Sampson D., Karagiannidis C. & Kinshuk (2002). Personalised Learning: Educational, Technological and
Standardisation Perspective. Interactive Educational Multimedia, 4, 24-39 (ISSN 1576-4990)
Personalised Learning: Educational, Technological
and Standardisation Perspective
Demetrios Sampson and Charalampos Karagiannidis
Informatics and Telematics Institute (I.T.I.)
Centre for Research and Technology – Hellas (CE.R.T.H.)
1, Kyvernidou Street, Thessaloniki, GR-54639 Greece
Tel: +30-310-868324, 868785, 868580, ext. 105
Fax: +30-310-868324, 868785, 868580, ext. 213
E-mail: sampson@iti.gr, karagian@iti.gr
http://www.iti.gr
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Kinshuk
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Information Systems Department
Massey University
Private Bag 11-222, Palmerston North, New Zealand
Tel: +64-6-3505799, ext. 2090
Fax: +64 6 3505725
E-mail: Kinshuk@massey.ac.nz
http://fims-www.massey.ac.nz/~kinshuk
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Abstract. The e-Learning paradigm shift capitalises on two main aspect: the
elimination of the barriers of time and distance, and the personalisation of the
learners’ experience. The current trend in education and training emphasises on
identifying methods and tools for delivering just-in-time, on-demand knowledge
experiences tailored individual learners, taking into consideration their differences in
skills level, perspectives, culture and other educational contexts. This paper reviews
the shift towards personalised learning, from an educational, technological and
standardisation perspective.
Keywords: personalised learning, instruction, constructivist learning, intelligent
tutoring systems, learning specifications and standards
1
Introduction to Personalised Learning
The emergence of the Knowledge Society and the Knowledge-based Economy signify a new
era for education and training. Within this framework, knowledge and skills of citizens are
becoming increasingly important both for the economical strength and social cohesion of the
society, and the quality of citizens’ life. The structural and functional society transformations
raise the demand for major reforms in Education and Training, aiming at reducing the risks
for knowledge gaps and social exclusion.
An interesting social and scientific debate is thus continuing, on the paradigm shifts in the
way that education and training is planned, organised and delivered, as well as the definition
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Sampson D., Karagiannidis C. & Kinshuk (2002). Personalised Learning: Educational, Technological and
Standardisation Perspective. Interactive Educational Multimedia, 4, 24-39 (ISSN 1576-4990)
of concrete future objectives of educational systems. Typical demands include [Rosenberg,
2001]:
9 personalised training schemes tailored to the learner’s objectives, background, style
and needs;
9 flexible access to lifelong learning as a continual process, rather than a distinct event;
9 just-in-time training delivery;
9 new learning models for efficient integration of training on workplaces;
9 cost effective methods for meeting training needs of globally distributed workforce.
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On the other hand, the rapid evolution of Information and Communication Technologies
(ICT) provides the enabling technological tools for facilitating the implementation of the new
paradigm in education and training, referred to as e-learning. e-Learning capitalises on
advances information processing and internet technologies to provide, among others
[Sampson, 2001]:
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personalisation, where training programmes are customised to individual learners,
based on an analysis of the learners’ objectives, current status of skills/knowledge,
learning style preferences, as well as constant monitoring of progress. On-line
learning material can be, then, compiled to meet personal needs, capitalising on reusable learning objects.
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interactivity, where learners can experience active and situated learning through
simulations of real-world events and on-line collaboration with other learners and
instructors.
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media-rich content, where educational material can presented in different forms and
presentation styles, and learning material can indexed and organised in such a way
that it can be searched, identified and retrieved remotely from several different
learners providing the right material to the right person at the right time.
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just-in-time delivery, where technologies such as electronic performance support
systems can facilitate training delivery at the exact time and place that it is needed to
complete a certain task, and wearable computers can provide real-time assistance in
actual work environments.
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user-centric environments, where the learner takes responsibility for his/her own
learning, and the instructor acts as the “guide on the side”, rather than a “sage on the
stage”.
Over the past few years, there is a growing interest in e-learning both in terms of research and
scientific developments, as well as, in financial market terms. Significant resources are
allocated in collaborative R&D projects in this area, investigating a number of important
aspects, both on technological and on pedagogical advances.
