Visualisation Tools for Supporting Self-Regulated
Learning through Exploiting Competence Structures
(University of Graz, Department of Psychology, Graz, Austria
(University of Graz, Department of Psychology, Graz, Austria
(University of Graz, Department of Psychology, Graz, Austria
Abstract: In this paper an approach is presented how self-regulated learning can be supported
and stimulated by visualising knowledge and competence structures in order to provide visual
guidance in the learning process. In the field of adaptive systems and related research
techniques of intelligent guidance have been developed, which, however, may have the
disadvantage of limiting the learner. On the other hand, self-regulated learning gives greater
control and responsibility to the learner, however, especially weak learner may have difficulties
without provision of guidance. The presented approach combines both offering guidance and
granting control over the own learning process. A set of learning tools have been developed
which implement and demonstrate the proposed approach. Since knowledge structuring and
knowledge visualisation are well established in the field of knowledge management, this
approach can be exploited to bridge the research fields of e-learning and knowledge
Keywords: adaptive system, adaptivity, self-regulated learning, skill, competence, Knowledge
Space Theory, information visualisation, human-computer interface
Categories: H.5.2, L.3.1, L.3.4, L.3.6
Presently, two important strands of e-learning research can be observed: First,
adaptivity and personalisation provided by adaptive systems capable of tailoring
content and behaviour to characteristics and needs of learners, and second, self-
regulated learning, a pedagogical approach which claims to give more control and
responsibility to the learner. The first strand, adaptive systems, has its origin in
technological developments (computer systems, Internet, hypermedia) and is
characterised by research how technology can support and guide the learning process.
This approach to e-learning holds the risk of having the learning process to a large
extent controlled by the system. If the models or structures underlying system
behaviour are invalid, however, the guidance provided by the system is actually worse
than no guidance [De Bra, 2000]. In contrast, the second research strand, self-
Proceedings of I-KNOW ’08 and I-MEDIA '08
Graz, Austria, September 3-5, 2008
regulated learning, has its origin in pedagogical learning theories and focuses rather
on the learning process of learners than on technology. Self-regulated learning,
however, requires the ability to autonomously define learning goals and paths, which
an individual not necessarily possesses [Baumgartner and Payr, 1994]. Especially
novices and beginners in a knowledge domain therefore need some support in
directing their learning [Ley, 2006]. Besides, the tradition of self-regulated learning is
not grounded on formal models that would be needed for technical implementation.
The approach presented in this paper combines these two research strands in
order to make use of the advantages of both for the learner's benefit. A set of tools has
been developed which follow and demonstrate this approach. Research and
development of these tools are part of the iClass research project [iClass, 2008]. The
aim was to support a self-regulative learning cycle, which according to [Zimmerman,
2002] consists of forethought (planning), performance (monitoring) and reflection.
The developed tools support the planning and reflection processes, performance
(viewing learning objects) is done by other iClass components.
The next section gives an overview on the research fields which are basis for our
approach. [Section 3] gives a more detailed description of our approach and presents
the developed tools. Selected development details and integration into the iClass
system are described in [Section 4]. Future work and conclusion can be found in
2 Theoretical foundation and related work
2.1 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 and 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-
2.2 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
A. Nussbaumer, C. Steiner, D. Albert: Visualisation ... 289
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.
2.3 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 and 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 and 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.
2.4 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.
290 A. Nussbaumer, C. Steiner, D. Albert: Visualisation ...
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.
3 Tool description and learning cycle
3.1 Combined approach
Though the approaches described in [Section 2] are rather different, they can be
combined to a uniform and new approach taking advantage from each side. The
approach of adaptive systems is based on user and domain models which are used to
provide guidance by exploiting an adaptation model [Figure 1a]. The approach of
self-regulated learning is based on mental learning processes of the learner and
describes which tools support the respective processes [Figure 1b].
Domain / User Models
content tools goal setting
(a) adaptivity approach (b) self-regulated learning
(c) combined approach
visual tool for
visual tool for
visual tool for
Figure 1: Combined approach based of adaptive systems and self-regulated learning.
The combined approach is to create learning tools, whereby each tool is related to
a specific learning process in terms of self-regulated learning [Figure 1c]. The set of
these tools represents a whole learning cycle and supports self-regulated learning as a
whole. The tools employ user and domain models for two purposes: First, the
domain and user models are visualised (through various information visualisation
techniques), and second, guidance based on the adaptation model is granted also in a
visual way, rendered on the same or additional visualisations. Hence, the same kinds
of models which are used by adaptive systems are presented to the learner in an easily
understandable manner. This empowers the learner to take over control from an
A. Nussbaumer, C. Steiner, D. Albert: Visualisation ... 291
adaptive system while being supported by the system through visualised structures
and visual guidance.
Domain and user models are based on CbKST [see also Görgün et al., 2005]. The
central elements are skills which are assigned to both learning objects and assessment
objects. A skill is defined by a set of domain concepts and an action verb which
specifies the cognitive processing of the respective concepts (e.g. apply the
Pythagorean Theorem). The user knowledge is represented as a set of skills which the
learner has available (competence state) and a set of skills which the learner should
have available at the end of the learning process (competence goal).
3.2 Planning Tool
The Planning Tool [Figure 2] supports the learning processes of goal setting and use
of task strategies. This tool visualises the domain skills and their prerequisite relations
as Hasse diagram (similar to directed acyclic graph) with ascending sequences of line
segments representing a prerequisite relation. On this graph, skills can be chosen to
define the competence goal and subsequently sequenced on the visual plan
component. Prerequisite skills of the chosen skills are also added to the plan. If the
created sequence of the skills is not in line with the prerequisite structure, this tool
gives visual feedback (in terms of coloured skills). Furthermore, it provides the
functionality of automatically sequencing the chosen skills corresponding to the
prerequisite relations. Furthermore, for each skill learning objects can be searched and
chosen which teach the respective skill. As soon as for all skills of the competence
goal learning objects have been added to the plan, visual feedback is provided that the
plan is complete. Further guidance is granted, as the tool also can propose meaningful
sequences of learning objects by using the learning object - skill relation.
