PhD_Thesis

CASE-BASED REASONING
IN VIRTUAL REALITY: A FRAMEWORK
FOR COMPUTER-BASED TRAINING
Leonardo Rocha de Oliveira
B.Eng. (Civil Engineering), M.Sc. (Construction Management)
T.I.M.E Research Institute
Department of Surveying
University of Salford
Salford, UK
Submitted in Partial Fulfilment for the Degree of
Doctor of Philosophy
JULY, 1998.

Page ii
Declaration
This is to certify that this thesis:
1. embodies the results on my own course of study and research;
2. has been composed by myself;
3. has not been submitted as an exercise for a degree at any other university; and
4. has been seen by my supervisor before presentation.
Signature of the Candidate: ……………..…………………
Date: 07/ July/ 1998.

Page iii
Acknowledgements
First I would like to thank the Brazilian Government that provided the funding
to develop this thesis at the University of Salford through CNPq, one of the National
Research Councils.
I would also like to express my gratitude to my supervisor, Dr. Ian Watson, for
his expert guidance, friendship and for always making me believe that it would be
possible to conclude this thesis. His expertise in case-based reasoning and artificial
intelligence played a key role in this thesis. “The simpler the better” is something that I
now believe and I will certainly take to my future career.
My gratitude is extended to my co-supervisor Dr. Arkadi Retik for his expert
guidance, friendship and for always making himself available despite the distance
between Salford and Glasgow. His expertise in virtual reality played a key role in this
thesis. “Not only what you see but what you can get from it” are words that I shall not
forget.
I owe special thanks to Martin Holden (Manchester City Council), Peter Gordon
(ACE Scaffolding) and Mark Pearce (Manchester Scaffolding) for participating in the
prototyping process.
My appreciation goes also to people such as Carlos Formoso, Luis Fernando
Heineck, Carin Schmidt, and Carlos Bonin, from the University Federal of Rio Grande
do Sul (UFRGS – BRAZIL) for supporting me to develop this work abroad.
The development of this thesis has also involved my personal life and the people
from the Surveying Department have provided not only the technical support but also
friendship. Ghassan Aouad, Miguel Mateus, Sheila Walker, Claudia de Cesare, Simon
Osbaldiston, Antonio Grilo, Jason Underwood, Sandra Heyworth, Ian Hanbridge, Peter
Unsworth, Lynn Williamson, Martin Betts, and Vanda Tomlinson are also people
whom I will never forget.
My special thanks to my family in Brazil that has always been supportive and
made me believe that whatever happens in my future, they will always be there to help.
My special gratitude goes to my mother, for all the encouragement and moral support.
Finally, my special and most sincere thanks to my wife Sylvie, for her love,
encouragement, and support reading and correcting the English language.

Page iv
In a vocational evaluation I did prior to joining the University, I went
accompanied by a same-age cousin, close friend, and we were asked
questions such as:
Would you prefer to know
a) how the engine of an aeroplane work to make it fly; or
b) why some people fear air flights?
This question, for some reason, was the starting point of our further
conversation, perhaps because we both had a straight answer:
• I said - why would someone be interested in knowing why people
fear air flights when there is so much technological challenge in an
aeroplane?
• She said - why would someone be interested in knowing how those
noisy big things work when you live surrounded by people?
Surprisingly, after all those years we are still very good friends!
Tolerance allows people to see the evergreen not only in black and white.

Page v
Table of Contents
CHAPTER 1 – INTRODUCTION___________________________________________________ 1
1.1 – Overview _________________________________________________________________ 1
1.2 – Research background _____________________________________________________ 2
1.4 – Hypothesis, aims and objectives ___________________________________________ 4
1.4.1 – VR as an interface for case representation______________________________ 4
1.4.2 – VR and the CBR model of cognition ____________________________________ 5
1.4.3 – Common objectives____________________________________________________ 5
1.5 – Research methodology ____________________________________________________ 6
1.6 – Outline of this dissertation ________________________________________________ 7
CHAPTER 2 - COMPUTER-BASED TRAINING ____________________________________ 9
2.1 – Overview _________________________________________________________________ 9
2.2 – Computer-based training_________________________________________________ 10
2.3 – The role of training in today’s society______________________________________ 11
2.4 – Training in the construction Industry _____________________________________ 12
2.5 – Training alternatives _____________________________________________________ 13
2.6 – CBT as an alternative solution____________________________________________ 14
2.6.1 – The cost advantage of CBT____________________________________________ 15
2.6.2 – General advantages of CBT ___________________________________________ 17
2.7 – CBT and human learning_________________________________________________ 18
2.8 – CBT and the dynamic memory theory _____________________________________ 22
2.9 – The dynamic memory theory______________________________________________ 23
2.10 – Learning from cases ____________________________________________________ 27
2.11 – Classroom and case-based instruction___________________________________ 29
2.12 – Synthesis of the chapter ________________________________________________ 30
CHAPTER 3 - ARTIFICIAL INTELLIGENCE AND TRAINING _____________________ 32
3.1 – Overview ________________________________________________________________ 32
3.2 – The origins of AI _________________________________________________________ 33
3.3 – AI in education __________________________________________________________ 35
3.4 – Intelligent tutoring systems_______________________________________________ 37
3.4.1 – The Knowledge module _______________________________________________ 38
3.4.2 – The Student module__________________________________________________ 38
3.4.3 – The Pedagogical module ______________________________________________ 39
3.5 – A review of ITS applications ______________________________________________ 41
3.5.1 – SCHOLAR ___________________________________________________________ 41
3.5.2 – SOPHIE______________________________________________________________ 42
3.5.3 – WEST________________________________________________________________ 42
3.5.4 – WHY_________________________________________________________________ 43
3.5.5 – BUGGY ______________________________________________________________ 43
3.5.6 – GUIDON _____________________________________________________________ 44
3.5.7 – CALAT _______________________________________________________________ 44
3.5.8 – EPITOME ____________________________________________________________ 45
3.5.9 – A brief recap on ITS __________________________________________________ 45

Page vi
3.6 – Intelligent computer aided training _______________________________________ 46
3.6.1 – AI training applications_______________________________________________ 48
3.6.2 – A brief recap on the ICAT applications_________________________________ 51
3.7 – The limitations and the future in ITS and ICAT ____________________________ 52
3.7.1 – The limitations _______________________________________________________ 53
3.7.2 – The future in ITS and ICAT ___________________________________________ 54
3.7.3 – Instruction and the World-Wide-Web (WWW) __________________________ 55
3.8 – Synthesis of the chapter__________________________________________________ 55
CHAPTER 4 - ARTIFICIAL INTELLIGENCE AND CASE-BASED REASONING _____ 58
4.1 – Overview ________________________________________________________________ 58
4.2 – The origins of CBR _______________________________________________________ 59
4.3 – An overview of CBR ______________________________________________________ 61
4.4 – Case representation______________________________________________________ 63
4.4.1 – Case acquisition _____________________________________________________ 65
4.4.2 – Case indexing________________________________________________________ 66
4.4.3 – Case Retrieval________________________________________________________ 69
4.4.4 – Case utilisation ______________________________________________________ 72
4.4.5 – Case-base maintenance ______________________________________________ 73
4.5 – The CBR interface________________________________________________________ 75
4.6 – Review of CBR applications_______________________________________________ 76
4.6.1 – CLAVIER_____________________________________________________________ 76
4.6.2 – ARCHIE______________________________________________________________ 77
4.6.3 – CASEline ____________________________________________________________ 77
4.6.4 – SMART ______________________________________________________________ 78
4.6.5 – GIZMO TAPPER ______________________________________________________ 79
4.6.6 – Brief recap on the applications reviewed_______________________________ 79
4.7 – Synthesis of the chapter__________________________________________________ 80
CHAPTER 5 – TRAINING WITH CASE-BASED REASONING ______________________ 83
5.1 – Overview ________________________________________________________________ 83
5.2 – Learning from past memories_____________________________________________ 84
5.3 – Case-based instruction___________________________________________________ 85
5.4 – Case-based instructional activities________________________________________ 87
5.3.1 – ID and ID2___________________________________________________________ 88
5.3.2 – ECAL ________________________________________________________________ 89
5.3.3 – Recap of the applications _____________________________________________ 90
5.5 – Learning from CBR_______________________________________________________ 90
5.5.1 – Discovery learning ___________________________________________________ 91
5.5.2 – Situated learning_____________________________________________________ 92
5.5.3 – Task centred learning ________________________________________________ 93
5.5.4 – Goal driven learning__________________________________________________ 94
5.5.5 – Common characteristics______________________________________________ 95
5.6 – Review of CBR instructional applications__________________________________ 96
5.6.1 – ASK-TOM ____________________________________________________________ 97
5.6.2 – DUSTIN______________________________________________________________ 98
5.6.3 – CREANIMATE ________________________________________________________ 99
5.6.4 – SPIEL (YELLO) ______________________________________________________ 100
5.6.5 – SCI-ED _____________________________________________________________ 101
5.6.6 – CADI _______________________________________________________________ 101
5.6.7 – A brief recap on the applications reviewed ____________________________ 102
5.7 – Case-based training_____________________________________________________ 103
5.8 – Synthesis of the chapter_________________________________________________ 105

