Adaptive Learning Environments and e-Learning
Standards*
Alexandros Paramythis and Susanne Loidl-Reisinger
Johannes Kepler University, Linz, Austria
alpar@fim.uni-linz.ac.at
loidl@fim.uni-linz.ac.at
Abstract: This paper examines the sufficiency of existing e-Learning standards for facilitating and supporting the
introduction of adaptive techniques in computer-based learning systems. To that end, the main representational
and operational requirements of adaptive learning environments are examined and contrasted against current e-
Learning standards. The motivation behind this preliminary analysis is attainment of: interoperability between
adaptive learning systems; reuse of adaptive learning materials; and, the facilitation of adaptively supported,
distributed learning activities.
Keywords: adaptive, e-Learning, standards, personalisation, interoperability
*This is an extended version of a paper presented in the 2
nd
European Conference on e-Learning (ECEL 2003), November
2003.
1. Introduction
In support of this argument, this paper
explores the concept of adaptivity in the
context of computational learning
environments. Furthermore, it attempts a high-
level assessment of the sufficiency of existing
e-Learning standards for driving the
convergence of the two strands of systems
outlined above. The intention is to provide a
preliminary assessment of the adequacy of
existing e-Learning standards for specifying,
and guiding the implementation of, adaptive
behaviour within learning environments.
In recent years we have witnessed an
increasingly heightened awareness of the
potential benefits of adaptivity in e-Learning.
This has been mainly driven by the realization
that the ideal of individualized learning (i.e.,
learning tailored to the specific requirements
and preferences of the individual) cannot be
achieved, especially at a “massive” scale,
using traditional approaches. Factors that
further contribute in this direction include: the
diversity in the “target” population participating
in learning activities (intensified by the gradual
attainment of life-long learning practices); the
diversity in the access media and modalities
that one can effectively utilize today in order to
access, manipulate, or collaborate on,
educational content or learning activities,
alongside with a diversity in the context of use
of such technologies; the anticipated
proliferation of free educational content, which
will need to be “harvested” in order to
“assemble” learning objects, spaces and
activities; etc.
The motivation for seeking standardization in
adaptive e-Learning is directly linked to cost
factors related to the development of ALEs and
adaptive courses thereof (e.g., higher initial
investment, higher maintenance costs) and the
low level of reuse possible in the field today
(due to proprietary models and representations
of system knowledge, adaptation logic, etc.)
(Conlan et al., 2002a). Our rationale can be
briefly outlined as follows:
To protect the high investment
necessary for the development of
adaptive learning material, one has to
ensure that the latter is not bound by
proprietary standards and formats. This
is a main prerequisite for enabling the
transfer of such material to new
environments.
There exist currently several systems which
employ adaptive techniques to enable or
facilitate different aspects of learning
(Brusilovsky, 1999). An important observation
one can make going over the related literature
is that a dichotomy appears between typically
commercial, standards-based e-Learning
systems on the one hand, and (typically
research prototypes of) adaptive learning
environments (ALEs) on the other, with little, if
any, standards compliance. It is argued that
this dichotomy is, in part, due to the lack of
sufficient support for adaptive behaviour in
existing e-Learning standards.
Taking this concept one step further,
one may need to ensure that different
learning environments can interoperate
in the context of adaptation. A typical
exemplary setup might involve one
environment holding an individual user’s
model and interaction / learning history,
and another acting as a content
repository.
http://www.ejel.org
©Academic Conferences Limited
Electronic Journal on e-Learning Volume 2 Issue 1 (February 2004) 181-194
182
At the same level, but worth individual
mention, is the case of content discovery
and aggregation. This introduces an
entirely new dimension, as content
“characterization” through metadata
provided by its initial author / designer,
can now be augmented with aspects
relating to the use of that content by
individuals and groups, and collected as
part of the adaptation “cycle”.
Furthermore, by combining findings from
several compatible systems, which
serve the same adaptive course to a
multitude of users, it would be possible
to make improvements to the course
itself. These could be effected wither in
a fully automated way, or in a “semi-
automated” one, in cases where it would
be preferable that no modifications are
made to courses without prior approval
by human experts.
Departing from the “traditional” treatment
of the learner as a solitary, mostly
passive receptor of information, one
would also need to account for adaptive
support in the context of collaborative
learning activities. Such activities may
be carried out from within the same or
“compatible” learning environments,
which, in turn, points to a different level
of interoperation requirements between
such environments.
The rest of the paper is structured as follows.
The next section, “Background”, outlines the
main concepts of adaptive personalization in
learning environments. The following section,
“Adaptation and e-Learning standards”, starts
with a brief account of the landscape of related
e-Learning standards, and goes on to discuss
how these can accommodate adaptivity, and
where extensions or entirely new standards
are required. Finally, the paper is concluded
with an account of the main points put forward
and their implications.