With this context, the concept of personalised learning becomes increasingly popular. It
advocates that instruction should not be restricted by time, place or any other barriers, and
should be tailored to the continuously modified individual learner’s requirements, abilities,
preferences, background knowledge, interests, skills, etc. The personalised learning concept
signifies a radical departure in educational theory and technology, from “traditional”
interactive learning environments to personalised learning environments. Some of the most
prominent characteristics of this shift can be summarised as follows: (i) while “traditional”
learning environments adopt the one-to-many learning mode, personalised learning
environments are based on the one-to-one or many-to-one learning concept (i.e. one, or many
tutors for one learner); (ii) traditional learning environments usually pose a number of
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Sampson D., Karagiannidis C. & Kinshuk (2002). Personalised Learning: Educational, Technological and
Standardisation Perspective. Interactive Educational Multimedia, 4, 24-39 (ISSN 1576-4990)
constraints in relation to the learning setting; personalised learning environments, on the
other hand, facilitate learning independent of time, location, etc; (iii) traditional learning
environments are usually being designed for the “average learner”; while, in personalised
learning environments, the learning material and sequencing, learning style, learning media,
etc, depend on the individual learner’s characteristics, i.e. background, interests, skills,
preferences, etc; (iv) in traditional learning environments, the curriculum, learning units, etc,
are determined by the tutor, while in personalised learning settings, they are based on the
learner's requirements (self-directed learning).
The Educational Perspective
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This paper reviews the shift towards personalised learning, from an educational, technological
and standardisation perspective. Section 2 discusses the educational perspective of
personalised learning, whereas section 3 revises the technological perspective, emphasising
on intelligent tutoring systems, adaptive educational hypermedia, intelligent pedagogical
agents and mobile agent technologies, and section 4 presents relevant international
standarisation efforts. Section 5 offers some conclusions and issues for future consideration.
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"Since at least the 4th century BC, adapting has been viewed as a primary factor for
the success of instruction" [Corno and Snow, 1986]; "Adaptive instruction by tutoring
was the common method of education until the mid-1800s" [Reiser, 1987]
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The concept of personalised learning builds mainly on the cognitive and constructivist
theories of learning. Instructional principles of cognitive theories argue for active
involvement by learners, emphasis on the structure and organisation of knowledge, and
linking new knowledge to learner’s prior cognitive structures. Constructivist instructional
theory, on the other hand, implies that instructional designers determine which instructional
methods and strategies will help learners to actively explore topics and advance their
thinking. Learners are encouraged to develop their own understanding of knowledge. This
does not negate the role of practice and feedback, but rather allows learners more latitude in
developing knowledge structures. Both of the above theories share some commonalities,
including having learners actively involved in learning and structuring solutions so that
learners can extract the maximum amount of data [Schunk, 1996].
Additionally, personalised learning builds on several commonalities in instruction that serve
to enhance learning, which are shared between several learning theories. Most theories
postulate that learners progress through stages, or phases of learning that can be distinguished
in several ways. One scheme, for example, classifies learners in terms of progressive skill
levels: novice, advanced beginner, competent, proficient, expert [Schunk, 1996].
The basic idea behind personalised learning can be traced back to the Richard Snow and Lee
Cronbach’s 1976 research in aptitude-treatment interaction (ATI), which reflect the notion
of tailoring instruction to student characteristics [Cronbach and Snow, 1977]. Aptitudes are
student characteristics, such as abilities, attitudes, personality variables, demographic factors,
etc. Treatments, on the other hand, are forms of instruction, or sets of conditions, associated
with instruction. ATI refers to differences in student outcomes (e.g. achievement, attitudes) as
a function of the interaction (combination) of instructional conditions with student
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Sampson D., Karagiannidis C. & Kinshuk (2002). Personalised Learning: Educational, Technological and
Standardisation Perspective. Interactive Educational Multimedia, 4, 24-39 (ISSN 1576-4990)
characteristics (aptitudes). In this context, ATI research examines how individual learning
differences in aptitudes predict student responses to forms of instruction [Schunk, 1996].
In this context, several research efforts have been devoted in the identification of the
dimensions of individual differences. One of the most prominent research areas in this
context concerns the learning styles and learning differences theory, which implies that how
much individuals learn has more to do with whether the educational experience is geared
towards their particular style of learning. Learning styles are strategies, or regular mental
behaviours, habitually applied by an individual to learning, particularly deliberate educational
learning, and built on her/his underlying potentials. Learners are different from each other,
and teaching should respond by creating different instruction for different kinds of learning.
Learners also differ from each other in more subject-specific aptitudes of learning, e.g. some
being better at verbal than numerical things, others vice versa.
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Learning styles have been at the centre of controversy for several decades now, and there is
still little agreement about what learning styles really are. One of the major distinctions made
in learning styles research is the visual/auditory/kinaesthetic distinction. Researchers
generally agree that modalities of learning are distinguishable, though whether they represent
learning styles or learning differences remains to be seen. Some have included such factors as
environmental influences such as intake (i.e. food), light, or heat as components of style.