Page vii
CHAPTER 6 – VIRTUAL REALITY CASE REPRESENTATION ___________________108
6.1 – Overview _______________________________________________________________ 108
6.2 – VR: from the labs to the industry ________________________________________ 109
6.3 – VR interface capabilities_________________________________________________ 111
6.3.1 – Interactive VR_______________________________________________________ 112
6.3.2 – Immersive VR _______________________________________________________ 113
6.3.3 – Augmented VR ______________________________________________________ 113
6.3.4 – Networked VR_______________________________________________________ 114
6.3.5 – A recap on the interaction modes.____________________________________ 115
6.4 – VR instruction __________________________________________________________ 118
6.5 – Visualisation and memory recall _________________________________________ 120
6.6 – VR interface for CBR ____________________________________________________ 122
6.6.1 – VR case contents____________________________________________________ 123
6.6.2 – Modelling VR cases__________________________________________________ 125
6.6.3 – Featuring case contents _____________________________________________ 126
6.6.4 – Retrieving VR cases _________________________________________________ 129
6.6.5 – Adapting the VR cases_______________________________________________ 131
CHAPTER 7 – CONCEPTUAL VECTRA DESIGN_________________________________136
7.1 – Overview _______________________________________________________________ 136
7.2 – The choice for a methodology ____________________________________________ 137
7.3 – Developing a CBR _______________________________________________________ 138
7.4 – VECTRA design requirements ___________________________________________ 140
7.4.1 – Training requirements_______________________________________________ 141
7.4.2 – CBR instructional capabilities _______________________________________ 143
7.4.3 – VR capabilities ______________________________________________________ 145
7.5 – Instructional activities design____________________________________________ 146
7.5.1 – Accessing learning capabilities_______________________________________ 149
7.5.2 – Media for instructional delivery ______________________________________ 152
7.6 – The VECTRA development methodology __________________________________ 154
CHAPTER 8 – THE VECTRA FRAMEWORK ____________________________________159
8.1 – Overview _______________________________________________________________ 159
8.2 – Building the VECTRA framework ________________________________________ 160
8.3 – The CBR–VR integration_________________________________________________ 161
8.3.1 – The choice for the software tools _____________________________________ 163
8.3.2 – VR development tools _______________________________________________ 164
8.3.3 – The choice for Superscape VRT 4 ____________________________________ 166
8.4 – The VECTRA framework_________________________________________________ 168
8.5 – Case memory structure _________________________________________________ 171
8.5.1 – Case featuring ______________________________________________________ 172
8.5.2 – The retrieval mechanism ____________________________________________ 173
8.5.3 – Case adaptation_____________________________________________________ 175
8.6 – Instructional facilities ___________________________________________________ 177

Page viii
CHAPTER 9 - THE VECTRA-SI PROTOTYPE ___________________________________181
9.1 – Overview _______________________________________________________________ 181
9.2 – The VECTRA-SI prototype _______________________________________________ 182
9.3 – The task of scaffold inspection___________________________________________ 184
9.3.1 – Experts approach to scaffold inspection ______________________________ 185
9.3.2 – Experts approach to training ________________________________________ 186
9.3.3 – VECTRA-SI training approach _______________________________________ 187
9.4 – VECTRA-SI case-based instruction ______________________________________ 190
9.4.1 – Case gathering ______________________________________________________ 191
9.4.2 – Case-based instructional strategy____________________________________ 192
9.4.3 – Case implementation ________________________________________________ 194
9.4.4 – Featuring cases and Scripts _________________________________________ 197
9.4.5 – Retrieval of cases and Scripts________________________________________ 198
9.4.6 – Case adaptation_____________________________________________________ 199
9.5 – User interface of the VECTRA-SI prototype _______________________________ 201
9.5.1 – The novice interface _________________________________________________ 202
9.5.2 – The intermediate interface ___________________________________________ 203
9.5.3 – The expert interface _________________________________________________ 203
9.6 – Feedback of the experts on the VECTRA-SI_______________________________ 205
9.6.1 – The experts and the VECTRA-SI _____________________________________ 206
9.6.2 – VECTRA-SI and classroom training __________________________________ 207
9.6.3 – VECTRA-SI and descriptions of past experiences _____________________ 209
CHAPTER 10 – CONCLUSIONS_________________________________________________212
10.1 – Overview ______________________________________________________________ 212
10.2 – Review ________________________________________________________________ 213
10.3 – Conclusions ___________________________________________________________ 214
10.3.1 – The CBR – VR integration __________________________________________ 214
10.3.2 – VR case representation_____________________________________________ 214
10.3.3 – VECTRA instruction________________________________________________ 215
10.3.4 – The VECTRA prototype _____________________________________________ 216
10.3.5 – The VECTRA framework____________________________________________ 217
10.4 – Recommendations _____________________________________________________ 218
10.4.1 – The CBR model of cognition ________________________________________ 218
10.4.2 – Design of VR cases_________________________________________________ 219
10.4.3 – VECTRA instructional capabilities __________________________________ 219
10.5 – Future research _______________________________________________________ 219
APPENDIX 1 – THE VECTRA-SI INTERFACE___________________________________221
APPENDIX 2 – CAPABILITIES OF VR WORLD BUILDERS ______________________228
REFERENCES _________________________________________________________________232

Page ix
List of Figures
Fig. 2.7a - Accessing long-term memory: adapted from Gagne (1985) and Wingfield (1979). .................. 20
Fig. 2.7b - The stages of learning (adapted from Gagne 1985). ................................................................ 20
Fig. 4.1 - CBR and its main components ................................................................................................. 61
Fig. 4.2 - The CBR process (adapted from Watson, I.D. (1997)).............................................................. 62
Fig. 5.1 - Achieving an instructional goal (adapted from Gagne (1992)) .................................................. 88
Fig. 6.6.3.a - Contents of a digitised image file.......................................................................................127
Fig. 6.6.3b - Contents of a Superscape™ VR file....................................................................................127
Fig. 6.6.5 - Object-oriented hierarchical architecture. .............................................................................132
Fig. 7.3 - Development stages of CBR applications................................................................................140
Fig. 7.4 - Decision factors for the appropriateness of CBT......................................................................141
Fig. 7.4.1 - Main elements of decision for the appropriateness of CBT....................................................142
Fig. 7.5 - Designing instructional activities.............................................................................................148
Fig. 7.6 - Development tasks of VECTRA applications. .........................................................................156
Fig. 8.5 - Object-oriented hierarchies in the VECTRA framework. .........................................................171
Fig. 8.5.1 - Structure for featuring MOP and Scripts...............................................................................173
Fig. 8.5.2 - The Retrieval algorithm .......................................................................................................175
Fig. 8.5.3 - Structure for case adaptation. ...............................................................................................176
Fig. 8.6a - Evaluation test for instructional activity.................................................................................178
Fig. 8.6b - Passing parameters for an evaluation object...........................................................................178
Fig. 9.2 - Overview of the VECTRA-SI prototype..................................................................................183
Fig. 9.4.2 - Synchronising sounds and viewpoint movements .................................................................193
Figs. 9.4.3a – 9.4.3d - Pictures of an on-site scaffold structure................................................................195
Fig. 9.4.3e - Case model for further implementation in VR.....................................................................196
Fig. 9.4.4 - Accessing case and Script features. ......................................................................................197
Fig. 9.4.5 - Dataflow diagram of the for case/Script retrieval process......................................................199
Fig. A1.1 - First screen of the VECTRA-SI prototype.............................................................................222
Fig. A1.2 - Options for case/Script retrieval ...........................................................................................223
Fig. A1.3 - Choosing case features.........................................................................................................223
Fig. A1.4 - Retrieval for the case that best match the inputted features....................................................224
Fig. A1.5 - VR case showing a scaffold structure ...................................................................................224
Fig. A1.6 - Overhand of scaffolding boards............................................................................................225
Fig. A1.7 - VR case of a scaffold structure.............................................................................................226
Fig. A1.8 - VR case of a scaffold structure.............................................................................................226
Fig. A1.9 - View from the roof top of a building ....................................................................................227
Fig. A1.10 - VR case of a scaffold structure ...........................................................................................227

Page x
List of Tables
Table 4.4.2 – Features of printers as CBR indexes................................................................................... 67
Table 6.6.4 – Retrieval on visualisation by digitised images and VR.......................................................130
Table 7.5.1 – Influencing learning outcomes..........................................................................................151
Table 7.5.2 – The design of instructional events.....................................................................................154
Table 8.3.3 – The capabilities of VRT and WTK for building the VR cases............................................167
Table 9.3.3 – Checklist of activities inspecting scaffold components. .....................................................189
Table 9.6.2 – Comparing instructional activities for classroom and VECTRA-SI training. ......................208
Tab. A2.1 – Aspects of reality supported by VR tools ............................................................................230

Page xi
List of Abbreviations
3D ______ Three Dimension
AI ______ Artificial Intelligence
ASCII ___ American Standard Code for International Interchange
CBR ____ Case-Based Reasoning
CBT ____ Computer-Based Training
DDE ____ Dynamic Data Exchange
DLL ____ Dynamic Link Library
ICAT ____ Intelligent Computer Aided Training
ITS _____ Intelligent Tutoring System
KADS ___ Knowledge Acquisition and Design System
SCL _____ Superscape™ Control Language
VR ______ Virtual Reality
VRML ___ Virtual Reality Modelling Language
VRT ____ Superscape™ Virtual Reality Toolkit
WTK ____ Sense8™ World Tool Kit
WWW ___ World Wide Web

