Competence-based adaptation of learning environments in 3D space

Abstract

This paper presents a concept how a learning environment can be established in 3D space and how it can be adapted to the competence state of a learner. In contrast to existing Learning Management Systems learning paths are spatially represented in 3D space. In this approach the learner can immerse into a virtual learning landscape consisting of learning objects and is guided by highlighting a path through the landscape. Path creation is based on skills which are assigned to learning objects and which make up the learner model. Principles of the self-regulated learning approach is realised by visualising the learner model in 3D space and by giving the learner freedom for the own learning process. An implementation of this approach is realised in the Second Life virtual world which is connected with a Web service managing the adaptation strategy.

Full text

CONFERENCE IMCL2009 APRIL 21 – 24 AMMAN, JORDAN
IMCL International Conference on Mobile and Computer aided Learning - www.imcl-conference.org 1
Competence-based Adaptation of Learning
Environments in 3D Space
A. Nussbaumer1, C. Gütl2, D. Albert1, D. Helic2
1 Department of Psychology, University of Graz, Graz, Austria
2 Institute Information Systems and Computer Media, Graz University of Technology, Austria
Abstract—This paper presents a concept how a learning
environment can be established in 3D space and how it can
be adapted to the competence state of a learner. In contrast
to existing Learning Management Systems learning paths
are spatially represented in 3D space. In this approach the
learner can immerse into a virtual learning landscape
consisting of learning objects and is guided by highlighting a
path through the landscape. Path creation is based on skills
which are assigned to learning objects and which make up
the learner model. Principles of the self-regulated learning
approach is realised by visualising the learner model in 3D
space and by giving the learner freedom for the own
learning process. An implementation of this approach is
realised in the Second Life virtual world which is connected
with a Web service managing the adaptation strategy.
Index Terms—Adaptation, Competence, E-Learning,
Virtual Reality.
I. INTRODUCTION
Adaptation and personalisation have been active and
lively areas of research over the last two decades, which
have often been reflected in scientific literature (for
example Brusilovsky et al., 1996; Conlan et al., 2002; De
Bra et al., 1999). The educational context has been one of
the most important application domains. Adaptive systems
have been created which are capable of adapting their
behaviour and output to the learners' needs and
preferences. An important system property is the ability to
adapt the sequence of the learning material (also called
learning path) to the learner's knowledge level.
Learning Management Systems (LMS) are computer
systems which mange the learning process on the level of
learning material. Usually they allow for creating and
storing learning content and they are responsible for
presenting content to the learner in a more or less
meaningful and intelligent order. Simple and non-adaptive
systems like Moodle (Moodle, 2009) enable the content
author and teacher to manually sequencing the learning
material. More sophisticated systems like AHA! (AHA!,
2009) have implemented strategies to dynamically
sequencing the content and to adapt the sequence to the
learner profile.
Traditionally, sequencing has been done by presenting
one learning object after another to the learner. From a
pedagogical point of view this is often criticised, because
the learner has no control and overview on the own
learning process. In order to overcome these shortcomings
the Open Learner Model approach aims at making visible
the model which is used by a system to adapt its behaviour
(Bull et al., 2008; Nussbaumer et al., 2008). Additionally
the visual models can be interactive to enable learners to
control their learning process. This approach should
stimulate the learners to reflect on their knowledge and
learning process. On a more general level this approach is
described in the scientific literature in the context of self-
regulated learning, which is described in the next section.
This paper presents an approach how a virtual reality
environment can be used to present learning material to
the learner. In contrast to other systems the learning path
is not a logical sequence of learning objects, but the
learning objects are arranged in 3D space in order to
represent the path in a spatial way. The adaptation strategy
is based on Competence-based Knowledge Space Theory
(see next section), which adapts the learning path to the
competence state of the learner. A detailed description of
this approach is given in Section III. Implementation
details and system architecture is described in Section IV.