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There are numerous methodologies and tools that attempt to categorise people according to
differences in learning and cognitive styles. The most well-known of these efforts include the
Myers-Briggs Type Indicator [Keirsey, 1998]; Multiple Intelligences [Gardner, 1999],
[Jasmine, 1997]; Auditory, Visual, Tactile/Kinaesthetic Learning Styles [Sarasin, 1998];
Grasha-Riechmann student Learning Style Scales – GRLSS [Grasha, 1996]; Kolb Learning
Styles Theory [Kolb, 1985]; Felder and Silberman Index of Learning Styles [Felder, 1996];
Honey and Mumford Learning Styles [Honey and Mumford, 1992].
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The ATI field, although theoretically elegant, has experienced several criticisms for its
practical applicability. The main criticism relates to the fact that, in order for ATI to be
effectively applied, we need to be able to (i) accurately classify each learner according to a
selected taxonomy of individual differences, and (ii) determine which are the characteristics
of the learning environment that are appropriate for this category of learners.
To overcome some of the above practical limitations of ATI, an alternative theory has been
proposed, the Achievement-Treatment Instruction. While ATI stresses relatively permanent
dispositions for learning as assessed by measures of aptitudes (e.g. intelligence, personality,
cognitive styles, etc), achievement-treatment interactions represent a distinctly different
orientation, emphasising task-specific variables relating to prior achievement and subjectmatter familiarity. This approach stresses the need to consider interactions between prior
achievement and performance on the instructional task to be learned. Prior achievement can
be practically assessed rather easily and conveniently through administration of pre-tests, or
through analysis of student's previous performance on related tasks. This, this approach
eliminates many potential sources of measurement errors, which have been a problem in ATI,
since the types of abilities to be assessed would be, for the most part, clear and unambiguous
[Park, 1996].
A number of practically oriented theories have also been developed. For cognitive skills
acquisition, the most important theory, relevant to our discussion, is Cognitive
Apprenticeship Framework [Collins et al., 1989]. Cognitive Apprenticeship Framework aims
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Sampson D., Karagiannidis C. & Kinshuk (2002). Personalised Learning: Educational, Technological and
Standardisation Perspective. Interactive Educational Multimedia, 4, 24-39 (ISSN 1576-4990)
to provide adequate domain competence to the learner while focusing on cognitive skills.
According to this framework:
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The learners can study task-solving patterns of experts to develop their own cognitive
model of the domain, i.e. about the tasks, tools and solutions (modelling).
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The learners can solve tasks on their own by consulting while receiving feedback
from the experts (coaching).
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The tutoring activity is gradually reduced with the learners’ improving performances
and problem solving (fading).
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Typically, a learner starts the learning process by observing a particular task as it would be
carried out by the “master” (or subject expert) and then tries to imitate the task. If the results
of the trial are not correct, or are sub-optimal, the expert assists the learner in finding the
areas of mistakes and sub-optimalities. If necessary, the learner can again observe the
master’s approach, and, since the re-observation is a result of a query from the learner, the
depth of details grasped by the learner from the observation are increased many folds.
The Technological Perspective
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Once the learner has successfully imitated the task, the expert provides opportunity to repeat
the task in different scenarios so that the learner can get mastery in the task. The repetition
process also facilitates the abstraction of the concepts related to the task and helps the learner
to apply the abstracted concepts in situated scenarios.
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This section reviews the technological state of the art with respect to personalised learning.
The section covers intelligent learning environments (ILEs), which are capable of
automatically adapting to the individual learner, and therefore constitute the most promising
technological approach towards the realisation of the personalised learning concept.
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Learning environments need to make several communication decisions: what content to
communicate, when, how, etc. In the context of this paper, a learning environment is called
intelligent in a measure to the extent that these decisions are made dynamically, at run- or
use-time, based on an analysis of the learning context, which is defined by the learner
characteristics, the type of educational material being exchanged, etc. Therefore, the
definition adopted in the context of this report with respect to intelligent learning
environments (ILEs) is as follows: an intelligent learning environment is capable of
automatically, dynamically, and continuously adapting to the learning context, which is
defined by the learner characteristics, the type of educational material being exchanged, etc.
ILEs have been shown to be highly effective at increasing students' performance and
motivation. For over 26 years, peer-reviewed studies have reported large, consistent gains
when quality courseware is used to enhance or replace traditional instruction. In studies
where time-to-learn is held constant, we typically observe 15% increases in student outcome
performance (instructional effectiveness). When student outcome performance requirements
are held constant, we typically observe 24% reductions in time-to-learn (instructional
efficiency). More recently, studies have reported even larger instructional effects - 34%
increases in outcome performance - and efficiencies - 55% learning time reduction - for ILE,
as compared to traditional instruction. Among effective, proven approaches to raising student
achievement, (intelligent) automated instruction is the cheapest and most practical by a wide
margin.