Page xii
Abstract
This thesis involves the development of a case-based training framework that
holds a repository of past experiences (cases) of domain experts. The cases are
represented in Virtual Reality (VR) and contain a real-time 3D simulation of experts
performing their job. The VR case representation also includes the guidance these
experts would provide when training novices. Users can thus retrieve the VR cases and
learn by re-experiencing 3D simulations of on-job activities with expert guidance.
This framework involves research in domains such as Case-Based Reasoning
(CBR), Computer-Based Training (CBT), and VR. CBR plays its role by providing the
foundations for the development of a computer tool that handles a repository of past
experiences. CBT contributes with the requirements for instructional strategies in
training tools. VR addresses the 3D representation of human memories and the user
interface with the instructional activities.
The hypothesis behind this work is that this approach can prove useful for
training for reasons such as: (i) it uses past experiences to support training that is a
natural process of human cognition; (ii) it allows users to learn-by-doing and interacting
with the VR interface; and (ii) it provides the advantages of CBT where users can
access the training course at the time and pace they wish.
The acronym VECTRA stands for Virtual Environment for Case-based
TRAining and it is a framework to ease the development of CBR instructional
applications. This framework has provided the development of the VECTRA-SI
application where on-job experiences of experts in Scaffold Inspection are implemented.
This thesis shows that the VECTRA framework provides a tool that can be used for the
development of intelligent instructional applications for a range of domains.

Chapter 1 – Introduction
1.1 - Overview
This chapter provides an overview of this thesis describing the research
background and the reasons that motivated its development. The hypothesis and
objectives of this work are also discussed in this chapter and are followed by a
description of the research activities involved in the development of this work. The final
section provides an outline of this thesis that briefly describes the contents of each
chapter.

Page 2
1.2 - Research background
Intelligence is the ability to respond successfully to new situations and the capacity to learn from
one's past experiences.
Gardner, H. (1992)
Artificial Intelligence (AI) is a research field that attempts to create computer
systems that emulate human intelligent behaviour (Minsky 1968; Barr 1981;
Feigenbaum 1995). This attempt can either regard (i) the development of computer
systems that use knowledge models to solve or provide advice for problems that
otherwise would require human expertise, or (ii) the study of models of human
cognition that allow the representation of knowledge.
The former deals with the development of computer systems that emulate
human intelligent behaviour when performing a task. These systems hold a body of
knowledge of the application domain that a human expert would need to perform the
same task (McCordick 1979; Boden 1987). Results of this AI research area cover
capabilities related to human intelligent behaviour such as natural language
processing, automatic programming, planning, image analysis, decision making and
problem solving.
The latter research area of AI regards the study of structures and paradigms
that emulate the processes associated with human intelligence such as thinking and
learning. Research in this area involves the study of human cognition, intelligent
behaviour and their representation in computer machines (Schank 1973; Winston 1975;
Norman 1975). Results of research in this area are paradigms for knowledge
representation such as semantic networks, predicate logic, frames, object-oriented
languages and case-based reasoning (CBR).
CBR is the AI paradigm focused on in this thesis and it emerged from research
in cognitive science where the act of recalling a previous experience is emulated. This
act is a common practice in intelligent human behaviour where the remembrance of
past experiences supports human reasoning to perform tasks such as problem-solving,
learning and decision making (Schank 1982; Riesbeck 1989, Kolodner 1993, Leake
1996).
Another aspect of CBR is that even applications that are not originally designed
with instructional purposes can provide learning as a ‘side effect’ of using CBR tools
(Anderson 1985; Veloso 1992; Kolodner 1993). In CBR, this ‘side effect’ learning is

Page 3
achieved by comparing the situation users are facing with similar past cases in the
computer, in a process named analogical reasoning (Schank 1988; Burstein 1989;
Veloso 1989).
Regardless of this ‘side effect’ learning, there are CBR applications specially
designed to provide instruction. Their instructional strategy usually relies on discovery
learning where users dig into the systems searching for a case that contains the
knowledge they want to acquire. More refined instructional strategies involve CBR
applications challenging the users’ knowledge by asking them questions before
presenting a case with the correct answer. The advantage of this latter instructional
strategy stems from its more active instructional approach in comparison to the passive
discovery learning.
This thesis focuses on the representation of cases and introduces Virtual Reality
(VR) both as technique for the visualisation of past experiences and as a technique
involved in the whole working cycle of CBR. The VR technology taken in this work uses
an object-oriented language to build the VR cases that allows access to the properties of
the VR objects. Every object in a VR world has its own attributes that can be accessed
and dynamically modified by the developers of the VR cases. This brings new
possibilities for the design of CBR applications, combining the dynamic memory theory
with an object-oriented programming language that also originates from a model of
human cognition (King 1988).
The implications that VR technology could have over the whole working cycle of
CBR as a paradigm of human cognition motivated this work. VR is also a powerful
interface to provide instruction due to its capabilities to simulate reality. Thus, the VR
interface’s capabilities to simulate on-job situations and stimulate the use of CBR
instructional applications were also key motivations for this work.
The VR cases in this thesis are built using an object-oriented language that
incorporates the processes of case featuring, retrieval and adaptation of past memories.
Differently from digitised multimedia files that require an external description of their
contents (see Section 6.6.3), VR cases allow to access the contents of their files. This
access gives a new perspective to the CBR model of human cognition that is
investigated in this thesis.

Page 4
This work also involves the development of a framework to build instructional
applications where VR simulations of on-job past experiences are held in a case
repository so that users can retrieve and learn from them. This framework has been
used to build a prototype and the application domain regards training in the inspection
of health and safety regulations on scaffold structures. Past experiences of experts in
this task are modelled in VR and users can retrieve and take their learning from them.
1.4 - Hypothesis, aims and objectives
CBR can be seen both as a methodology to build AI systems and as a model of
human cognition. The hypothesis behind this work is that VR can play an important
role in these two aspects of CBR. VR as a system’s interface allows users to have access
to past experiences in a simulation environment as close to reality as computers can
currently provide. For the model of cognition, the object-oriented language of the VR
tool allows direct access to the contents of the files and the retrieval of individual
objects. This language also makes possible the access to distinct pieces of the past
experiences held in each case.
This thesis has therefore two aims, exploiting issues regarding VR (i) as an
interface for case representation and (ii) as a framework capable of holding the CBR
model of cognition. The objectives of this thesis regarding each of these two aspects of
CBR and those which are common to both of them are described below.
1.4.1 – VR as an interface for case representation
Dearden (1995) stated that “the success of any interactive intelligent system is
dependent not only on the quality or on the appropriateness of the knowledge
encapsulated within the system but also on the quality of the interaction that the
system supports”. From this statement, it can be inferred that the interface in CBR
plays an important role in the quality of the support provided to users.
The user interface is also a major concern for computer-based training
applications for reasons such as stimulating users to take the tool, accessing different
learning preferences and providing an instructional methodology that complies with
the domain considered. An objective of this work is to investigate VR’s capabilities to
represent past experiences. More specifically, the objectives regarding the VR interface
for CBR are:

Page 5
• to analyse the VR requirements to perform the task of case acquisition for the
creation of the VR cases;
• to investigate the VR capabilities to provide the instructional events and contribute
to accessing different learning styles and preferences;
• to identify the role that the VR cases can play in issues related to computer-based
instruction such as the stimulation of users to take the tool, learning effectiveness
and simulation of on-job situations
• to exploit the VR-CBR instructional approach for the specific domain of inspection
of scaffold structures.
1.4.2 - VR and the CBR model of cognition
The second aim of this thesis is to exploit the capabilities of VR technology to
emulate the human process of reasoning that is at the foundations of CBR. The specific
objectives regarding this issue are:
• to investigate the role that the access to the contents of the VR files can play over
the CBR working cycle and its capabilities for retrieving, featuring, indexing, and
adapting cases in the repository;
• to develop a framework that makes the broad set of the ideas of the dynamic
memory theory operational, such as the breaking down of past experiences into
small pieces and allowing the featuring and retrieval of each independent piece of
memory;
• to identify the feasibility of creating a framework that allows the development of a
shell aiming at speeding up the process of building applications;
1.4.3 - Common objectives
This thesis involves objectives that are common to the CBR-VR model of
cognition and interface simulating past on-job experiences. These objectives are related
to the instructional capabilities of this integration between CBR and VR and are:
• to investigate the amount of work involved in acquiring and representing the VR
cases to build CBR instructional applications;

