Conference PaperPDF Available

Visualisation Tools for Supporting Self-Regulated Learning through Exploiting Competence Structures


Abstract and Figures

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 management.
Content may be subject to copyright.
Visualisation Tools for Supporting Self-Regulated
Learning through Exploiting Competence Structures
Alexander Nussbaumer
(University of Graz, Department of Psychology, Graz, Austria
Christina Steiner
(University of Graz, Department of Psychology, Graz, Austria
Dietrich Albert
(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
1 Introduction
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
[Section 5].
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-
specific paths.
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
for learner
uses communication,
collaboration, and
content tools goal setting
Adaptive System
(a) adaptivity approach (b) self-regulated learning
(c) combined approach
visual tool for
goal setting
Self-regulated Learner
Adaptation Model
goal setting
Self-regulated Learner
Learning Tools
visual guidance
Learning Cycle
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
learning processes.
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.
[Baumgartner and Payr, 1994] Baumgartner, P. & Payr, S.: Lernen mit Software [Learning with
software]. Innsbruck: Österreichischer Studienverlag.
[Brusilovsky, 1996] Brusilovsky, P.: Adaptive Hypermedia, an Attempt to Analyze and
Generalize, In P. Brusilovsky, P. Kommers, & N. Streitz (Eds.), Multimedia, Hypermedia, and
Virtual Reality (Lecture Notes in Computer Science, Vol. 1077). Berlin: Springer-Verlag, 288-
[Brusilovsky, 2000] Brusilovsky, P.: Adaptive hypermedia: From intelligent tutoring systems to
Web-based education (Invited talk), In: G. Gauthier, C. Frasson and K. VanLehn (eds.)
Intelligent Tutoring Systems. Lecture Notes in Computer Science, Vol. 1839, (2000), pp. 1-7,
Berlin: Springer Verlag.
[Dabbagh and Kitsantas, 2004] Dabbagh, N., & Kitsantas, A: Supporting Self-Regulation in
Student-Centered Web-Based Learning Environments, In: International Journal on E-Learning,
3(1), (2004), pp. 40-47.
[De Bra, 2000] De Bra, P.: Pros and Cons of Adaptive Hypermedia in Web-Based Education,
In: Journal on CyberPsychology and Behavior, 3(1), (2000), pp. 71-77, Mary Ann Lievert Inc.
[De Bra et al., 2004] De Bra, P., Aroyo, L., Cristea, A.: Adaptive Web-based Educational
Hypermedia, In: Mark Levene, Alexandra Poulovassilis (Eds.) Web Dynamics, Adaptive to
Change in Content, Size, Topology and Use, (2004), pp. 387-410, Springer-Verlag.
[Doignon and Falmagne, 1999] Doignon, J.-P., & Falmagne, J-C.: Knowledge spaces, (1999)
Heidelberg, Berlin, New York: Springer-Verlag.
[Heller et al., 2006] Heller, J., Steiner, C., Hockemeyer, C., & Albert, D.: Competence-Based
Knowledge Structures for Personalised Learning, In: International Journal on E-Learning, 5(1),
(2006), pp. 75-88.
[Hockemeyer, 2003] Hockemeyer, C.: Competence Based Adaptive E-Learning in Dynamic
Domains, In: F. W. Hesse & Y. Tamura (Eds.), The Joint Workshop of Cognition and Learning
through Media-Communication for Advanced E-Learning (JWCL 2003) , pp. 79-82, Berlin.
[iClass, 2008] iClass: The iClass research project,
[Ley, 2006] Ley, T.: Organizational competency management - A competence performance
approach. Methods, empirical findings and practical implications, Aachen: Shaker
[Puustinen and Pulkkinen, 2001] Puustinen, M. & Pulkkinen, L.: Models of self-regulated
learning: a review, In: Scandinavian Journal of Educational Research, 45(3), pp. 269-286.
[Shneiderman, 1996] Shneiderman, B.: The Eyes Have It: A Task by Data Type Taxonomy for
Information Visualizations, In: Proceedings of the IEEE Symposium on Visual Languages (VL
1996), pp. 336–343.