II. THEORETICAL BACKGROUND
A. Adaptivity and adaptive systems
The concept of adaptivity has a long tradition in
technology-enhanced learning, for example it has been
applied in Intelligent Tutoring Systems (ITS) to some
extent, user-model-based Adaptive Systems (AS), and
Adaptive Hypermedia Systems (AHS) (Brusilovsky,
2000). Following the discussion in (Brusilovsky, 1996; De
Bra et al., 2004), users (learners) differ in terms of
(learning) goals, pre-knowledge, individual traits and
needs, as well as pedagogical parameters. Based on these
characteristics adaptive presentation (adaptation on the
content level) and adaptive navigation support (direct
guidance, adaptive ordering, hiding, and annotation of
links) are the most important features which can be
provided by an adaptive system. Domain models and user
models are defined in order to specify relationships
between users and content, which forms the basis for the
adaptation functionality. In educational applications these
relationships typically represent the knowledge about
learners and content. Furthermore, adaptive systems
usually contain adaptation models which determine the
adaptation strategy of those systems. In this way an
adaptive system can help the learner to navigate through a
course by providing user-specific paths.
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B. Competence-based Knowledge Space Theory
(CbKST)
Knowledge Space Theory (KST) and its competence-
based extensions (CbKST) are prominent examples how
an adaptation strategy can be grounded on a theoretical
framework (Hockemeyer, 2003). KST constitutes a sound
psychological mathematical framework for both
structuring knowledge domains and for representing the
knowledge of learners. Due to (psychological)
dependencies between problems prerequisite relations can
be established. The knowledge state of a learner is
identified with the subset of all problems this learner is
capable of solving. By associating assessment problems
with learning objects, a structure on learning objects can
be established, which constitutes the basis for meaningful
learning paths adapted to the learners knowledge state.
Competence-based Knowledge Space Theory (CbKST)
incorporates psychological assumptions on underlying
skills and competencies that are required for solving the
problems under consideration. This approach assigns to
each problem a collection of skills which are needed to
solve this problem and to each learning objects those skills
which are taught. Similar to the knowledge state a
competence state can be defined which consists of a set of
skills which the learner has available. Furthermore, there
may also be prerequisite relationships between skills.
CbKST provides algorithms for efficient adaptive
assessment to determine the learner's current knowledge
and competence state, which builds the basis for
personalization purposes. Based on this learner
information, personalised learning paths can be created.
C. Self-regulated Learning
Self-regulated learning has become increasingly
important in educational and psychological research.
Compared to adaptive learning systems, the tenor in self-
regulated learning is to give the learner greater
responsibility and control over all aspects of (technology-
enhanced) learning. There are only few attempts trying to
build a complete model of self-regulated learning
(Puustinen & Pulkkinen, 2001). Most of these models deal
with self-regulation as a process that involves goal setting
and planning, monitoring and control processes, as well as
reflection and evaluation processes. From this it becomes
apparent that self-regulation is closely related to meta-
cognitive strategies. In (Dabbagh & Kitsantas, 2004) six
self-regulatory processes and their significance to Web-
based learning tools have been identified. For example (a)
goal setting is supported by communication tools, such as
e-mail communication with a tutor, (b) the use of task
strategies is supported by content delivery tools, such as
concept mapping software to organise course content, (c)
self-monitoring is supported by use archived discussion
forums, (d) self-evaluating is supported by the use of
rubrics, evaluation criteria, and peer feedback, (e) time
planning and management is supported by communication
tools concerning time budgeting, and (f) help seeking is
supported by hypermedia tools.
D. Information and knowledge visualisation
The abilities of humans to recognise visual information
are highly developed. Patterns, colours, shapes and
textures can rapidly and without any difficulty be
detected. On the other hand, the perception of text-based
content is much more effort than the perception of visual
information (Shneiderman, 1996). Information
visualisation is the transformation of abstract data and
information into a form that can be recognised and
understood by humans. In this sense, information
visualisation can be seen as an interface to abstract
information spaces. So exploring large volumes of data
can be done effectively by humans.
Information visualisation techniques are widely used in
Web-based social software (e.g. graph visualisation is
used to outline online community networks and tag clouds
are often used to provide overview on collaboratively
tagged Web content) and especially in knowledge
management (e.g. visualisation of large knowledge
structures for providing overview and interface to it). In
contrast to these application areas, information
visualisation is barely used in e-learning applications.
An emerging field of applying information and
knowledge visualisation is the area of Open Learner
Models. These models are visually outlined and presented
to the learner in order to provide the possibility of
inspection them. Learners might want to know about the
basis for the calculations of the system. According to (Bull
et al., 2008) opening up these models might increase self-
reflection and motivation of the learners.