Page 6
• to evaluate the VR cases in terms of computer hardware requirements such as
storage space, processing speed, graphic cards and interaction devices;
• to determine the programming requirements of a framework allowing to work over
the VR cases and coping with the demands of the instructional strategies;
• to identify the requirements for case featuring and retrieval that best fit the CBR as
a model of cognition and the domain of instruction;
1.5 – Research methodology
A prototype has been developed as part of this research to further explore the
objectives described in the previous section. The VECTRA acronym stands for Virtual
Environment for Case-based TRAining. In this thesis the VECTRA acronym will be
used to refer to the framework for the development of case-based instructional
applications. As a prototype, VECTRA-SI refers to the application domain of Scaffold
Inspection that is used to evaluate the hypothesis, aims and objectives of this thesis.
The development of the VECTRA prototype is part of the research methodology
adopted in the development of this thesis. This methodology follows the development
life cycle of information systems and includes a literature review on the main issues
involved in this research work. The combination of the methodological requirements
from research and information systems development guided the work in this thesis and
involved the following stages:
• Literature review – it is the first step and involves a review of issues such as
CBT, AI instruction, CBR and VR education;
• Prototype conceptualisation – involves the decision for the application domain
and the identification of domain experts willing to support this research;
• Choice for computer tools – involves the choice for the hardware and software to
develop the VECTRA framework and VECTRA-SI prototype so as to fit the domain
aspects;
• Knowledge acquisition – involves interviews with the experts to gather their on-
job past experiences and index the case repository;

Page 7
• Case analysis – involves the analysis of case contents and the requirements prior
to their implementation in the computer tool;
• Prototype implementation – deals with the implementation of the prototype in
the computer and involves tasks such as the design of the cases in the VR world
builder in accordance with the domain’s instructional requirements and the CBR
working cycle;
• Prototype verification – takes the feedback of the experts and other people
involved with IT and education on the qualitative aspects of the application.
The original proposal involved one last stage of prototype validation where the
instructional capabilities of the prototype were to be tested with students and trainees.
However, learning evaluation is a complex and time-consuming task that was left for
future developments. Further details about the work carried out in each stage of the
adopted methodology are discussed further in Chapter 7 and the following section
briefly describes the contents of each chapter of this thesis.
1.6 - Outline of this dissertation
This dissertation contains two central themes regarding the use of VR in the
CBR paradigm that are: the VR capabilities to represent the ideas that conceived the
CBR as a model of human cognition and the use of VR as an interface for the
representation of past experiences. These two issues have been evaluated in an
application prototype that aims at providing training for the inspection of scaffold
structures.
In order to provide a sequence to explore each of these issues, this dissertation
has been organised as follows:
Chapter 1 gives an overview of this dissertation and briefly describes the main topics
involved. This chapter also sets the issues regarding the research work such as its
motivation, hypothesis, objectives and the domain of its application.
Chapter 2 discusses training and focuses on the role that CBT can play for
organisations and their employees. The case-based instructional approach is then
introduced along with a discussion about the human process of learning.

Page 8
Chapter 3 discusses the origins of AI, focusing on instructional applications and
reviewing their origins, the development tasks and instructional strategies involved as
well as state-of-the-art architectures and applications.
Chapter 4 introduces CBR as a technique for the development of AI systems, discusses
its working cycle and reviews some state-of-the-art CBR applications.
Chapter 5 provides an overview of CBR instruction focusing on the learning strategies
it can support and reviews some applications. Towards the end of this chapter, VR is
introduced as an alternative interface for instructional applications.
Chapter 6 presents an overview of the VR technology and its capabilities to interface
with the users and simulate on-job situations. VR is analysed according to its potential
for the representation of CBR cases focusing on the design, the internal architecture
and the role that the VR cases can play for CBR.
Chapter 7 presents the conceptual stage of development of the VECTRA-SI prototype,
describing the choice for a methodology and the work carried out prior to its
implementation in the computer.
Chapter 8 describes the development of the VECTRA framework and its capability to
hold interdisciplinary VR case-based instructional applications.
Chapter 9 describes the whole development process of a prototype for training in
scaffold inspection built in the VECTRA framework.
Chapter 10 presents the conclusions drawn from the development of this thesis,
provides recommendations for further developments in the VECTRA framework and
directions for further research.
Appendix 1 presents a training session with the VECTRA prototype.
Appendix 2 describes the capabilites of some VR modellers for IBM-PC machines.

Chapter 2 - Computer-based training
2.1 - Overview
Computer-based training (CBT) has lately been perceived as an attractive
technology for those who sponsor training as much as for those who receive it
(Shlechter 1991; Dean 1992; Ravet 1997). However, to take the most from CBT, this
chapter will show that a joint effort between employers and the designers of the
instructional course is required. This joint effort includes issues such as motivating
trainees to take the training, addressing individual learning preferences, providing a
instructional methodology and evaluating trainees’ performance at work.
The design of the CBT is the focus of the discussion in this chapter that starts by
reviewing the state-of-the-art in CBT and its role in professional training. Then, human
learning preferences and cognitive aspects of training are reviewed. Next, an
alternative methodology for the development of instructional computer tools is
discussed. It relies on the dynamic memory theory (Schank 1982): a theory that
matches the training requirements of the industry with the human learning process.
Finally, the implication of this instructional approach over the development of CBT is
reviewed and the synthesis of this chapter is drawn.

Page 10
2.2 – Computer-based training
CBT can be defined as an instructional experience between the computer and
the learner (Harrison 1990; Shlechter 1991; Dean 1992). The computer provides the
stimulus and the learner responds, in an interaction resulting in progress towards
increased skills or knowledge. For instance, to enable someone to acquire the
knowledge and skills that comprise competence to become an expert in a certain task,
such as safety regulations for scaffolding, CBT can be used as an alternative media to
provide the instruction.
Computer science has been providing software for education since the early 60s,
though it is over the last few years that these applications have been receiving greater
attention (Gery 1995, Brooks, D.W. 1997; Schank 1997). Changes on the business side
of training, where companies require a quickly adaptable and skilled work force (Senge
1994; Schank 1997) are reasons behind the interest for new training alternatives.
The interest in CBT can be justified for reasons such as the power of current
hardware and software to handle training applications, the developments of hardware
devices (joysticks, steering wheels, gloves and head-sets) providing new interface
capabilities, multimedia facilities making applications more attractive and useful to
users and the increasing availability of computers at home and at the office (Dean 1992;
Boschmann 1995; Heinich 1996; Schank 1997; Brooks, D.W. 1997).
The availability of hardware at affordable prices and software tools that do not
demand highly skilled programmers to develop applications have also contributed to
this interest in CBT tools (Cardinale 1994; Reynolds 1996; Tucker 1997). Commercial
software for the development of CBT tools (also called as CBT Shells or authoring
training tools) reduce the cost and minimise design obstacles for the development of
applications (see Section 3.7.1).
These Shells also provide facilities allowing checks of how much time has the
learner spent on the instructional program and print out reports of a user’s
performance for each instructional session, thus helping to determine the effectiveness
of the training. CBT Shells usually include a programming language where special
routines can be developed to integrate applications to software packages such as
databases and spreadsheets.

Page 11
In spite of the growing popularity of CBT as an alternative instructional media,
different domains, instructional requirements, and learning preferences, still constitute
barriers to the use of this instructional media. Therefore, even if the tool contains a
robust body of domain knowledge, it can fail to motivate the learners to take the tool
(Schank 1997; Ravet 1997).
Reasons behind the possible failure of CBT are varied and include the lack of
users’ motivation to improve their skills, the instructional methodology adopted for the
domain, the system’s interface that is difficult to work with, the lack of users’
enthusiasm in taking the instruction, and the difficulties in accessing users’ learning
preferences. Further details related to the effective implementation of CBT are given
later in this chapter and the following section discusses the role that training can play
for organisations.
2.3 - The role of training in today’s society
The industry depends on the skills of thinking, collaboration, creativity, inquiring, innovation, and
endless learning. Every company is a product of its employees' abilities.
(Senge, 1990).
As a response to an international dimension of competition in the marketplace,
companies of all sectors have come under pressure to offer higher quality and more
competitively priced products (Senge 1994, Reed 1994). Enterprises that want to
succeed have to keep their work force properly trained and up-to-date with the
technological advances and the changes they bring to the skills required to perform the
work (Senge 1994; Schank 1997).
Today’s professional expertise soon becomes outdated and companies are
required to invest in lifelong training programs to keep a skilled work force (Senge
1994; Schank 1997). A survey revealed that in 1990 in the USA already 44% of
corporations were willing to spend about one thousand American dollars a year per
employee for training (Senge 1990). Thus, companies are aware of this need for training
and of the improvements it can bring to their work.
Schank (1997) observed that people do not like to spend time learning new
skills. In fact, learning new skills are “viewed as necessary evil by management and
with disdain by employees” (Schank 1997). Other reasons that have been discouraging
training are the cost and time involved with the training courses and the poor

Page 12
instructional approaches that often fail to motivate trainees to go through the training
courses (Pea 1989).
Authors such as Senge (1990), Dean (1992) Lee (1995) and Ravet (1997) show
that motivating employees to undertake training is the role of the leaders of a company.
The management staff is also responsible for defining the training goals, allocating the
resources and deciding the instructional approach that best fits the company’s work
force. These professionals have to assure that (i) the instructional approach is
consistent with the company’s goals and (ii) these goals are being met by regularly
evaluating the work force (Senge 1990; Ishikawa 1991; Jenkins 1996). Further details
on the role played by employers, course designers and employees towards training are
discussed in this chapter. The following section focuses on training in the construction
industry that is the application domain of this work.
2.4 – Training in the construction Industry
It is difficult to imagine an industrial sector that could not benefit from training
and the construction industry is no exception. Difficulties that are inherent to the
domain such as the need to move the work force from site to site, the different local
conditions faced at each work place, the differences in each construction project and the
ever changing weather conditions make it difficult to establish comparisons of work
efficiency and the need for training (Tatum 1988; Strassman 1988; Latham 1994; Prais
1995).
In manufacturing industries, a large number of unskilled workers can efficiently
operate the machinery when set up under the supervision of a single skilled person
(Strassman 1988). This does not apply to the construction industry where the tasks and
work are far less sequential and uniform than in the manufacturing industry.
Moreover, most of the work in construction is performed individually and under poor
supervision (Latham 1994). Due to these characteristics and to difficulties in assessing
productivity, the construction industry continues to use an inadequate proportion of
skilled labour (Prais 1995). The result is that the low level of efficiency of the work in
construction occurs both at a company’s internal level and at the general industry level
(Tatum 1988).