[Zimmerman, 2002] Zimmerman, B.J.: Becoming a Self-regulated Learner: An Overview, In:
Theory into Practice, 41(2), (2002), Springer-Verlag.
A. Nussbaumer, C. Steiner, D. Albert: Visualisation ... 295
... Previous studies have explored the use of clickstream data to measure SRL primarily in two types of technology-enhanced learning environments: interactive learning and LMS. The first group of studies has focused on interactive learning environments in which students are offered various tools that are designed to support SRL, including cognitive tools for information processing (e.g., note-taking window), goal-setting tools, reflection tools, and help-seeking tools (Nussbaumer, Steiner, and Albert, 2008;Perry and Winne, 2006;Winne and Jamieson-Noel, 2002). The second large group of studies has focused on student SRL behaviors using clickstream data from LMSs (e.g., blackboard and canvas), which are usually used to deliver learning materials (e.g., text, video, and audio), conduct learning activities (e.g., assignments and discussion), and support different forms of evaluation (e.g., exams and grade book systems; Lewis et al. 2005). ...
... International Journal of Educational Technology in Higher Education (2020) 17:13 and adjust their learning. SRL behaviors, such as cognitive strategy use, planning, and help-seeking, are measured with data on the frequency of, timing of, characteristic conditions of, and behavioral reactions to the use of these SRL tools (e.g., Nussbaumer et al. 2008;Winne and Jamieson-Noel, 2002). While detailed and diverse SRL behaviors can be inferred from data collected from these interactive learning environments, most of these learning environments are used in laboratory studies (e.g., Perry and Winne, 2006;Winne and Jamieson-Noel, 2002) or for specific domains or topics (e.g., learning the human life cycle; Perry and Winne, 2006) and thus have not been commonly adopted in higher education. ...
Full-text available
Abstract Student clickstream data—time-stamped records of click events in online courses—can provide fine-grained information about student learning. Such data enable researchers and instructors to collect information at scale about how each student navigates through and interacts with online education resources, potentially enabling objective and rich insight into the learning experience beyond self-reports and intermittent assessments. Yet, analyses of these data often require advanced analytic techniques, as they only provide a partial and noisy record of students’ actions. Consequently, these data are not always accessible or useful for course instructors and administrators. In this paper, we provide an overview of the use of clickstream data to define and identify behavioral patterns that are related to student learning outcomes. Through discussions of four studies, we provide examples of the complexities and particular considerations of using these data to examine student self-regulated learning.
... This competence model is based on Competence-based Knowledge Space Theory (CbKST) (Heller et al. 2006) that structures competences through prerequisite relations. Visualisation tools have been created that display the competence structures and let the learner select learning goals and learning paths (Nussbaumer et al. 2008). ...
Full-text available
This book presents the outcomes of four years of educational research in the EU-supported project called ROLE (Responsive Online Learning Environments). ROLE technology is centered around the concept of self-regulated learning that creates responsible learners, who are capable of critical thinking and able to plan their own learning processes. ROLE allows learners to independently search for appropriate learning resources and then reflect on their own learning process and progress. To accomplish this, ROLE´s main objective is to support the development of open personal learning environments (PLE's). ROLE provides a framework consisting of “enabler spaces” on the one hand and tools, content, and services on the other. Utilizing this framework, learners are invited to create their own controlled and preferred learning environments to trigger and motivate self-regulated learning. Authors of this book are researchers, developers and teachers who have worked in the ROLE project and belong to the ROLE partner consortium consisting of 16 internationally renowned research institutions, including those from 6 EU countries and China. Chapters include numerous practical tutorials to guide the reader in creating innovative and useful learning widgets and present the best practices for the development of PLE's.
... This competence model is based on Competence-based Knowledge Space Theory (CbKST) (Heller et al. 2006) that structures competences through prerequisite relations. Visualisation tools have been created that display the competence structures and let the learner select learning goals and learning paths (Nussbaumer et al. 2008). ...
Full-text available
Self-regulated learning (SRL) competences are crucial for lifelong learning. Their cultivation requires the right balance between freedom and guidance during the learning processes. Current learning systems and approaches, such as personal learning environments, give overwhelming freedom, but also let weak learners alone. Other systems, such as learning management systems or adaptive systems, tend to institutionalise learners too much, which does not support the development of SRL competences. This chapter presents possibilities and approaches to support SRL by the use of technology. After discussing the theoretical background of SRL and related technologies, a formal framework is presented that describes the SRL process, related competences, and guidelines. Furthermore, a variety of methods is presented, how learners can be supported to learn in a self-regulated way.