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Sampson D., Karagiannidis C. & Kinshuk (2002). Personalised Learning: Educational, Technological and
Standardisation Perspective. Interactive Educational Multimedia, 4, 24-39 (ISSN 1576-4990)
3.1
Intelligent Tutoring Systems
In 1982, Sleeman and Brown reviewed the state of the art in computer aided instruction and
first coined the term Intelligent Tutoring Systems (ITS) to describe tutoring systems that were
"computer-based (i) problem-solving monitors, (ii) coaches, (iii) laboratory instructors, and
(iv) consultants" [Shute and Psotka, 1995], [Sleeman and Brown, 1982]. Although R&D
efforts in the ITS area are characterised by great diversity, the essential modules that are
required for ITS are widely agreed to include mainly the student model, the domain model,
the tutoring model and the interaction model, which are briefly described below.
Student Model
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Student modelling remains at the core of ITS research. What distinguishes ITS from CAI is,
in fact, the goal of being able to respond to the individual student's learning style to deliver
customised instruction. The student model stores information that is specific to each
individual learner. At a minimum, such a model tracks how well a student is performing on
the material being taught. A possible addition to this is to also record misconceptions. Since
the purpose of the student model is to provide data for the pedagogical module of the system,
all of the information gathered should be able to be used by the tutor.
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Domain Model
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The key feature that distinguishes a knowledge communication system from standard ITS on
the Domain Expertise dimension is that the representation of the subject matter is not merely
a set of static frames, but actually is a dynamic model of the domain knowledge and a set of
rules by which the system can "reason." These systems have their roots in expert systems
research (such as medical diagnostic or electronic troubleshooting systems) and have the
ability to generate multiple correct sets of solutions, rather than a single idealised expert
solution. This component contains information the tutor is teaching, and is the most important
since without it, there would be nothing to teach the student. Generally, it requires significant
knowledge engineering to represent a domain so that other parts of the tutor can access it.
One related research issue is how to represent knowledge so that it easily scales up to larger
domains. Another open question is how to represent domain knowledge other than facts and
procedures, such as concepts and mental models.
Tutoring Model
This component provides a model of the teaching process. For example, information about
when to review, when to present a new topic, and which topic to present is controlled by the
pedagogical module. As mentioned earlier, the student model is used as input to this
component, so the pedagogical decisions reflect the differing needs of each student. ITS must
model the student's current knowledge and support the transition to a new knowledge state.
This requires that ITS alternate between diagnostic and didactic support.
Diagnosis means that an ITS infers information about the learner's state on three levels: At
the behavioural level, ignoring the learner's knowledge and focusing only on the observable
behaviour. At the epistemic level, dealing with the learner's knowledge state and attempting
to infer that state based on observed behaviour. At the individual level, covering such areas as
the learner's personality, motivational style, self-concept in relation to the domain in question,
and conceptions the learner has of the ITS.
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Sampson D., Karagiannidis C. & Kinshuk (2002). Personalised Learning: Educational, Technological and
Standardisation Perspective. Interactive Educational Multimedia, 4, 24-39 (ISSN 1576-4990)
The second facet of pedagogical expertise is didactic support, the "delivery" aspect of
teaching. Generally, ITS have concentrated on the modelling and manipulation of the content
or domain, with little attention being paid to didactics.
Interface Model
Interactions with the learner, including the dialogue and the screen layouts, are controlled by
this component. That is, the interface model is concerned with the presentation of the
educational material to the student in the most effective way. Research has been carried out in
two dimensions: multimedia content and user exploration.
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The use of multimedia objects in ITSs can enhance their efficacy to a great extent. However,
just the collection of multimedia objects does not guarantee proper learning [Rogers et al.,
1995]. Another important aspect is the proper interaction of the learner with the interface
components, specially when learning is recognised as a complex activity (or process)
combining various factors such as information retrieval, navigation, and memorisation
[Dillon, 1996]. One significant development in this area is the Multiple Representation (MR)
approach [Kinshuk et al., 1999] that has been developed to present multimedia objects (such
as audio, pictures and animations) into a multimedia interface world where the relationships
of the objects to the world are governed by the educational framework. Learners are provided
with various forms of interactivity to suit the pedagogical goals of the intelligent educational
systems. This approach ensures the suitable domain content presentation by guiding the
multimedia objects selection, navigational objects selection, and integration of multimedia
objects to suit different learner needs.