Page 13
Another difference between the manufacturing and construction industries is
the scale of the competition that they face. In the manufacturing industry the
competition occurs on an international scale where the price and quality of the final
products quickly addresses inefficiencies (Strassman 1988; Tatum 1988). On the other
hand, difficulties in evaluating working inefficiencies and the local scale of competition
that the construction industry is exposed to (Lathan 1994; Prais 1995) have been
allowing the presence of low productivity, low innovation rate, high price of the final
products and high accident rates.
Prais (1995) presented a survey where standard examination tests were
performed with British and German building craftsmen, allowing for a comparison
between the two countries. The evaluation included such tasks as brickwork, carpentry,
plastering, roadwork, and painting. Results of this survey showed that the British
labour was well behind the Germans.
Although questions were raised as to whether influences emerging from the
whole educational process in these countries had played a role in the results, the author
made clear that the training provided by governmental institutions and construction
companies were the main reasons behind the higher German score. Studies like Prais
(1995) and Lathan (1994) provide an indication that training is a key factor to
compensate for the construction industry’s current inefficiencies.
2.5 - Training alternatives
In order to supply training, companies can choose from a range of methods,
though the most popular form of training has been instructor-led, with face-to-face
contact in the classroom (Milheim 1994; Dean 1992; Tucker1997). Nonetheless,
Harasin (1995) shows that there is no evidence to support that this is the best form of
training. In fact, authors such as Wells (1990) and Hiltz (1994) show the opposite,
where CBT applications have achieved superior training outcomes than classroom
training.
Under increased pressures from global competition, the effectiveness of hiring
professional trainers to improve working skills has been questioned by authors such as
Shlechter (1991) and Dean (1992). Instructor-led training as any other training method
also presents difficulties and limitations (Gagne 1992). For instance, it is likely that a

Page 14
group of classroom trainees will include individuals with different learning preferences
and knowledge background. It may slow down the group’s instruction at various stages
of the training sessions as the trainer tries to address these differences individually.
The training sessions can thus easily fail to provide employees with equal time
privileges or to cater for their own personal learning preferences. The hiring of
professional trainers and educational facilities can also prove difficult and entail costly
investments for a company without guarantee of a proper return (Lee 1995; Gery 1995;
Masie 1995).
The effectiveness of other training methods has also come under judgement. On-
the-job guided training, videotapes and reading materials have also become
controversial. For instance, videotapes are expensive to develop and edit, and once
outdated, a whole new process of filming becomes necessary to update the training.
Therefore, there are pros and cons for each of the training media mentioned, as it is
further reviewed in Section 9.6.2 where a comparison between these training media is
presented.
The following section introduces CBT as an alternative training media and
discusses aspects such as its cost, its development time and the role that CBT can play
as a corporate knowledge for companies.
2.6 - CBT as an alternative solution
Training in the industry is labour-intensive, costly and highly dependent on the
availability of skilful experts. Although these professionals may have an extensive
knowledge of their domains, they may fail to be skilful trainers or may simply not have
the time to spend training novices. As a result, private organisations have become open
to new training alternatives (Shlechter 1991; Dean 1992; Brooks, D.W. 1997).
While CBT was not intended to replace live instructors or teachers, many
businesses realised that computers could handle certain training tasks. CBT has come
in as an interesting training alternative (Schank 1997). In fact, advances in computer
technologies, such as computer networks, databases, interactive multimedia, friendly
interfaces, hardware devices, and now VR running on personal computers, have
enabled companies to get quality training opportunities with limited budgets.

Page 15
Not much longer than a decade ago, CBT started to gain popularity in the form
of tutorials for secretaries and writers on how to use word processing programs (Brooks,
D.W. 1997). This kind of applications became popular because they were easier than
reading through pages of unfriendly user’s manuals and also presented the advantage
of being on-line with the word processor package. These early CBT programs soon
branched out into other computer-related training functions including tutorials on
database programs and spreadsheets.
Nowadays, software applications often do not even provide a user’s manual on
paper, as it can be quite tedious to read, expensive to print and edit, and even lacking
effectiveness in several domains (Reynolds 1996). On-line help for office tasks such as
changing the toner of a printer where the multimedia CBT tool includes animated
illustrations associated to text is now standard. An advantage is the real-time on-job
training that avoids the hassle of keeping training manuals available to everyone.
Advances in computer technology and the use of computers at the office support
the growing popularity of CBT. Faster CPUs with powerful graphic interfaces have
allowed CBT programs to become highly sophisticated, effective and interesting to use.
Interactive interfaces, colourful illustrations, and other graphics interface capabilities
have pushed the growth of CBT. As a result, CBT applications are no longer restricted
to computer related topics. Today’s CBT market offers a range of training applications
and several domains are getting benefits from CBT.
CBT is currently available in a number of applications that range from
relatively simple topics (such as providing typing skills) to topics as complex as the
training of astronauts to perform their job in space (see Section 3.3). The advantages
that CBT can offer justify its popularity and the ever-growing number of applications
available in the market. Further details on the cost advantage of CBT are discussed in
the next section.
2.6.1 - The cost advantage of CBT
For a company, the cost factor plays an important role in choosing the type of
training to apply (Schank 1997). The cost advantage CBT can provide seems to have
played an important part in its growing popularity. However, evaluating the cost of
implementing CBT properly is not an easy task (Dean 1992; Ravet 1997). If CBT can

Page 16
represent an expensive initial investment, this investment could soon be “reduced” as
the company spreads it over a number of trained employees. Another advantage CBT
can offer in terms of cost is that future updates in the training course may be performed
at a very low expense, a fact that contrasts with other training methods such as
classroom training where the update cost is usually equivalent to the initial
investment.
An example of the cost-benefit of CBT was discussed in a meeting of the VRT1
users’ group. A company needs to train its 100 employees who are scattered throughout
Europe (or even the world) to sell a new product over the next couple of weeks.
Different training options were studied and their cost compared. First, hiring a trainer
to fly from location to location and present this course material was considered, but the
cost entailed by such an option seemed rather high. Another alternative discussed was
to bring all the employees together for a seminar. This implied taking into
consideration not only the daily expenses of the trainer, but also those of the 100
trainees, not to mention the difficulty of co-ordinating the schedules of all the
participants. Therefore, this second option seemed to entail a rather overwhelming task
and expensive cost.
A third alternative proposed was to take a couple of weeks to develop a CBT
course. Once finished and tested, copies (floppy-disk or CD-ROM) could be made and
sent to all of the 100 employees with a message indicating that the course must be
completed within a week. One point to consider is the initial cost of the development of
the CBT course. Nonetheless, this cost also exists for the "traditional" training
programs and when this initial cost is spread over 100 distribution copies, this project
may turn out to be a relatively easier and less expensive solution as scheduling conflicts
and travel costs are avoided.
Still considering the CBT alternative, future updated releases of the training
package can significantly reduce its cost. Obviously, there are situations when the
capabilities of CBT may not fit well with the training task. For instance, the example
given above fits well with the update of products such as mobile phones or new releases
of products that the salespeople already have the skills to sell. However, a novice that
does not yet have the skills to sell any product has different needs that CBT may not so
1 Users’ group of the Superscape™ virtual reality toolkit.