2. Background
2.1 What is adaptive learning?
The term “adaptive” is associated with a quite
range of diverse system characteristics and
capabilities in the e-Learning industry, thus
making it is necessary to qualify the qualities
one attributes to a system when using the
term. In the context of this paper, a learning
environment is considered adaptive if it is
capable of: monitoring the activities of its
users; interpreting these on the basis of
domain-specific models; inferring user
requirements and preferences out of the
interpreted activities, appropriately
representing these in associated models; and,
finally, acting upon the available knowledge on
its users and the subject matter at hand, to
dynamically facilitate the learning process. The
preceding informal definition should
differentiate the concept of adaptivity from
those of tailorability / configurability, flexibility /
extensibility, or the mere support for
intelligently mapping between available media
/ formats and the characteristics of access
devices. Please note that in several places in
this paper, the term “adaptation” is used as a
synonym for “adaptivity”.
Adaptive behaviour on the part of a learning
environment can have numerous
manifestations. Instead of attempting to
exhaustively enumerate all of these, we will
provide a high-level categorization, which
suffices for the analysis in the following
section. The broad and partially overlapping
categories that we will be referring to are:
adaptive interaction, adaptive course delivery,
content discovery and assembly, and, finally,
adaptive collaboration support. Each of these
categories is briefly qualified below, followed
by an overview of the models and processes
that are typically instated in adaptive e-
Learning systems.
2.2 Categories of adaptation in
learning environments
The first category, Adaptive Interaction, refers
to adaptations that take place at the system’s
interface and are intended to facilitate or
support the user’s interaction with the system,
without, however, modifying in any way the
learning “content” itself. Examples of
adaptations at this level include: the
employment of alternative graphical or colour
schemes, font sizes, etc., to accommodate
user preferences, requirements or (dis-)
abilities at the lexical (or physical) level of
interaction; the reorganization or restructuring
of interactive tasks at the syntactic level of
interaction; or the adoption of alternative
interaction metaphors at the semantic level of
interaction. Although interface adaptations can
be thought of as generally independent from
the material or “content” delivered through a
learning environment, this is not usually the
case with learning activities - the major
differentiating factor being the emphasis on
ensuring and optimising “content” attainment in
the former case, versus the emphasis on
supporting a process in the case of activities.
The dependency of learning activities on
interface adaptations is a natural consequence
of the fact that the interface encapsulates the
http://www.ejel.org
©Academic Conferences Limited
Alexandros Paramythis & Susanne Loidl-Reisinger
183
very “tools” for carrying out an activity, be it
interpersonal communication, collaboration
towards problem-solving, etc.
The second category, Adaptive Course
Delivery, constitutes the most common and
widely used collection of adaptation techniques
applied in learning environments today. In
particular, the term is used to refer to
adaptations that are intended to tailor a course
(or, in some cases, a series of courses) to the
individual learner. The intention is to optimise
the “fit” between course contents and user
characteristics / requirements, so that the
“optimal” learning result is obtained, while, in
concert, the time and interactions expended on
a course are brought to a “minimum”. In
addition to time and effort economy, major
factors behind the adoption of adaptive
techniques in this context include:
compensating for the lack of a human tutor
(who is capable of assessing learner capacity,
goals, etc., and advising on individualized
“curricula”), improving subjective evaluation of
courses by learners, etc. The most typical
examples of adaptations in this category are:
dynamic course (re-)structuring; adaptive
navigation support; and, adaptive selection of
alternative (fragments of) course material
(Brusilovsky, 2001).
The third category, Content Discovery and
Assembly, refers to the application of adaptive
techniques in the discovery and assembly of
learning material / “content” from potentially
distributed sources / repositories. The adaptive
component of this process lies with the
utilization of adaptation-oriented models and
knowledge about users typically derived from
monitoring, both of which are not available to
non-adaptive systems that engage in the same
process. At this point, we would like to make
an explicit distinction between the perspective
of the individual learner wishing to locate
relevant material within a (possibly
constrained) corpus, and the perspective of the
author or “aggregator” who undertakes the
task of putting together a course from existing
materials and targeting a specific audience –
or, seen differently, collecting and tailoring
material for accommodating specific user /
context characteristics. Although adaptation
may very well be suitable in both perspectives,
in the context of this paper we will be focusing
on the first one, i.e., the assembly and
contextualisation of material that is intended
for an individual learner. This allows us to
consider the more complex scenaria that
emerge when one’s personal learning and
interaction history can be utilized to infer
criteria for content selection and processing.