... Further studies can develop self-regulation focused training to facilitate the learning of creativity. Nussbaumer, Steiner, and Albert (2008) proposed that the following six self-regulatory processes are important for web-based learning: (1) goal setting supported by communication tools; (2) the use of task strategies supported by content delivery tools (e.g., concept mapping software; (3) self-monitoring supported by the use of discussion forums; (4) self-evaluation supported by the use of rubrics, evaluation criteria, and peer feedback; (5) time planning and management supported by communication tools meant for time budgeting; and (6) help-seeking supported by hypermedia tools. These mechanisms can be taken into consideration while designing a training program for the learning of creativity. ...
Full-text available
The goal of aptitude-treatment interactions (ATIs) is to find the interactions between treatments and learners' aptitudes and therefore to achieve optimal learning. This study aimed at understanding whether the aptitudes of meaning-making, self-regulation, and knowledge management (KM) would interact with the treatment of 17-week KM-based training and then influence creativity in e-learning. The participants were 31 university students, and all variables were measured using online systems. ATIs and mediation effects during the training were found. Specifically, while meaning-making indirectly influenced creativity via KM, self-regulation influenced creativity both directly and indirectly via KM; moreover, university students with higher level of KM and self-regulation ability benefited more from the training than their counterparts. This study not only sheds lights on understanding how ATIs influence creativity learning, but also provides a new approach KM-based training to improve university students' creativity in environments of e-learning.
Many studies indicate that most students find it difficult to learn effectively in computer‐based learning environments because of a lack of self‐regulated learning (SRL) abilities. Therefore, based on the design approach of the rule‐based system, this paper proposes a rule‐based self‐regulated learning assistance scheme (SRL‐RuAS) to intelligently facilitate personalized learning with SRL‐based adaptive scaffolding support for learning computer software. In the SRL‐RuAS scheme, SRL‐based adaptive scaffolding strategies (SRL‐AS) are defined according to SRL behaviors, the subject knowledge is formatted using the concept ontology technique, the relations of learning resources and the portfolio of students are modeled by the proposed learning analysis data model, and the learning analysis rules of adaptive scaffolding are defined according to SRL‐AS. Consequently, an intelligent learning environment with SRL‐RuAS can intelligently give learning support to actively assist students in understanding the learning status and guiding learning improvement. Hence, the learning process with SRL‐based assistance is able to improve learning motivation and effectiveness. Experimental results of a case study for learning computer software show that the SRL‐RuAS scheme is beneficial for learning achievement and the satisfaction of students, and its scaffolding strategies and application domain can thus be manageable and extended because of the rule‐based system design.
Conference Paper
Personalized learning aims to offer students the adaptive learning supports according to individual learning capabilities. However, most students may be difficult to efficiently self-regulate their learning behavior to conduct the active learning processes due to the insufficient Self-Regulated Learning (SRL) abilities. Therefore, this study adopts the SRL learning strategies to design and develop a SRL-Tutor system to support the requirements of the personalized learning. It tries to provide students with the adaptively scaffolding processes to assist them in efficiently learning subject's knowledge and SRL skills, e.g., adaptive prompt and assessment, and personalized learning guidance based on the diagnostic report. Through one-semester experiment in Grade 8 Mathematics subject, its results show that SRL-Tutor is indeed able to efficiently enhance the students' learning outcome.