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Another aspect for interface design is the consideration of user’s capability and preferences to
explore the learning environment. Exploration of domain concepts/knowledge is treated as an
effective technique for constructivist learning. This exploratory learning is often
accompanied by cognitive efforts to develop and apply the domain concepts/knowledge,
which efforts would enhance learning effects. However, it is not so easy for learners to get
good learning results just because they had possibility to explore the domain content. The
cognitive efforts may cause cognitive overload. Some intelligent/adaptive support is therefore
necessary. Exploration Space Control (ESC) methodology [Kashihara et al., 2000] has been
developed for supporting exploratory learning which attempts to limit learning space, called
exploration space, to adequately control the cognitive load the learners would face in their
exploration process. In ESC, the extent of the exploration space is controlled according not
only to the domain complexity, but also to the learners’ competence, understanding levels,
experiences, characteristics, etc. The control is done by restricting exploration tools provided
in user interface, tailoring information to be presented, recommending a few among a number
of choices, etc.
The interface allows communication between the student and the other aspects of the ITS.
The goal of knowledge communication requires that the interface contain a discourse model
to resolve ambiguities in the student responses. Since the learner is most likely to provide
incomplete or contradictory responses when stymied, providing a properly supportive
response that can advance the diagnostic process is important. This helps the ITS avoid
redundant presentations and enhances instruction.
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Sampson D., Karagiannidis C. & Kinshuk (2002). Personalised Learning: Educational, Technological and
Standardisation Perspective. Interactive Educational Multimedia, 4, 24-39 (ISSN 1576-4990)
3.2
Adaptive Educational Hypermedia
Adaptive Educational Hypermedia (AEH) is a relatively new direction of research within the
area of adaptive and user model-based educational applications [Brusilovsky et al, 1998].
AEH systems build a model of the individual user/learner, and apply it for adaptation to that
user. In this sense, they can be considered as a sub-domain of ITS. Their distinctive
characteristic is that the educational material is represented in a hyperspace form, and
adaptation is applied for re-structuring this hyperspace, or for modifying the links between
the different nodes of the hyperspace, or for modifying the content of each node in the
hyperspace, etc. For example, an AEH system may adapt the content of a hypermedia page to
the user's knowledge and goals, or to suggest the most relevant links to follow. AEH systems
are commonly used when the hyperspace is reasonably large and where a hypermedia
application is expected to be used by individuals with different goals, knowledge and
backgrounds [Brusilovsky, 1998].
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Existing educational hypermedia systems have relatively small hyperspaces representing a
particular course or section of learning material on a particular subject. The goal of the
student is usually to learn all this material, or a reasonable part of it. The hypermedia form
supports student-driven acquisition of the learning material. The most important feature in
educational hypermedia is user knowledge of the subject being taught. Adaptive hypermedia
techniques can be useful to solve a number of the problems associated with the use of
educational hypermedia. Firstly, the knowledge of different users can vary greatly and the
knowledge of a particular user can grow quite fast. The same page can be unclear for a novice
and, at the same time, trivial, or boring, for an advanced learner. Second, novices enter the
hyperspace of educational material knowing almost nothing about the subject. Most of the
offered links from any node lead to material which is completely new for them. They need
navigational help to find their way through the hyperspace. Without such help, they can "get
lost" even in reasonably small hyperspaces, or use very inefficient browsing strategies.
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According to [Brusilovsky, 1998], Adaptive Hypermedia (AH) systems, in general, and AEH,
in particular, can be categorised with respect to several dimensions. The first question to pose
about a particular AEH system is: what aspects of the student working with the system can be
taken into account when providing adaptation? To which features - that can be different for
different students (and may be different for the same student at a different time) - can the
system adapt? Generally, there are many features related to the current context of the student
work and to the student as an individual which can be taken into account by an AEH system.
The features that are used by existing systems are: student's goals, knowledge, background,
hyperspace experience, and preferences. Student's knowledge, which is most commonly used
in educational systems, is usually represented by an overlay model based on the structural
model of the subject domain, which, in turn, is usually represented as a network of domain
concepts. Sometimes, a simpler stereotype student model is used, which distinguishes several
typical "stereotype students". The student's current goal is usually modelled in a similar
manner. That is, the system supports a set of possible student goals, and an overlay student
goal model is used to predict the current goal. Student's background and hyperspace
experience is also usually modelled through overlay models, while student's preferences are
usually either specified by the student, or are deduced by the accumulation of several student
models in a group student model.
Another important question concerning AEH systems is: what can be adapted by the system?
Which features of the system can differ for different students? What is the space of possible
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Sampson D., Karagiannidis C. & Kinshuk (2002). Personalised Learning: Educational, Technological and
Standardisation Perspective. Interactive Educational Multimedia, 4, 24-39 (ISSN 1576-4990)
adaptations? The adaptations in AEH systems may include the content of the hypermedia
pages (adaptive presentation), as well as the links included in each page (adaptive navigation
support). The former case can be further decomposed into adaptive multimedia presentation,
and adaptive text presentation, which is most commonly used. The latter case includes direct
guidance (providing the "next" node to follow), adaptive sorting of links, adaptive hiding of
links, adaptive annotation of links, and/or map adaptation. The latter adaptation techniques
can be applied to several types of links, including local non-contextual links, contextual links
or "real hypertext" links, links from index and content pages, and links on local maps or
global hyperspace maps.