Page 17
easily be able to address. Therefore, the application domain also plays an important
role in deciding whether the CBT alternative will be an appropriate solution.
2.6.2 - General advantages of CBT
Several reasons support the use of CBT. Perhaps the most popular is that
learners can take the instructions at their own pace, moving onto new stages only when
they have mastered the current, and free from any pressure from other learners. With
the current multimedia interfaces, CBT can provide an instructional environment that
is attractive to users and makes learning fun (Schank 1997), thus reducing the
potential for distraction or disruptive classroom behaviour.
In spite of the reasons provided above and the power of current software tools
facilitating the development of applications, there are other advantages that CBT can
provide when compared to classroom training. The following list presents advantages of
CBT that have been compiled from a review of the work of authors such as Dean
(1992); Cardinale (1994); Gery (1995); Reynolds (1996); Brooks, D.W. (1997); Ravet
(1997); and Schank (1997):
• CBT can provide instructional events matching individual learning preferences by
covering a variety of multimedia instructional deliveries;
• CBT can help overcome potential barriers to training such as the instructional level
of the training activities that can be too high for some and too low for other students;
• learners can start, stop, restart and repeat the training session as they wish,
independently from the availability of a tutor, and allowing training for people that
have time limitations for traditional courses due to childcare, transportation
problems, or scheduling conflicts;
• CBT can count on the help of Internet delivery and portable computers, thus
reaching people with disabilities or living in remote areas more easily;
• CBT constitutes a kind of simulation that learners can use until they feel confident
enough to face the real situations;
• CBT can simulate on-job activities that are of rare occurrence, too expensive to
create real simulations for, potentially dangerous to the learners’ health, or that
learners may interfere with and cause damage to;

Page 18
• learners do not lose part of the training due to temporary distraction, tiredness or
difficulties related to the oral comprehension of the trainer as they can replay and
re-read the material until fully comprehended;
• CBT programs can include the expertise and teaching experiences of various
professionals, thus reducing biases on apprenticeship;
• CBT can avoid or diminish the need of often rare and usually busy experts to
perform the instructional task;
• the contents of the CBT instruction can be updated and include the feedback given
by the users;
• CBT programs can be installed on private networks, allowing online availability for
employees, security for copyrights and other advantages that Intranet/Internet
support can provide to companies;
• CBT programs can be linked with other training techniques or be part of training
courses involving other activities such as classroom and on-job work.
Gagne (1992) cited that no computer can offer the same level of personal contact
that face-to-face trainer/trainee interaction can provide. Nonetheless, even for domains
where the presence of trainers is required, these professionals can use CBT as part of
their training courses for such tasks as helping to illustrate their point, providing
homework adapted to the students’ background and evaluating apprenticeship.
The way different individuals learn and react to the idea of learning from a
computer tools is a key factor to the acceptance of CBT. It is essential to have a clear
idea of the users’ background and learning preferences prior to choosing CBT as a
training alternative. Further details on this issue are discussed in the following section.
2.7 - CBT and human learning
Educational methods based on research in cognitive science are the educational equivalents of the
polio vaccine and penicillin. Yet, few outside the educational research community are aware of
these breakthroughs or understand the research that makes them possible.
John T. Bruer, 1994.
Instruction from a book, from a teacher, or from a computer can provide learning
that is related to the instructional strategy of these different media and not to the
learning source. For instance, learning from past experiences can be provided by

Page 19
reading about them from a book, by watching their filmed illustrations or either by
reading about them and watching them on a computer screen. Section 2.5 shows that
different instructional media can be more adequate to deliver certain types of
instructional strategies. In this section, the human learning process is seen as
independent from the learning source.
Another possible source of misunderstanding is the language used in references
on pure cognitive psychology and computer related learning such as CBT and CBR. For
instance, when learning occurs by associations between things or experiences, CBR
references use the term “learning by analogy”. On the other hand, references in
cognitive psychology use instead the term “intellectual skills” for this type of learning
(Gagne 1975; 1992). To avoid possible misunderstanding with the jargon used in the
dissertation, a glossary is provided at the end of this work. Preference is given, though,
to the jargon used in references related to the model of cognition behind CBR.
Even the words training and learning are sometimes misused in references.
Learning and training are two distinct activities and must be addressed differently.
The Concise Oxford Dictionary presents training “as the act or process of teaching or
learning a skill” and learning as “the act, process or experience of acquiring knowledge
by study” (reading books, observing someone developing a task, attending a training
course or simply reasoning based on one’s own mental process).
Training is an instructional process that aims at acquiring skills to carry out a
specific task. In terms of computer applications, Dean (1992) have defined CBT “as a
tool to help people learn to do something previously beyond their capabilities”. On the
other hand, learning is an individual process that differs from one individual to
another. Thus, individuals attending the same training course may achieve different
levels of learning.
Klatzky (1980), Anderson (1985), and Gagne (1985) cited that learning occurs by
transferring information from the short-term to the long-term memory (Figure 2.7a),
taking place as an individual response to a stimulus from the external environment. On
its way from the short to the long term memory, learning requires people to think about
it, relate it to other things they know, question it, and transform it into their own
words. The authors also cited that the result of learning is a permanent change in the
learner’s mind.

Page 20
Instructional
media
Instructional
methodology
Instructional
events
Training
Course
Short-term
memory
Long-term
memory
Feedback
Human memory
Fig. 2.7a - Accessing long-term memory: adapted from Gagne (1985) and Wingfield (1979).
Authors such as Kolb (1984), Wingfield (1979) and Gagne (1975) have
decomposed learning in sub-processes that occur in the human mind. For instance,
Wingfield (1979) cited that the three major stages of learning are (i) input, (ii) storage
and (iii) retrieval. Kolb (1984) describes learning as a four-step process that is (i)
perceiving information, (ii) reflecting on how it will impact an aspect of our life, (iii)
comparing how it fits into our own experiences, and (iv) thinking about how this
information offers new ways for us to act.
Gagne (1975) has gone further in decomposing the learning process into nine
stages that were reduced to six events in later publications (Gagne 1992). Figure 2.7b
shows the reviewed version of the author and indicates the sequence of the occurrence
of these events and the processes associated with them. Further details on the work of
Gagne (1992) are given in Section 5.4 that describes two CBT applications relying on
his theories of learning capabilities.
Motivation
- expectancy
- stimulus Apprehension
- attention
- perception Acquisition
- understanding
- coding Retention
- storage
Recall
- retrieval
Performance
- response
Stages
Time
Fig. 2.7b - The stages of learning (adapted from Gagne 1985).

Page 21
Gagne (1985) explains the role played by each stage in human learning and how
to properly achieve them. A brief description of each learning stage, as given by the
author, is shown below.
• The Motivation stage - it establishes expectancy in the learners. They anticipate
the reward that they will obtain when the learning goal is achieved.
• The Apprehension stage - it concerns catching the learners’ attention, a process
initiated with the stimulus caused by the first stage but a process that may not last
long if the training does not appeal to the learners. It is also important to note that
different individuals have a different perception of the stimulus and therefore a
different response to it because of their different learning preferences.
• The Acquisition Stage - it refers to the stage where the learners transform the
original information into neural information. This information is first stored into
the short-term memory and whether it gets through to the long-term memory will
depend on the effectiveness of the learning.
• The Retention stage - it deals with the storage of the information acquired into
the learners’ long-term memory. This is the stage of learning the scientific
community has the least knowledge of. Some aspects of this stage are known, such
as the fading with the passage of time, the possibility of new memories replacing
older ones and the gathering of different aspects of the same experience by different
individuals. Others aspects, such as the limit of the capacity of the long-term
memory, how to access it effectively and how long it will keep an experience for, are
still a mystery.
• The Recall stage - it is the stage that allows the learners to apply the knowledge
they have gained in one context to other situations, by retrieving the memories they
have stored. Although the retrieval appears to be most effective when close to the
time of the learning, cues can also help the retrieval process (i.e. recall can be
induced by different means than the one used to transmit the information). Thus,
effective instruction needs to provide the learner with the means to trigger and
make resurface the relevant information that has been stored.
• The Performance stage - it refers to the stage where the learners respond to a
situation using the knowledge they have stored, thus proving that learning has

Page 22
actually occurred. This is the stage when learners perceive they have achieved the
goal set in the first stage and hence close the “learning loop”. Questions related to
the number of times the learners should be tested to prove that learning has
actually occurred and the length of time this new learning will stay in the learners’
mind remain unanswered.
The breaking down of the human learning process helps to identify the role that
employers, employees and designers play throughout the implementation of CBT.
Another important factor is to recognise that the means to reach each stage of learning,
like motivation for instance, vary from individual to individual. Learning preferences
are also influenced by age, background and other personal characteristics that are
proper to each individual.
Although it seems difficult for CBT to cope with all these individual differences,
there are instructional approaches that have been producing satisfactory results. One
of these instructional approaches is the case-based instruction that was introduced by
Schank (1982, 1995; 1997) and is further discussed in the next section.
2.8 – CBT and the dynamic memory theory
Improvement in memory rests almost entirely on improvement in techniques of learning. First, it is
important to attend to the material; second, to give it organisation; and third, to rehearse the
material as much as possible.
Wingfield (1979)
Previous sections of this chapter have shown that CBT is an alternative method
of instruction and like any other instructional alternative, its effectiveness faces
barriers imposed by individual learning preferences. Studies in cognitive psychology, as
shown in the preceding section where the learning process is broken down into
instructional stages, can also be seen as efforts towards providing instruction that
access each stage properly.
Another area of cognitive psychology that has been contributing to the design of
instruction deals with studies of human memory and cognition. One of the results of
the studies conducted in this area is the dynamic memory theory (Schank 1982; 1995;
1996) that proposes a model where human memory is seen as a repository of past
experiences. Intelligence that helps learning is related to the act of remembering past
experiences and the knowledge stored in memory helps processing new situations.