Conference Paper
A Hypermedia-based Learning Environment (HLE) has shown potential in helping students improve their learning performance in terms of complicated subjects and skills, however, most students find it difficult to learn well in this kind of Open-Ended Learning Environments (OELE) due to a lack of Self-Regulated Learning (SRL) abilities. Manually adaptive scaffolding is labor-intensive and time-consuming, and Intelligent Tutoring Systems focus on the process of adaptive learning content and paths, not the SRL. Therefore, this paper proposes a Self-Regulated Learning System with Rule-based Learning Diagnostic Scheme (SRLS-RLDS) to automatically support adaptive scaffoldings for students in an HLE/OELE, where a rule-based approach and concept ontology is used to model teachers' diagnostic knowledge to help students regulate their learning. The proposed SRLS-RLDS was applied to a case of software learning, and the experimental results showed that students who studied with SRLS-RLDS adaptive scaffoldings had significantly higher post-test scores than those who studied without adaptive scaffoldings. Moreover, all students agreed that the proposed SRLS-RLDS scheme can effectively help them concentrate on learning content and to better understand the domain knowledge in their self-regulated learning processes.
Full-text available
A change in perspective can be certified in the recent years to technology-enhanced learning research and development: More and more learning applications on the web are putting the learner centre stage, not the organisation. They empower learners with capabilities to customize and even construct their own personal learning environments (PLEs). These PLEs typically consist of distributed web-applications and services that support system-spanning collaborative and individual learning activities in formal as well as informal settings. These PLEs typically complement or interface with Learning Management Systems (LMS) with additional widgets, services, and data integrated from and with organization-external learning tools. Consequently, the aim of this workshop is not to discuss the concepts ‘PLE vs. LMS’, but to focus more generally on how learning experiences can be enriched using mash-ups of widgets and services with microformats and how technology can help to respond automatically to competence level, need, or context. Moreover, the investigation of necessary competencies to deploy mash-up technologies is dedicated special attention in this workshop. Technologically speaking, this shift manifests in a learning web where information is distributed across sites and activities can easily encompass the use of a greater number of pages and services offered through web-based learning applications. Mash-ups, the ‘frankensteining’ of software artefacts and data, have emerged to be the software development approach for these long-tail and perpetual-beta niche markets. Core technologies facilitating this paradigm shift are Ajax, javascript-based widget-collections, and microformats that help to glue together public web APIs in individual applications. Interoperability is the enabler to allow these different components to be worked together facilitating the achievement of the underlying learning task. In a wide range of European IST-funded research projects such as LTfLL, Palette, STELLAR, TENcompetence, Mature, iCoper, Role, and OpenScout a rising passion for these technologies can be identified. This workshop therefore serves as a forum to bring together researchers and developers from these projects and an open public that have an interest in understanding and engineering mash-up personal learning environments (MUPPLEs). The aim of this workshop is to bring together the various research and development groups in technology-enhanced learning that currently focus on the development of the next generation learning environments – learning environments that put the individuum centre stage and empower learners with design capabilities by deploying modern mash-up principles to establish system-spanning interoperability. As this approach is rather young, the workshop sought to attract both research results and work in progress in order to chart out the current state-of-the-art of MUPPLEs in TEL and to define main enablers and future challenges. Naturally, it serves as a forum for establishing new collaborations. (
In general, research confirms that learning is more effective when students obtain feedback regarding their learning progress. Currently, new versions of e-learning platforms include indicators that provide some static feedback mechanisms and help both learners and educators in planning their learning strategies. This paper explains the usage of indicators in current e-learning systems, generates a taxonomy for their classification, and studies their influence on student performance. Also, it provides a study which is based on the combination of a user-based evaluation process that facilitates data collection and data mining algorithms to infer association rules between learning variables and performance. The results highlight how procrastination influences negative learning performance and how time-related indicators are tightly coupled with students' performance in e-learning platforms.
Conference Paper
This paper provides an overview of the linkage of two pedagogical approaches, Skills-based Learning and Self-Regulated Learning (SRL) supported by software. In linking these approaches, adaptive skills-based learning tools based on a psycho-pedagogical competence model, were assigned to a cyclic SRL process-model. The INNOVRET project provides the underlying framework in which this new model will be tested. Software components for Moodle (the learning management system of choice) were specifically tailored to suit the needs of the project's targeted audience (i.e. heat pump installers). The entire system/model will be critically analysed in this paper.
Full-text available
The Web has revolutionized the way information is delivered to people throughout the world. It did not take long for learning material to be delivered through the Web, by using electronic textbooks. The use of hypertext links gives the learner a lot of freedom to decide on an order in which to study the material. This leads to problems in understanding the textbook, which can be solved by using methods and techniques. In this chapter we describe how the field of educational hyper- media benefits from and . We also show that the information gathered about the learners and their learning process can be used to improve the quality of the elec- tronic textbooks.