Figure 2: Planning Tool. The figure shows the prerequisite relations on skills and a
plan consisting of skills and learning objects.
292 A. Nussbaumer, C. Steiner, D. Albert: Visualisation ...
3.3 Adaptive Assessment Tool
An adaptive assessment based on KST [Doignon and Falmagne, 1999] is conducted to
determine which skills a learner has available. Questions are posed to the learner
taking into account previous answers and exploiting prerequisite relationships among
problems. The traditional algorithm calculates the sequence of questions and is
capable of posing a minimal number of questions to determine the learner's
knowledge. The result of the assessment is a (verified) set of skills (competence state)
which the learner has available.
In order to support self-regulated learning, modifications to the algorithms are
made, which gives the learner greater control over the assessment procedure: (1)
Instead of actually answering the question, the learner may judge whether to be able
to solve the respective problem, which supports self-reflection. (2) The learner can
determine the difficulty level of the questions. (3) Instead of presenting exactly one
question to the learner, the algorithm can present a set of questions and the learner
may choose between these questions.
3.4 Self-Evaluation Tool
A learner may reflect on what having learned by defining skills which consist of
concepts and action verbs. This is done in three steps: (1) The learner is provided with
a list of concepts and chooses those concepts that have been covered in the learning
process so far. (2) Then the learner self-evaluates for each concept the level of
‘expertise’. These levels are indicated by the Bloom taxonomy levels, i.e. the action
verbs remember, understand, apply. (3) The combination of concepts and Bloom level
action verbs results in skills – defined by the learner.
With this approach during the self-evaluation procedure the learner reflects on
what having learned and after the procedure the learner is presented with the skills
which result from the self-evaluation procedure. In contrast to the Assessment Tool,
this method does not pose questions, but asks directly for learned domain concepts.
3.5 Learner Knowledge Presentation Tool
This tool presents the skills which the learner has learned during the learning process.
Three sources for this information are used: (1) The skills which have been taught by
learning objects are visualised in a chronological order together with the learning
objects. (2) The acquired (verified) skills resulted from the adaptive assessment and
(3) the skills (non-verified) resulted form the Self-evaluation Tool.
The presentation of skills is done in a visual way, learned acquired skills (verified
and non-verified) are rendered in different colours. Furthermore, the competence goal
(also defined as set of skills) is rendered in a way that missing skills can be seen
immediately. In this way the learner directly monitors his learning progress and skill
gap (compared to the competence goal).
3.6 Domain Structuring Tool
Creating domain models is usually the task of teachers and domain authors. A tool has
been developed which allows for easily creating domain models by again employing
visualisation techniques. For example, defining prerequisite relations between skills
A. Nussbaumer, C. Steiner, D. Albert: Visualisation ... 293
can be immediately seen in the prerequisite graph, and assigning skills to learning
objects are done in a fish-eye visualisation where all learning objects including the
assigned skills are show and the selected learning object is magnified.
4 Implementation and Integration
For the implementation of the tools an open and extensible framework has been
developed which consists of four pillars: (1) The knowledge representation model is
implemented as object-oriented model and can be easily used by the other
components. A converter has been created which transforms the domain model into
OWL format and vice versa. (2) CbKST algorithms (e.g. assessment algorithm) have
been implemented and integrated into the framework. (3) Visual components (e.g.
prerequisite relation graph) which rely on the knowledge representation model are
implemented as reusable software components. They make use of information
visualisation techniques, such as graph drawing and fish-eye distortion. (4) The tools
have implemented basic user interfaces and integrate the knowledge representation
model, the CbKST algorithms, and the visualisation components.
All implementation is done in Java and almost all parts are developed from
scratch (except the OWL parser). Besides using the tools as stand-alone application,
they also can be used as Applets, which is needed for the integration into the iClass
system. The iClass system is designed as a service-oriented architecture with a Web-
based front-end and an application server. Integration into the iClass system is
realised in two ways. First, the tools are part of the front-end and are launched from
front-end components. Second, the tools make use of the iClass services via SOAP,
for example loading and storing domain models is done on the content delivery Web
5 Conclusions and Future Work
In this paper an approach has been presented how self-regulated learning can be
supported and stimulated. This approach makes use of concepts of the adaptive
systems and related research in order to integrate guidance in self-regulated learning
processes. Furthermore, a knowledge representation model (domain and user model)
is used as a basis for the guidance. In contrast to adaptive systems, these models are
not hidden from the user and only used by the adaptation algorithms, but - and this is
seen as the major innovation of this paper - these models are visualised by the
learning tools. Through these visualisations the learner can get both guidance and
responsibility for his learning process at the same time. Several tools have been
developed which exploit this approach in order to support particular self-regulated
The presented approach is supposed to have great potential for further work. The
research field of information visualisation is lively, which can bring new possibilities
of visual guidance. Furthermore, in the field of knowledge management, knowledge
structuring and knowledge visualisation are well established. Both are essential for
the presented approach and hence, can be exploited to bridge the research fields of e-
learning and knowledge management.
294 A. Nussbaumer, C. Steiner, D. Albert: Visualisation ...
Evaluation of usability and learning effectiveness of the developed tools are
currently conducted and will be finished before the final review of the iClass project
in summer 2008.
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