The fourth and final category, Adaptive
Collaboration Support, is intended to capture
adaptive support in learning processes that
involve communication between multiple
persons (and, therefore, social interaction),
and, potentially, collaboration towards common
objectives. This is an important dimension to
be considered as we are moving away from
“isolationist” approaches to learning, which are
at odds with what modern learning theory
increasingly emphasizes: the importance of
collaboration, cooperative learning,
communities of learners, social negotiation,
and apprenticeship in learning (Wiley, 2003).
Adaptive techniques can be used in this
direction to facilitate the communication /
collaboration process, ensure a good match
between collaborators, etc.
2.3 Models in adaptive learning
environments
All of the above categories of adaptation in
learning environments are based on a rather
well-established set of models and processes.
The rest of this section presents brief accounts
of some of the models that one typically
encounters in ALEs.
The domain model: Since most current
ALEs are focused on adaptive course
delivery, the domain-, or application-
model is usually a representation of the
course being offered. However, in those
cases where more general learning
activities are supported, the domain
model may additionally contain
information about workflows,
participants, roles, etc. The most
important aspect of adaptive-course
models is that they are usually based on
the identification of relationships
between course elements, which are
subsequently used to decide upon
adaptations (Brusilovsky, 2003).
The learner model: The term learner
model is used to refer to special cases
of user models, tailored for the domain
of learning. The specific approach to
modeling may vary between adaptive
learning environments. Nevertheless,
there is at least one characteristic
shared by practically all existing
systems: the model can be updated at
interaction time, to incorporate elements
or traces of the user’s interaction history.
In other words, the learner model in
ALEs, not only encapsulates general
http://www.ejel.org
©Academic Conferences Limited
Electronic Journal on e-Learning Volume 2 Issue 1 (February 2004) 181-194
184
information about the user (e.g.,
demographics, previous achievements,
etc.), but also maintains a “live” account
of the user’s actions within the system.
Group models: Similarly to user / learner
models, group models seek to capture
the characteristics of groups of users /
learners. The main differentiating factors
between the two are: (a) group models
are typically assembled dynamically,
rather that “filled in” dynamically, and (b)
group models are based on the
identification of groups of learners that
share common characteristics,
behaviour, etc. As such, groups model
are used to determine and “describe”
what makes learners “similar” or not, as
well as whether any two learners can
belong to the same group. This dynamic
approach to identifying groups and user
participation in them is already used
widely in collaborative filtering and
product recommenders, and bears great
promise in the context of e-Learning.
The adaptation model: This model
incorporates the adaptive theory of an
ALE, at different levels of abstraction.
Specifically, the (possibly implicit)
adaptation model defines what can be
adapted, as well as when and how it is
to be adapted. The levels of abstraction
at which adaptation may be defined,
range from specific programmatic rules
that govern run-time bahaviour, all the
way to general specifications of logical
relationships between ALE entities, that
get enforced automatically at run-time.
The most widely known ALEs today
(e.g., NetCoach (Weber, and
Brusilovsky, 2001), AHA! (De Bra et al.,
2002b), InterBook (Brusilovsky et
al.,1998), etc.) use adaptation models
that generically specify system
behaviour on the basis of properties of
the content model (such as relationships
between content entities).
Although there would be probably little
contention as to the enumeration of the models
encountered in ALEs, the related literature
reports a proliferation of approaches in their
representation and utilization within different
systems (Brusilovsky, 2003). It is argued that
this is one of the major stumbling blocks that
stand between adaptation and the e-Learning
mainstream today. Awareness of this problem
has given rise to several research efforts,
aimed at standardizing as much of the
adaptation modelling process as possible, on
the basis of existing standards (see, e.g., the
“Workshop on Adaptive E-Learning and
Metadata” carried out under the auspices of
the WM2003 conference -
http://wm2003.aifb.uni-
karlsruhe.de/workshop/w05/). The “reuse” of
existing e-Learning standards and their
“retargeting” for use in the context of
adaptation, which is also a premise of this
paper, is intended to: (a) facilitate the smooth
and gradual transition from existing non-
adaptive learning environments and courses to
their adaptive counterparts, and (b) enable the
graceful downgrading of adaptive content and
activities when delivered over, or supported by,
a “traditional” learning environment.
3. Adaptation and e-Learning
standards
There currently exist numerous organisations,
consortia, etc., that are working in the area of
e-Learning standards. For instance
organisations like the Dublin Core Metadata
Initiative, the IEEE, the IMS Global Learning
Consortium, the Alliance of Remote
Instructional Authoring and Distribution
Networks for Europe, the Aviation Industry
CBT Committee, the Advanced Distributed
Learning Initiative, etc. are dedicated to, or
have committees and working groups active in,
the establishment of e-Learning standards.