Page 23
Learning is thus seen as a dynamic process where new experiences re-align with the
pasts, modifying the original memory structure.
Theories of human cognition linked to the sequence of learning show that the
effectiveness of CBT requires a joint effort between the companies where the trainees
work and the designers of the training tool. The companies play a role at the initial and
final stages of the learning process. At the initial stage, by motivating the trainees and
showing that the learning will be rewarded. At the final stages, by giving trainees the
opportunity to use their new skills, evaluating their performances and showing the
results of the improved skills.
The design of the instructional tools plays its role at the intermediate stages of
the learning process. This role concerns the providing of applications containing a
sound body of domain knowledge, enabling users to understand the instructional
events, catching trainees’ attention and interest, and accessing learners’ long-term
memory. The dynamic memory theory provides an approach to cope with these
instructional difficulties.
The three major reasons behind the use of this theory in this work are:
1. it represents a theory of learning for both computers and people and the CBT tool
that relies on this theory can improve itself as much as the users;
2. it provides an instructional methodology that relies on the gathering and storage of
real on-job experiences that users can retrieve and take their learning from; and
3. it supports instruction by accessing memories of real on-job past experiences that is
a natural form of human learning on the journey from novice to expert.
This work proposes an alternative instructional approach that the designers of
training tools can follow. This approach is founded on the dynamic memory theory
(Schank 1982) that is further described in the following section.
2.9 - The dynamic memory theory
Learning means the dynamic modification of memory.
Schank (1995)
The dynamic memory theory (Schank 1982) is a model of human cognition. This
model represents intelligent behaviour that involves the collection, use and

Page 24
modification of past experiences in human memory. CBT applications relying on this
model thus access a natural form of human learning where past experiences are
modelled and stored in a case repository. Users can retrieve those past experiences and
take their learning from them. (Schank 1996).
The origins of the dynamic memory theory could go back to the work of Bartlett
(1932). The author worked on the conditions of human learning and on the nature of
the errors that seemed common in memory recall, based on the meaning and
understanding of learned materials. Bartlett (1932) cited that memory could be seen as
“nothing more than a collection of anecdotes whose true accuracy and validity could be
as dubious as that of our wayward witness”.
In his experiments, Bartlett (1932) had people reading short stories and then
tested their recall by asking them to re-tell these stories at various time intervals.
These experiments led the author to believe that memory had a dynamic reconstructive
aspect, influenced by the individuals’ understanding of what they had learned. From
this study, the author elaborated a theory whose central theme was the reconstructive
nature of memory where newly acquired information is mapped onto a pre-existing
memory structure that the author called schemata. Schemata were thus described as a
dynamic structure of concepts as they change and are made more complete by the
acquisition of new information (Bartlett 1932).
Another aspect of the schemata was that they were individually unique as two
persons experiencing the same event will later reproduce similar recall “only to the
extent that their schemata are similar or at least allow for equivalent mapping” (Bartlett
1932). The author went further by citing that “when a person reproduces meaningful
material exactly as it was first experienced, this is more a happy coincidence of a valid
transformation than evidence that no transformation of the input has occurred”.
Another effort of relevance for both studies of human cognition and this thesis
was the experiment of Bower (1969). This author gave two groups of volunteers, with a
similar knowledge background, twelve lists of ten words to memorise. One group
studied the lists as an exercise of memorisation of the words. The second group was
instructed to invent stories including all the words on each list. Both groups were given
the same amount of time to perform the learning of each ten-word list. The difference

Page 25
was dramatic, since each member of the first group only recalled an average of
seventeen words while the members of the second group recalled about hundred out of
the original one hundred twenty words. The author concluded that schemata were
fragmentary and that a process of reconstruction could help recall.
Tulving (1972) also presents a theory of cognition where memory is classified as
episodic and semantic. The episodic memory relates to concrete experiences that have a
sequence in time or space. The recall of these memories can be triggered by a similar
sequence of events that composed these previous experiences. Examples of this type of
memory are found in tasks where someone has a sequence of tasks to perform until
certain equipment becomes operational. Another classic example is when going to a
restaurant, the customer expects a sequence such as finding a table, choosing from the
menu, ordering the meal, eating, paying and leaving.
Semantic memories are abstract, individual, do not follow a timed sequence and
usually involve a conceptual representation of the world. An example of semantic
memory is the use of words and language where there is no unique sequence in
choosing the words that will communicate well. Another aspect of semantic memory is
the influence of someone’s culture when receiving the words. For instance, for some
people the word snow may invoke the image of a beautiful place covered in white where
one can ski and have fun. For others snow may be associated to cold weather and to the
difficulties it imposes to people who cannot travel freely.
Perhaps the first author to provide a structure for the human cognition was
Norman (1975) who cited that knowledge is structured in the form of an interconnected
semantic net containing ideas and concepts. Learning occurs when people acquire new
information and integrate it into their existing knowledge structure. Learning thus
means an alteration in the knowledge network where either a new structure is added
or the actual structure is modified to cope with the new knowledge (Norman 1975).
Schank’s (1982) dynamic memory theory is an approach that brings together the
work of all these authors. The premise of his theory is that “remembering,
understanding, experiencing and learning cannot be separated from each other” (Schank
1982). The dynamism of memory comes from the changes it goes through as a result of
new learning or new experiences. For instance, when learning a new method of
performing an task such as inserting paper in a new printer model, old experiences in

Page 26
performing this task can be recalled and provide expectations that will drive the
learning.
The dynamic memory theory was first described by Schank (1982). Since then,
this theory has been evolving and the parts that compose its structure have also been
changing. For the sake of brevity, this work will not discuss the evolution of this theory
along with the work of Schank (1982; 1996). It will only present the core structure of
the theory that was presented in Schank (1996). This structure is described below.
• Memories Organisation Packets (MOP) – it holds both a general description of
a past experience and its organisation i.e. it gives the sequence of the events that
constitute this experience. An example of MOP given by Schank (1995) is the
experience of visiting a doctor that includes the whole sequence of events such as
booking an appointment, checking in with the nurse, reading a magazine in the
waiting room while waiting to be called, and finally seeing the doctor. A MOP is
thus a representation of a series of events that lead to the achievement of a goal (in
this case the goal is consulting a doctor).
• Scripts – are the specific situations contained in a MOP and can also be seen as “a
set of expectations about what will happen in a given situation” (Schank 1996). Each
individual event of the “visiting a doctor MOP” is considered as a Script in the
dynamic memory theory. Sometimes an MOP such as visiting a doctor and visiting
a dentist can have a series of Scripts that are common to both MOPs. Thus,
recalling one of these MOPs and its sequence of scripts can help define the
expectations for the sequence of scripts of the other.
• Meta-MOP – are structures that work as a template organising the MOP. Meta-
MOP are at the top of the hierarchy in the dynamic memory theory and deal with
general goals such as learning a skill where a series of MOPs are involved.
The value of the dynamic memory theory for CBT lies both in its being an
instructional activity that accesses a model of cognition and in its providing an
architecture that allows the development of computer applications. The dynamic
memory theory is at the origins of the CBR methodology for the development of
applications (see Chapter 4) and is also at the foundations of the case-based
instructional approach proposed in this thesis.

Page 27
The case-based instructional approach emulates a situation where learners with
a problem describe their situation to the system that retrieves a similar past experience
and presents it to the learner. Providing the descriptions that properly address the
contents of each case in the repository is thus an important requirement. However, this
requirement is not exclusive to the case-based instructional approach but is common to
any application relying on CBR. These issues that concern the requirements of general
CBR applications are discussed in Section 4.4. Further details on the learning that can
be accessed from the case-based instructional methodology are given in the next
section.
2.10 - Learning from cases
The way memory is organised has great importance for theories of learning.
Schank (1995).
Case-based instruction is a form of education where tutors, lead by the questions
of students, tell a relevant story (or a past experience) and allow students to figure out
the answer based on the example given. This educational approach is as old as human
education (Schank 1995) and was used by teachers such as Salomon, Jesus Christ,
Buddha and Plato. Case-based instruction is a natural human form of education that is
also part of common dialogue where people tell past experiences that are relevant to the
point they are trying to make in their conversation.
Teaching from cases is at the core of the work of Schank (1995; 1996; 1997) and
his research group at the Institute for the Learning Sciences (Northwestern
University). They explore a case-based teaching architecture where the cases are
represented as digitised films of experts telling stories about their experiences. Schank
(1990) cited that this architecture “exploits the basic capacity of students to learn from
stories and the basic capacity of teachers to tell stories that are indicative of their
experiences”.
The case-based teaching architecture used at the Institute for the Learning
Sciences is similar to the instructional approach in this thesis. Cases are represented
containing simulations of experts’ past experiences. Rather than the recording of an
expert telling a story, this work represents the cases in a virtual environment
simulating the location where the experience took place and the on-job actions of the

Page 28
experts. The instructional approach of this work aims at training and will be referred to
as case-based instruction.
Shank’s (1997) case-based teaching and the case-based instruction adopted in
this thesis share common aspects related to their effectiveness for instruction. For
instance, both approaches are based on the premise that learning is best taken in
functional contexts and with similarities to real situations (either described or
simulated) rather than from bare facts (see Section 5.2). Moreover, students can
acquire knowledge in real time with the problem-situation that they encounter. When
the need for learning comes, the tools are available to present a similar experience that
can provide the learning. Other instructional aspects that are common to these two
works are:
• the cases must provide instructions that are relevant to understand the domain and
the instructional goal of the application;
• the cases must be designed to avoid users’ misinterpretation of their contents;
• the cases must be attractive in order to capture and maintain users’ interest;
• the application requires a comprehensive set of cases covering the domain to be
instructed;
• the application must retrieve cases that are relevant and applicable to the learners’
request; and
• the cases must be presented in such a form that helps learners draw out useful
generalisations.
Another aspect discussed in Section 5.3 is that expertise is built upon a rich set
of experiences and learning is acquired in relation to previous knowledge. Therefore,
even if the case retrieved is not relevant to the situation users are facing, it could bring
knowledge background to users, thus speeding up future learning. The following
section discusses other advantages that come from the case-based instructional
approach, focusing on its differences to classroom training.