III. CONCEPTUAL APPROACH
In this section an approach is described on a conceptual
level how a virtual learning landscape can be created in
3D space, which is capable of automatically adapting to
the learner and of providing guidance and feedback for the
learner.
A. The educational perspective
Basically the learning material is created in 3D space by
content authors. They create and place learning objects in
a 3D landscape, whereby learning objects are more or less
interactive 3D models which convey specific knowledge
chunks. It is up to the content author how sophisticated the
learning objects are designed. They can consist of simple
text documents, contain images and diagrams, or they can
also contain movies which are played. Furthermore
learning objects can also be complex 3D models which
represent three-dimensional information, for example
molecules. The learner (represented as avatar) can walk
through this landscape and make use of the several
learning objects. In this way a learning landscape emerges
which, however, is still static and has no adaptive features.
Positioning of learning objects in the virtual world is
done by the content author manually. This is important
because the spatial position may also contain information
with respect to content. For example, the solar system
domain may be structured in the way that the planets are
learning objects and the spatial position represents the real
positions of the planets. For this reason positioning must
not be done dynamically by the system, though in some
cases this would be a possibility to express the learning
path. Obviously it is a requirement for the virtual world
that a content author can create 3D objects and freely
move and position them in 3D space.
Learning paths are an important property of
technology-enhanced learning systems. As described in
Section 2, Competence-based Knowledge Space Theory
(CbKST) provides psychologically sound methods to
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create meaningful learning paths. In traditional learning
systems they are realised as a sequence of learning
objects, whereby the learner has a next-option to move
forward. In 3D space there is a different situation, since
learning objects are permanently positioned and the
learner walks through the space. Therefore methods are
needed which indicate the path through the learning
landscape. For example, objects can be highlighted by
pointing a spot light to an object which should be learned
next. In computer graphics light sources are well known
concepts in 3D worlds which most of them have
implemented. Another method would be to highlight a
learning object by adding a marker object to each learning
objects which can change the colour to indicate that this
learning object is the next one.
Realising a learning path is done by successively
highlighting one learning object after another one. This
mechanism bears also the possibility to highlight more
objects at the same time if they are equal (at the same
level) regarding the logical sequence. In this way the
learner can freely choose between them, which gives more
control to the learner. A further level regarding self-
regulated learning is obviously given by the fact that the
learner can freely move in 3D space and can deal with any
learning object in the learning landscape independent of
the highlighting state. Obviously this is a very natural way
of providing self-regulated learning possibility, since it
comes from the general system design and is not an
explicitly created system feature.
In order to implement an adaptive strategy and to
arrange learning objects accordingly a knowledge
representation model is needed. Basically a domain has to
be defined which comprises a subject matter at an
appropriate size. For example the Pythagorean Theorem
can be a manageable domain for pupils. Learning objects
are the components which teach the domain and which the
learners interact with. Furthermore, skills are defined
which describe knowledge of learners on a cognitive level.
Skills are related to learning objects in the way that
learning objects are teaching specific skills. Furthermore,
following Competence-based Knowledge Space Theory,
skills are structured through prerequisite relations between
skills meaning that certain skills should be learned before
other skills.
A further element of the conceptual design is a
feedback object which gives information to the learner
about the learned skills. As pointed out above, by using a
domain model for content structuring, skills are defined
including prerequisite relations between them. The skills
of a domain and their prerequisite structure are
represented in 3D space as a 3D skill structure model. If a
learning object has been done than specific skills have
been taught by this learning object. These skills can be
highlighted in the skills structure model by changing the
colour of the respective skills. For example learned skills
can be green, the other skills can be grey.
This approach follows the idea of opening up user
model to the learner, which is supposed to stimulate self-
reflection and motivation (see Section 2). However,
instead of presenting this information as list or 2D
diagram, in a virtual world the structure can be
represented as 3D model. The skill structure model is
visible all the time, as long as the learner is not too far
away from it. No extra window is needed for the
presentation of this information.
The described design also bears possibilities for
collaborative learning, if the used virtual world provides
communication features between different persons.
Instead of a single learner, the walk through the learning
landscape can be done by a group of learners. Then they
can talk about the subject matter, their difficulties of
understanding, and what they actually do understand.
Furthermore, a tutor can accompany a learner through the
learning landscape for the reason of direct communication
and help.