It is beyond the scope of this paper to
enumerate all entities involved in the
establishment of e-Learning standards, or the
standards themselves. Instead, the authors
have opted to make selective references to
some of the standards, where such references
are relevant to the ongoing discussion.
Nevertheless, it should be noted that the core
of standards that have been analysed and are
referred to in the subsequent sections are the
various specifications of IMS
1
, ADL SCORM
2
,
the set of standards previously known as
“PAPI”
3
(henceforth referred to simply as
PAPI), and the AICC specifications
4
.
In the following, we first delineate the main
problems not addressed by today’s standards
and then proceed to identify what we consider
as necessary additions / enhancements to
them, as well as point out requirements that
necessitate the evolution of new standards.
1
http://www.imsproject.org
2
http://www.adlnet.org
3
http://jtc1sc36.org/
4
http://www.aicc.org
http://www.ejel.org
©Academic Conferences Limited
Alexandros Paramythis & Susanne Loidl-Reisinger
185
3.1 Adaptation-oriented “domain”
modelling
Current standards and concepts for
educational metadata focus on content-centred
approaches and models of instructional
design. Scenarios that concentrate on how to
structure and organize access to learning
objects are mirrored in concepts such as
content packaging. Standards focus on search,
exchange and re-use of learning material,
often called content items, learning objects or
training components. The Learning Object
Metadata specification, in particular, aims at
metadata to facilitate the generation of
consistent lessons composed of de-
contextualised and distributed learning objects
(e.g., consistence in the level of difficulty). Its
vision is to enable computer agents to
automatically and dynamically compose
personalized lessons for an individual learner.
The IMS Learning Design specification goes a
step further, by providing a conceptual model
that enables authors to describe processes
and activities including social interaction. The
MASIE Centre Report (MASIE Centre, 2002)
identifies four main uses of metadata today:
categorisation of content, generation of
taxonomies, reuse, and dynamic assemblies.
All uses are directly or indirectly relevant to
adaptation / personalisation.
As already mentioned, current, generic ALEs
that support adaptive course delivery require
an additional level of information about the
entities that make up a course, namely the
interrelationships between the entities
(Brusilovsky, 2003). The primary goal in
seeking standardisation in this dimension is to
make it possible to have declarative definitions
of relationships and concepts, leaving their
procedural interpretation and implementation
to each ALE. Using these, different systems
may choose to provide different adaptive
features or support different types of
personalisation, much in the same way that
systems differ in how they present
standardised modules.
(De Bra et al., 2002a), for example, address
the definition of higher-level concept
relationship types and the automatic
translation of instances of such types into
lower-level adaptation rules for the AHA!
adaptive e-Learning system. Some of the
relationship types discussed therein denote
direct relationships between concepts and
learning elements (e.g., concept A is a
prerequisite for concept B, element X
exemplifies concept C), while others bear a
clear adaptation / knowledge inference flavour
to them (e.g., element Y when read provides
knowledge towards concept D, or, element Y
when read indicates interest in concept E).
At a lower level than De Bra, we also need to
be able to define “assets” associated with
“learning objects / elements” which can have
standardised relationships to each other and to
the enclosing object. Consider, for example,
two mutually exclusive elaborations of a given
concept, one being brief and the other
detailed; contrast that with two complementary
elaborations of a given concept, the first being
a required brief reading, while the second
being an auxiliary amendment to the first. This
also implies the possibility to define learning
elements that are (more or less) atomic chunks
of learning material, distinct from “pages” and
with arbitrary granularity (e.g., a paragraph).
Currently, defining relationships such as the
ones described above, can be achieved
through the use of Learning Object Metadata,
if the following conditions are met:
A “vocabulary
5
” is developed defining
the relationships between concepts, as
well as the characteristics of these
relationships (e.g., transitivity), so that
their interpretation by application
software is not open to interpretation.
Every learning entity that is an individual
“concept” has an associated LOM-
compliant metadata record.
The entity’s metadata specify the entity’s
relationships with other entities, using
the aforementioned relationship
vocabulary and the entities’ identifiers.
This approach has the benefit of compliance
with current standards, and requires only the
introduction of a new, adaptation oriented
vocabulary for relationships. A similar
approach would be to introduce dedicated
(optional) adaptation-specific constructs in the
main course description. The latter, however,
would evidently require modifications to
standards commonly used to define courses,
which may be considered a much higher (as
compared to the above approach) “entry cost”
for introducing adaptation in e-Learning
standards. A third option would of course be to
keep adaptation-related information / metadata
separately than the description of the course
itself. This has the benefit of rendering the two
rather independent, but would most likely
prove problematic in terms of course
maintenance. This is especially the case as far
5
Alternatively referred to as a “value domain”, for which
the “permissible values” are well specified.
http://www.ejel.org
©Academic Conferences Limited