Full-text available
This chapter describes recent and ongoing research to automatically personalize a learning experience through adaptive educational hypermedia. The Web has made it possible to give a very large audience access to the same learning material. Rather than offering several versions of learning material about a certain subject, for different types of learners, adaptive educational hypermedia offers personalized learning material without the need to know a detailed classification of users before starting the learning process. We describe different approaches to making a learning experience personalized, all using adaptive hypermedia technology. We include research on authoring for adaptive learning material (the AIMS and MOT projects) and research on modeling adaptive educational applications (the LAOS project). We also cover some of our ongoing work on the AHA! system, which has been used mostly for educational hypermedia but has the potential to be used in very different application areas as well.
Full-text available
Researchers interested in academic self-regulated learning have begun to study processes that students use to initiate and direct their efforts to acquire knowledge and skill. The social cognitive conception of self-regulated learning presented here involves a triadic analysis of component processes and an assumption of reciprocal causality among personal, behavioral, and environmental triadic influences. This theoretical account also posits a central role for the construct of academic self-efficacy beliefs and three self-regulatory processes: self-observation, self-judgment, and self-reactions. Research support for this social cognitive formulation is discussed, as is its usefulness for improving student learning and academic achievement. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Full-text available
This article first describes the state-of-the-art of model building and empirical research in the field of self-regulated learning (SRL) and then focuses on self-regulated learning in Technology-Enhanced Learning Environments (TELEs). We present recent research results obtained in a European project (TELEPEERS) in the context of which we evaluated TELEs in a peer review manner with respect to their potential for supporting self-regulated learning. In addition, data were obtained on a sample of TELEPEERS students working in these environments and comparative analyses were made across the European project partners.
Conference Paper
Full-text available
Adaptive hypermedia is a new area of research at the crossroads of hypermedia and adaptive systems and. Education is the largest application area of adaptive hypermedia systems. The goals of this paper are to provide a brief introduction into adaptive hypermedia and supply the reader with an organized reading on adaptive educational hypermedia. Unlike some other papers that are centered around the current state of the field, this paper attempts, from one side, to trace the history adaptive educational hypermedia in connection with intelligent tutoring systems research and, from another side, draft its future in connection with Web-based education.
Numerous benefits of student-centered web-based learning environments have been documented in the literature; however the effects on student learning are questionable, particularly for low self-regulated learners primarily because these environments require students to exercise a high degree of self-regulation to succeed. Currently few guidelines exist on how college instructors should incorporate self-regulated strategies using web-based pedagogical tools. The scope of this paper is to (a) discuss the significance of self-regulation in student-centered web-based learning environments; (b) demonstrate how instructional designers and educators can provide opportunities for student self-regulation using web-based pedagogical tools; and (c) redefine the role of the instructor to support the development of independent, self-regulated learners through the use of web-based pedagogical tools.
The effect of self-regulatory processes on test preparation and performance was examined. The author used a structured 1-to-1 interview to query 62 college students enrolled in the same psychology class about their self-regulatory processes. The author expected that (a) high test scorers would use more self-regulatory processes to enhance their test preparation and performance than would low test scorers; (b) self-regulation would positively affect test performance; and (c) self-regulatory skill, self-efficacy beliefs, and perceived instrumentality would predict subsequent test performance. All hypotheses were supported by the data. The results of this study are discussed from a sociocognitive perspective.
Hypertext and hypermedia applications allow users to navigate through large sets of information in many different ways. It is impossible for an author to foresee all possible paths a user may follow. Adaptive hypermedia is a fairly new research field on the crossroad of hypertext (or hypermedia) and user modeling. Its goal is to improve usability of hypermedia through the automatic adaptation of hypermedia applications to individual users. Adaptive hypermedia systems (AHS) offer methods and techniques for adapting the content of information pages and the links between pages. A number of experiments have been conducted to demonstrate the benefits of adaptive hypermedia, mostly of adaptation of link structures. In this article we argue that the benefits of using an AHS are a result of careful authoring, more than of the adaptive techniques themselves.