects in order to build up a practical
set of specifications to describe courseware. Moreover, ADL also provides a sample implementation of
a SCORM-compliant learning management system [ADL, 2007b]. As SCORM is utilised also in the
practical part of this dissertation, it is going to be described closer later on.
Finally, different projects – mainly research projects – also had a significant impact on standardisation
of learning content. For instance, the “Alliance of Remote Instructional Authoring and Distribution Net-
works for Europe” (ARIADNE, [ARIADNE, 2004]) founded by the European Community was mainly in-
volved in the development of the LOM standard mentioned above [Duval et al., 2001]. Further, [Dietinger,
2003, p. 68ff] reports on other projects like “PROmoting Multimedia Access to Education and Training in
EUropean Society” (PROMETEUS) and initiatives like the “Schools Interoperability Framework” (SIF)
in this area. Additionally, also companies like Microsoft investigated in this area and still are involved, as
shown with the “Learning Resource iNterchange” (LRN) reference implementation [Microsoft, 2000].
5.1.2 The present
In fact, the standardisation process in the field of e-learning is still in progress and only a few specifications
are really standardised by an international organisation like the ANSI, as stated by [Gries, 2003]. Figure

5.4. A standard-based approach to adaptive e-learning 75
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.

76 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 77
(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 dissertation’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.

78 5. Towards Standardising Adaptable Courseware

80 6. An Ideal Environment for Adaptive E-Learning
Addressing techniques on this adaptation level, two types can be outlined here: On the one side,
adaptive presentation techniques include the provision of instructions on different levels of detail, in a
multimodal way, following different layouts or adapting navigational elements of the user interface to
improve orientation. Concerning layout variants, the style-guiding technique is often applied. On the
other side, instruction level adaptation also utilises techniques like conditional text, stretch text, page or
fragment variants and frame-based approaches.
[Henze, 2000, p. 15] outlines that conditional text allows the definition of inner-instructional depen-
dencies between fragments; stretch text aims at describing keywords more comprehensively; and frame-
based techniques (on fragmental level) determine certain rules to present fragments according to a special
order, for example given for didactical reasons. Further, one technique of adaptive navigation, namely link
annotation, would also match this category, because the instruction or at least some navigational elements
have to be adapted.
With respect to FORMABLE, the methods and techniques of this category can be subsumed by the
two operations “AdaptInstructionByDidactics” and “AdaptInstructionByPedagogy”. Therefore, the exact
technique applied on an instructional level can be described by one of these methods, as they are restricted
to selection and presentation of learning content. Furthermore, the formal approach to the last chapter
defines an instruction as a pair consisting of a knowledge artefact (the content) and a so-called activity,
which represents certain features of the learning management system.
Examples of such activities are, besides passively presenting content, assessment methods like quizzes
or tasks, feedback mechanism like form-based questionnaires and even collaborative tasks, such as dis-
cussions or chats. Therefore, FORMABLE not only deals with adapting the content itself, but is also
capable of varying learning activities. Additionally, it is more relevant to examine dependencies between
instructional design (including for example some adaptation technique) and the didactical and pedagogical
models behind the system and, particularly, the effect of adaptation of the learning process.
6.1.2 Adaptation of the instructional sequence
The instructional sequence can be defined as the learner’s path through a course. In the FORMABLE
model, this order is given by the course itself, which is specified as a sequence of instructions, but can
be adapted by the methods “AdaptSequenceByDidactics” and “AdaptSequenceByPedagogy”. Referring
to the learning paradigms in section 3.1, the range of instructional sequencing can reach from a strict
order keeping the learner on exactly one pre-defined path to a course in which the learner can select each
instruction directly.
Restrictions of the instructional sequence are mainly given by didactical aspects, for instance the
macro-adaptive instructional approach in section 4.1 outlines the importance of dependencies between in-
structions or pre-assessments for instructional units. Theoretical approaches for adaptation of instructional
sequences were for example introduced in section 4.3, whereby the knowledge space theory by [Albert
and Hockemeyer, 2002] is an example of the definition of didactical dependencies. On the other side,
[Karampiperis and Sampson, 2005] examine all possible combinations of sequences according to a media
space and a domain model as well as learner observations.
[DeKoch, 2000, p. 18] considers the instructional sequence (the structure) as “the organisation of
the content specification as to which content items will be visited and how they will be visited through
navigation”. Therefore, some methods and techniques of adaptive navigation can also be assigned to this
category of adaptation. [Brusilovsky, 1996] enumerates adaptation methods like global guidance (shortest
path) and local guidance (best fitting link) as well as orientation support addressing the user’s knowledge,
goals and a global view of the system.
On the other side, [Conlan, 2005, p. 35ff] summarises techniques such as relevance (e.g. given by
didactical aspects), direct guidance (next-button with best fitting instruction), link ordering (link-list sorted