3.3
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The last broad categorisation of AEH systems concerns how adaptation can help, i.e. the
methods and techniques of adaptation, for content adaptation and adaptive navigation
support. Concerning the methods for content adaptation, the most popular one is to hide from
the student some parts of the information about a particular subject which are not relevant to
the student's level of knowledge about this concept. Following another approach, which has
been termed prerequisite explanation, before presenting an explanation of a concept, the
system may insert explanations of all its prerequisite concepts which are not sufficiently
known to the student. Alternatively, following the explanation variants method, the system
may store several variants for some part of the page content, and the student gets the variant
that corresponds to his/her student model; or the system may sort the fragments of
information about a concept, and present the information that is most relevant to the student's
knowledge. Concerning the techniques for content adaptation, one can distinguish between
the conditional text technique, where all possible information about a particular concept is
divided into several chunks of text, each one associated with a condition concerning the
student's knowledge of the domain, and only the chunks for which the condition is true are
presented to the student; or the stretch-text technique, where particular "hot-words" are
associated with some text, which is "collapsed", or "un-collapsed" according to the student's
knowledge. The most powerful adaptation technique for content adaptation is frame-based
adaptation, where all the information about a particular concept is represented in form of a
frame, and special presentation rules are used to select which information within a frame will
be presented, according to the student's knowledge. Finally, concerning the methods for
adaptive navigation support, we can distinguish between global guidance, local guidance,
local orientation support, global orientation support and management of personalised views.
Intelligent Pedagogical Agents
Intelligent agents have been characterised in a large range of definitions. In particular, there is
no real agreement on what an agent is. Agents’ abilities vary significantly, depending on its
roles, capabilities, and environments. In order to describe these abilities, different notions of
agents have been introduced. Intelligent agents are introduced by most of the researchers with
four major concepts defining their behaviour: (i) autonomy, (ii) responsiveness or
reactiveness, (iii) pro-activeness and (iv) social ability. There is also a strong notion on the
characteristics of agents, which refer to adaptiveness, pro-activity and intentionality. There
are also various taxonomies created for agents, e.g. collaborative, interface, mobile,
information, reactive, hybrid, and smart agents. In this context, intelligent agents have been
associated with a variety of functions, for example, personal assistants, information
managers, information seekers, planning agents, co-ordination agents or collaborative
schedules, user representatives, and so forth. Their application in the educational field is
mostly as personal assistants, user guides, alternative help systems, dynamic distributed
system architectures, human-system mediators, and so forth [Aroyo and Kommers, 1999].
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Standardisation Perspective. Interactive Educational Multimedia, 4, 24-39 (ISSN 1576-4990)
Agent technology appears to be a promising approach to address the challenges of modern
day educational environments, influenced enormously by advanced information and Internet
technologies. All these changes imply that on the one hand, increasingly complex and
dynamic educational infrastructures need to be managed more efficiently and, on the other
hand, new types of educational services and mechanisms need to be developed and provided.
It is in particular that such services need to satisfy a diverse range of requirements in addition
to personalisation, including, for example, support for user mobility, support for users while
coping with new types of technologies, effectiveness, information support, and so forth.
Agents appear to support in a more efficient way those requirements in comparison to other
already existing technologies. Besides the ability to process autonomy and co-operation
among themselves, agents possess the capabilities for issues such as security, both online and
offline service providing, and so forth [Muller, 1996].
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Advances in user interface and autonomous agent technology make it possible to a new type
of intelligent computer tutors: animated pedagogical agents that can engage human learners
in natural instructional dialogs. These agents have a number of novel and interesting features.
They are able to respond and adapt to dynamic environments, allowing them to provide
opportunistic instruction in dynamic environments and support learning-while-doing. They
have animated personas that permit them to demonstrate how to perform task, and engage in
face-to-face dialogs incorporating facial expressions and gestures. They can interact with
multiple students and agents at once, in order to facilitate collaborative and team learning.
They can learn from human instructors, and then teach what they have learned to human
students.
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Although pedagogical agents build upon previous research on intelligent learning
environments, they bring a fresh perspective to the problem of facilitating on-line learning,
and address issues that previous intelligent tutoring work has largely ignored. Because
pedagogical agents are autonomous agents, they inherit many of the same concerns that
autonomous agents in general must address. It has been argued that practical autonomous
agents must in general manage complexity. They must exhibit robust behaviour in rich,
unpredictable environments; they must co-ordinate their behaviour with that of other agents,
and must manage their own behaviour in a coherent fashion, arbitrating between alternative
actions and responding to a multitude of environmental stimuli. In the case of pedagogical
agents, their environment includes both the students and the learning context in which the
agents are situated. Student behaviour is by nature unpredictable, since students may exhibit a
variety of aptitudes, levels of proficiency, and learning styles.