Page 29
2.11 - Classroom and case-based instruction
A review of the literature related to instructional approaches such as computer-
based training, classroom training and on-job supervised training leads to the
conclusion that all forms of instruction have their strengths and weaknesses. Section
2.6 shows a list of advantages of CBT as an instructional alternative. This section
discusses classroom and case-based instruction from the cognitive point of view that
each takes on providing learning.
An issue raised by Merril (1993) and Schank (1995) is that instructor led
training courses are tutor-oriented rather than trainee-oriented. Tutors try to provide
students with as much information as they can during the time available for the course.
Trainees are faced with an amount of information that, though previously organised by
the tutor, follows an instructional approach that fits the tutor’s viewpoint in instructing
the subject rather than the trainees’ experiences and difficulties in performing their
jobs.
Instructors are thus presenting information that, though relevant to the domain,
answers to questions that the trainees have not asked and perhaps providing solutions
to problems that the trainees have not had. Therefore, instead of having their own
experiences to recall, alter and learn from, students are left with the memories of the
instructors providing information on how to proceed.
Authors such as Twining (1991), Dean (1992), Weller (1994); Schank (1995) and
Ravet (1997) recognise that learning would be more effective if the trainees could make
their own mistakes, acknowledge their own failures and learn from them. Those
authors also suggest that the ideal situation is to have an instructor looking over the
trainees’ shoulder when doubts emerge. However, companies cannot afford having
neither employees making mistakes nor instructors permanently watching over each
employee.
CBT tools relying on the case-based instruction can provide help at the time the
trainees are facing a situation that requires skills they do not have. For instance, in the
domain of scaffold inspection, the case-based instruction will be most useful when users
have to inspect a component that they have never inspected before. They will then be
able to use the case-based instructional tool and retrieve a case where the component is
inspected.

Page 30
There is no evidence that the instructor led training is less appropriate or
effective than case-based instruction. It is evident, though, that trainees relying on the
former instructional approach are dealing with an instruction where someone tells
them what to do whereas the latter makes them find out what has previously been
done, reason and compare it to the situation they are facing. From the cognitive
psychology viewpoint, case-based instruction is therefore more in accordance with the
theories of cognition of Bartlett (1932), Bower (1969), Norman (1975) and Shank (1982).
2.12 - Synthesis of the chapter
CBT provides an instructional experience that aims at enhancing levels of
performance. Reasons behind the growing interest for CBT are its competitive cost in
comparison to other training alternatives, the potential of current hardware and
software to provide training applications, and the increasing use of computers for
professional and home tasks. However, to take the most from CBT, a joint effort
between the employers and the designers of the CBT applications is required.
The design of CBT is the focus of this chapter that introduces case-based
instruction. Cases are past experiences of experts performing their job and are
modelled and kept in a repository. This approach has its foundations in studies of
human cognition, where ”much of human reasoning is case-based and people constantly
experience such reminding, comparing one experience to another so as to learn from
both” (Schank 1982).
This model of human cognition also asserts that past experiences in memory are
broken down into pieces that can be recalled and altered independently. For instance,
in the domain of scaffold structures, experts usually have their own way of inspecting
health and safety regulations. Each time they are faced with a new type of scaffold,
they bring past inspections from their memory and check the components that are
relevant. If there is a new component or a different structure configuration, this new
piece of inspection can be added into the memory repository of the experts, thus
improving their experience and skills.
The memory storage of this new case can even change in the future if another
expert gives instructions regarding the inspection of new components and how the
inspections should be performed. Once the implications that the new component brings

Page 31
to the scaffold inspection are memorised, a new experience is added to memory. For
future similar inspections, the expert will be able to retrieve this experience from
memory and rely on its contents to perform new inspections.
This work models the cases in VR, simulating the physical space where the
experience and the actions of the experts took place. Users can thus retrieve the cases
and take their learning from them. Further details on the working cycle of this
instructional approach are shown in Chapter 4 that describes CBR as a technique for
the development of AI applications.

Chapter 3 - Artificial intelligence and training
3.1 - Overview
The previous chapter discusses CBT as an alternative form of training and
introduces a theory of human cognition as both an instructional approach and as a
methodology for the development of CBT applications. This model of human cognition
corresponds to an effort in artificial intelligence (AI) where past experiences of experts
performing their jobs are represented in the computer. Users can retrieve these past
experiences and learn from them.
This chapter provides an overview of the concepts of AI, describing its current
state-of-the-art in instructional applications. Previous research work in AI covering
both paradigms for knowledge representation and computer systems that embody
human instructional performances is reviewed. This chapter finishes describing
applications of AI instruction, highlighting their strengths and weaknesses.

Page 33
3.2 - The origins of AI
AI is not the study of computers, but of intelligence in thought and action. Computers are its tools,
because its theories are expressed as computer programs that enable machines to do things that
would require intelligence if done by people.
Boden (1987) - preface to first edition
AI is a research field that associates computer science and intelligent behaviour,
involving interdisciplinary areas such as cognitive psychology, paradigms for computer
knowledge representation and the design of systems that attempt to emulate aspects of
human intelligent behaviour (Barr 1981, Boden 1987; Schank 1990). Research in AI
has led to the development of computer systems using knowledge models to solve (or
provide support for) problems that otherwise would require human expertise (Rich
1983; Partridge 1990).
The origins of AI are associated with a combination of intellectual efforts in
research areas such as the evolutionary behaviour of living organisms, theories of
language, mathematical logic and studies of cognitive psychology modelling aspects of
human memory and reasoning (Barr 1981; Schank 1990; Partridge 1990). Studies in
human cognition that are at the foundations of AI are described in Chapter 2. Studies
in mathematical logic and symbolic deduction carried out by authors such as
Whitehead (1925), Church∗ (1996), Tarski∗ (1995), Turing∗ (1992) and Kleene∗ (1971)
helped the formalisation of logical reasoning and intelligence that led to the birth of AI
programming (Barr 1981).
Barr (1981) cited that Turing∗ (1992) could be considered the father of AI. His
work on mathematical theories applied to both modelling of patterns in living
organisms and non-numerical computation behaving as models of intelligence, are the
foundations of AI. The work of Turing∗ (1992) in symbolic processing and his “universal
machine” capable of executing describable algorithms has contributed to the creation of
computer machines accepting programming languages.
Right after the creation of programmable computers appeared software
packages dealing with tasks associated with human intelligence such as solving
puzzles, playing chess and translating texts from one language to another. The work of
∗ The original work of these authors could not be found and instead compilations of their work are
presented in the references of this dissertation.

Page 34
researchers such as Feigenbaum (1995), Minsky (1968), Simon (1969) and Newell
(1963) on the semantics of information processing and the programming techniques
supporting aspects of human intelligence also helped establishing the foundations of
AI.
McCarthy (1969) first introduced the term AI at a conference at Dartmouth
College in 1956 (Partridge 1990). The participants of this conference, such as Minsky
(1968) who founded the first AI lab at MIT, Shannon (1956) from Bell Labs, Newell
(1957) who became the first president of the American Association of AI, and Simon
(1969) who won a Nobel prize working at the Carnegie Mellon University, can be
considered the AI pioneers (Partridge 1990).
Forsyth (1990) cited that the 1980s were the golden age of AI, when academic
and commercial institutions from all over the world became involved with AI research
and developments. For instance, in 1981 the Japanese Ministry of Trade and Industry
announced its interest in projects involving machines capable of learning and
communicating in natural language. In 1983 the UK launched the ALVEY programme
of advanced information technology with a budget of £ 350 million, stimulating
research and development in AI in the country. It was also in the 1980s that the
Europeans invested 1600 million ECUs over a period of five years in the ESPRIT
program, promoting European co-operation and the establishment of standards for AI
developments.
Results from research in the 1980s include software tools (Shells) for the
development of intelligent systems, standard methodologies for AI developments and
the emergence (and re-emergence) of AI techniques such as neural networks, fuzzy
systems and genetic algorithms. Architectures and Shells for the development of AI
instructional applications and the CBR paradigm also emerged from the 1980s
research.
Despite the achievements of AI in the 1980s, fundamental issues related to the
complex nature of AI developments are still to be cleared (Hickman 1992; Russel, S.J.
1995; Bailey 1997). For instance, Partridge (1990) cited that the major discovery of AI
research was that the “phenomenon of intelligence is astonishing complicated to be
represented in computer machines”. Moreover, authors such as Rich (1991), Kolodner
(1993), Schank (1994), Feigenbaum (1995) and Russel, S.J. (1995) cited that results of

PhD_Thesis

Recommended

Recommended

More Related Content

Viewers also liked

Viewers also liked (9)

Similar to PhD_Thesis

Similar to PhD_Thesis (20)

PhD_Thesis