B. The technical perspective
Following the design described in (Nussbaumer et al.,
2007), the overall design of the system is split into two
parts. First the virtual world with a 3D interface contains
the learning objects (learning content) and is used by the
learners to interact with the system. Second the logical
part (CbKST Web Service) stores domain model and user
model and is responsible for the adaptation strategy. For
the sake of flexibility these two parts are separated into
two systems which communicate with each other over the
Internet using SOAP protocol (see Figure 1).
Figure 1. The overall system architecture. The learner interacts with
the virtual world and an extension inside the virtual world connects to
the CbKST Web Service for providing adaptation, guidance, and
feedback.
As outlined in Figure 1, an extension to the virtual
world has to be implemented. On the one hand this
extension is connected to the learning objects and skill
structure object and controls the behaviour and state of
them. On the other hand it is also connected to the CbKST
Web Service where it gets the information how it should
control the learning landscape. Obviously the virtual
world must allow integrating program code which can
also make connections over the Internet to Web Services.
If a learner has done a learning object, then this has to
be indicated by clicking on the marker object attached on
the learning object. The marker object changes its state
(colour) and sends a message to the CbKST extension that
it has been clicked. The CbKST extension passes this
information to the CbKST Web Service and retrieves a
message which learning objects should be done next.
After that the CbKST extension initiates that the
respective learning objects (or attached markers) change
their state to indicate that the learner should continue with
them.
In addition to indicating the next learning objects the
skill structure object also has to be updated. After a
learning object has been done, the CbKST extension also
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gets the current skill state and sends this information to the
skill structure object which changes its appearance
according to the current skill state.
These two communication flows outline that a
communication infrastructure is needed. Object in the
virtual world have communicate with the control module
(CbKST extention) and the control module has to
communicate with the CbKST Web Service.
In order to achieve adaptation, guidance, and feedback,
an adaptation module is needed, which is designed as Web
Service. In this place the algorithms for creating learning
paths and for calculating the current skill state are
implemented. Furthermore domain and user model are
located there which the algorithms need for their
calculations. The separation of this component form the
virtual world brings independence from the
implementation inside the virtual world. In this way more
flexibility can be achieved, since the Web Service only
has to be developed once, even if different virtual worlds
are employed.
IV. IMPLEMENTATION
For the implementation the popular Second Life
(Second Life, 2009) has been chosen as virtual world.
Compared to other similar virtual worlds, such as (Sun
Wonderland, 2009), Second Life has reached a reasonable
technical maturity level, since development already has
been ongoing for ten years. However, every virtual world
can be used for the implementation if the technical
requirements are fulfilled.
The logical part which is responsible for managing the
knowledge representation model, for managing the user
model based on the knowledge representation model and
for generating the learning paths on a logical level is
implemented as a Web Service in a Tomcat environment.
It can be contacted via standard Web Service interface
(SOAP, XML-RPC).
The most important requirements for the virtual world
are the possibility of authoring the 3D objects and
manually positioning them. Furthermore, it must be able
to connect to a Web Service via SOAP or XML-RPC in
order to get information regarding learning path and user
model. Next it must be possible to add small programs
(scripts) to 3D objects which can do control tasks, such as
starting learning path service or connecting to Web
Service. Finally, the virtual world must offer the
possibility to dynamically change properties of 3D
objects, such as changing the colour of an object.
Second Life offers all these requirements sufficiently.
Objects can be easily created without experience in CAD.
A simple interface allows the author to create new objects,
to change properties, and to add textures. Scripts can be
added to each object, which controls their behaviour. A
simple scripting language is used which can be learned if
an author has little programming skills. This language
provides a function to access a Web Service via XML-
RPC and gets the result of this call as an event for further
processing.
A prototype of the implementation has nearly been
finished, which follows the conceptual approach described
in Section 3. Figure 2 and Figure 3 present a screenshot of
a setting in Second Life with six learning objects and a
skill structure object with nine skills. In this example,
learning objects are realised as simple text panels,
however they also could be more complex models. Each
learning object has a marker at the bottom which shows
the current state of the learning object. There are three
possible states, green means that the learning objects has
been done by the learner, orange means that this object
should be done next, and grey means that this object
should be done later. The skill structure model shows the
skills for this example domain in a prerequisite structure.
Skill can have two states, green spheres are already
acquired skills and grey spheres are not acquired skills.
The learner who walks through the learning landscape is
represented as the avatar in the front (Figure 2) and in the
back (Figure 3).