The goal of this line of research is to create agents that have life-like personas that are able to
interact with students on an ongoing basis. Animated personas can cause learners to feel that
on-line educational material is less difficult. They can increase student motivation and
attention. But most fundamentally, animated pedagogical agents make it possible to more
accurately model the kinds of dialogs and interactions that occur during apprenticeship
learning. Factors such as gaze, eye contact, body language, and emotional expression can be
modelled and exploited for instructional purposes [Shaw et al, 1999b].
The advantages of intelligent agents can be summarised as follows [Andre et al., 1997],
[Johnson et al, 2000]:
•
they can attract the student's focus and attention, and they can guide the user through
a presentation; therefore, they increase the computer's capability to engage and
motivate learners;
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•
they can realise new presentation means, such as two-handed pointing; they can
convey additional conversational and emotional signals via facial expressions and
body movements, i.e. they increase the bandwidth of communication between learners
and computers; and
•
they can demonstrate physical tasks, such as operation and repair of equipment;
demonstrating a task may be far more effective than trying to describe how to perform
it, especially when the task involves spatial motor skills.
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In this context, animated pedagogical agents can yield important educational benefits. First,
animated pedagogical agents can improve students' performance. Second, agents that provide
multiple levels of advice and employ multiple modalities produce the best problem-solving
performance. The largest formal empirical study of an IPA to date has been conducted with
the HERMAN-THE-BUG agent (see below). The study involved one hundred middle school
students, each one interacting with one of several versions of the agent, varying along two
dimensions. First, different versions employed different modalities: some provided only
visual advice, some only verbal advice, and some provided combinations of the two. Second,
different versions provided different levels of advice: some agents provided only high-level
(principle-based) advice, others provided low-level (task-specific) advice, and some were
completely mute. During the interactions, the learning environment logged all problemsolving activities, and the students were given rigorous pre-tests and post-tests. The results of
this study were three-fold:
baseline result: students interacting with learning environments with an animated IPA
show statistically significant increases from pre-tests to post-tests; some critics have
suggested that animated IPA could distract students and hence prevent learning; this
finding establishes that a well-designed agent in a well-designed learning environment
can create successful learning experiences;
•
multi-level, multi-modality effects: animated IPA that provide multiple levels of
advice combining multiple modalities yield greater improvements in problem-solving
than less expressive agents; this finding indicates that there may be important learning
benefits from introducing animated IPA that employ both visual and auditory
modalities, to give both "practical" and "theoretical" Advice;
•
complexity benefits: the benefits of IPA increase with problem-solving complexity; as
students are faced with more complex problems, the positive effects of IPA on
problem solving are more pronounced; this finding suggests that IPA may be
particularly effective in helping students solve complex technical problems.
•
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persona effect: the very presence of a lifelike character in an interactive learning
environment can have a strong positive effect on learner's perception of their learning
experience.
The study also demonstrated an important synergistic effect of multiple types of explanatory
behaviours on student's perception of agents: agents that are more expressive (both in modes
of communication and in levels of advice) are perceived as having greater utility and
communicating with greater clarity.
In short, animated pedagogical agents offer great promise for knowledge-based learning
environments. In addition to coupling feedback capabilities with a strong visual presence,
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these agents play a critical role in motivating students. The extent to which they exhibit lifelike behaviours strongly increases their motivational impact.
3.4
Mobile Agents
The benefits of intelligent agents are not limited to only pedagogical aspects. Agent solutions
are being also applied to other aspects of learning system to improve personalised learning.
The most recent work on agents has brought forward a new “species” of agents: mobile
agents.
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Mobile agents have the ability to move from one computer to another. Mobile agents
technology in recent years has been an area of interest for many big research groups, e.g.
Telescript [White, 1996], AgentTCL [Gray, 1997], Aglet system [Chang and Lang, 1996],
Bee-gent and Plangent [Bee-gent, 2001], Hive [Minar, 2000]. There are specific benefits that
mobile agents provide to learning systems compared to static agents, particularly in the web
environment.
In web-based learning environments, mobile agents can be used to pre-fetch the domain
content that the student would most likely request in near future, based on the monitoring
of student’s previous interaction. Depending on the state of the network, an immediate
request or a reservation can be made with the help of mobile agent. In this way, end-toend quality of the service can be improved for the delivery of distributed educational
content, particularly when large multimedia files are involved. Thus mobile agents
technology can avoid unnecessary networking delays, cope the bandwidth limitation and
adapt the representations to students.