Figure 2. A learning landscape with six learning objects and a skill
structure model at the beginning. The orange marker of one learning
object indicates that the learner should do this object first.
Figure 3. A learning landscape with six learning objects and a skill
structure model after the learner has already done two learning objects
(green markers). The orange markers indicate that these learning objects
are appropriate to do next. The skill structure model shows the acquired
skills (green spheres).
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Figure 2 shows the situation at the beginning of the
learning process. One of the learning objects has an
orange marker, which indicates that the learner should do
that one. In the skill structure model, no skill is marked as
acquired. Figure 3 shows the situation after the learner
has done two learning objects (objects with green
markers). Two learning objects have orange markers,
which indicates that one of them should be done next. The
learning is free to choose in this situation. The skill
structure model shows that three skills are acquired -
obviously these skills are conveyed by the two learning
objects with the green markers.
Each of the markers and the skills are attached with
scripts. These scripts have the duty to control the
appearance of the marker, to handle the interaction with
the user, and to do the communication with the main
control element. If a learner has finished a learning object,
then the respective marker has to be touched. The script of
the marker changes the colour from grey to green and
reports to the main control element that this learning
object has been done. The main control element (the
CbKST extension) does the communication with the
CbKST Web Service and controls the markers and skill
structure model. After a learning object has been done, the
main control element contacts the CbKST Web Service
and gets information about the updated skill state and
which learning object should be done next. Then this
control element send messages to the markers of the
involved learning objects and a message to skill structure
element to update its state.
V. CONCLUSION AND OUTLOOK
In this paper a novel approach has been presented how
adaptation of learning paths can be realised in virtual
worlds. The adaptation strategy is realised in an external
Web Service which controls the adaptation behaviour of a
virtual world. Theoretical basis for the adaptation is the
psychological sound Competence-based Knowledge
Space Theory which already has been applied in
traditional learning systems several times. Combining
CbKST with virtual reality provides new possibilities
regarding self-regulated learning, since the leaner can use
the inherent properties of the 3D world by freely moving
around in 3D space. A learning path is offered to the
learner, the does not restrict the learner to the given
sequence.
Future development will concentrate on pre- and post-
assessment. If pre-assessment is conducted than the
learning path can be adapted to the pre-knowledge of the
learner. A post-assessment reveals the actual knowledge
of the learner and can indicate which learning objects
eventually should be processed again. Both types of
assessment deliver sets of skills the learner has available,
which can be visualised on the 3D skill structure object in
different colours. For example, skills which have been
taught, but are not available in the post-assessment be
visualised in red colour.
There is a restriction in the usage of a learning
landscape. Since highlighting of learning objects markers
and skills can only be done once at a time, a learning
landscape can be used by only one learner at the same
time. A control object is needed where the learner can
start the learning path service and stop it. As soon as the
learning path service has been activated by a specific
learner, than it is blocked for all other learners. However,
this does not force other persons to be kept out from the
learning landscape unless they do not interact with the
system. A possible solution can be to copy the whole
learning landscape and put it on a different place.
Privacy is another issue which has to be thought of. The
skills structure element shows the current skill state of the
learner, however, everybody who is in the vicinity can see
this. Not everybody likes to be inspected by others. So
some considerations have to been made about that.
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AUTHORS
A. Nussbaumer is member of the Department of
Psychology (Cognitive Science Section) at the University
of Graz, Austria (email: alexander.nussbaumer@uni-
graz.at).
C. Gütl is member of the Institute of Information
Systems and Computer Media (IICM) at the Graz
University of Technology, Graz, Austria, and member of
the School of Information Systems, Curtin Univerity of
Technology, Perth, WA (e-mail: cguetl@iicm.tugraz.at).
D. Albert is full professor at the Department of
Psychology (Cognitive Science Section) at the University
of Graz, Austria (email: dietrich.albert@uni-graz.at).
D. Helic is member of the Institute of Information
Systems and Computer Media (IICM) at the Graz
University of Technology, Graz, Austria (e-mail:
dhelic@iicm.tugraz.at).
The work presented in this paper is partially supported by European
Community under the Information Society Technologies (IST) program
of the 7th FP for RTD - project GRAPPLE. The authors are solely
responsible for the content of this paper. It does not represent the opinion
of the European Community, and the European Community is not
responsible for any use that might be made of data appearing therein.
Published as submitted by the author(s).
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