•
With the continuous increase in the number of mobile users, the access to web-based
learning environments is increasing from portable-computing devices, such as laptops,
palmtops, and electronic books. These devices can have unreliable, low-bandwidth, highlatency telephone or wireless network connections. Mobile agents are an essential tool for
increasing efficiency of such access.
•
Mobile agents offer learning systems developers a new programming paradigm with
higher-level abstraction and unified “process” and “object”. In terms of scalability of
system and easy authoring, these features of mobile agents offer a flexible and effective
philosophy on learning environment development, design, and scalability.
•
Web-based learning environments generally share resources on different systems. Both
the computers and networks on which such systems are built tend to be heterogeneous in
character. As mobile agent systems are generally computer and network independent,
they can provide excellent support for distributed systems and resources sharing.
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4
The Standardisation Perspective
During the past few years, a number of international efforts have been initiated for defining
specifications and standards which can facilitate re-usability in learning technologies. The
main initiatives in the area are the IEEE LTSC (Learning Technologies Standards
Committee, http://ltsc.ieee.org), the IMS (Instructional Management Systems) Global
Learning Consortium Inc (http://www.imsproject.org), the European CEN/ISSS Learning
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Technologies Workshop (http://www.cenorm.be/isss/Workshop/lt), and the US ADLnet
(Advanced Distributed Learning Network, http://www.adlnet.org).
These efforts have already resulted in a number of specifications for e-Learning applications
and services. However, the current versions of these specifications do not support
personalised learning.
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In particular, today we can describe in a common way learning objects, e.g. through the IEEE
Learning Objects Meta-Data (LOM) specification. We can also describe learner
characteristics in a common format, e.g. through the IMS Learner Information Profile (LIP)
specification. Moreover, we can describe learning packages (i.e. collections of learning
objects) in a common format, though the IMS Content Packaging (CP) specification.
However, the current version of this specification facilitates only the definition of simple,
table of contents-like structures. As a result, an e-learning system importing a content
package can only present the same information to all learners, thus personalised, on-demand
learning cannot be supported.
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In this context, a number of international efforts have been initiated for the extension of the
current versions of these specifications, to allow the definition of rules which determine
which (different) parts of learning packages should be selected for different learner
categories. One such approach is carried out in the context of the European KOD
“Knowledge on Demand” project.
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The KOD project works on an extension of the CP specification (the knowledge packaging
format), so that it can enable the definition of adaptation rules, which specify which parts a
learning package should be selected for different learner categories. As a result, the KOD elearning system (or any e-learning system which is compliant with the KOD knowledge
packaging format) can import knowledge packages, disaggregate them, interpret the rules
included in them, and present different “knowledge routes” to different learners, according to
their individual profiles. Moreover, since the “adaptation logic” (adaptation rules) behind
adaptive educational content are represented in a common format, adaptive educational
content can be easily interchanged and re-used, thus re-usability for personalised, on-demand
access can be supported.
Figure 1 –Through the current version of the CP specification, all learners receive the same
learning material
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Figure 2 –Through the KOD knowledge packaging format, different learners receive different
learning material, adapted to their profile
Conclusions
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Another issue in the standardisation of learning material is to provide an efficient way to
search and browse various learning objects according to individual requirements. A webcourse search engine has been developed [Gong and Kinshuk, submitted], which is a userfriendly, efficient and accurate assistant for the learners to get what they want from the vast
ocean of learning objects being developed all over the world. The system uses Metadata
specifications to record and index various learning objects, and lets the searchers and the
resources “communicate” with each other. Following the Metadata specifications, the system
collects exact information about educational resources, provides adequate search parameters
for search, and returns evaluative results. With intuitive interfaces, the learners can find the
appropriate learning objects to suit their needs.
This paper reviews the shift towards personalised learning, from an educational,
technological and standardisation perspective. Academia has seen a number of changes in the
instructional process, including a shift of focus from instruction dominated to constructed
dominated learning, system design from Socratic dialogue to student-centred environments,
technology advancements from page turners to sophisticated web-based adaptive learning
environments. All these changes suggest the increasing support to individual students. The
increasing demand for just-in-time learning and learning-on-demand in work environments,
and the increasing demand for life-long learning situations, have given rise to those learning
environments where traditional human experts are not available on the spot. The need for
personalised learning systems to “fill the gap” is therefore to stay and further research in this
direction is more than welcomed [Sampson et al., 2002].
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Acknowledgements
Part of the work reported in this paper was carried out in the context of the KOD “Knowledge
on Demand” project, which is partially funded by the European Commission, through the
Information Society Technologies (IST) Programme (Contract No IST-1999-12503).
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