Conference PaperPDF Available

Using Visual Guidance and Feedback Based on Competence Structures for Personalising E-Learning Experience

Authors:

Abstract and Figures

In this paper a novel approach is presented how visualisation of knowledge and competence structures can be employed in order to support personalisation in e-learning systems. Information visualisation strategies are used to present ontology-based domain knowledge and related competence structures to the learner. In contrast to adaptive e-learning systems which employ domain and user models for the adaptation and personalisation strategies, the learner uses these visual knowledge maps to decide on the own learning process. Furthermore, visual knowledge maps are used to give feedback to the learner about learning progress and assessment result. Methods and techniques are presented how these maps can be used for visual guidance and feedback. A set of learning tools have been developed which implement and demonstrate the proposed approach.
Content may be subject to copyright.
Using Visual Guidance and Feedback Based on
Competence Structures for Personalising
E-Learning Experience
Dietrich Alberta, Alexander Nussbaumera, Christina Steinera
aDepartment of Psychology, University of Graz, Austria
dietrich.albert@uni-graz.at
Abstract: In this paper a novel approach is presented how visualisation of knowledge and
competence structures can be employed in order to support personalisation in e-learning
systems. Information visualisation strategies are used to present ontology-based domain
knowledge and related competence structures to the learner. In contrast to adaptive
e-learning systems which employ domain and user models for the adaptation and
personalisation strategies, the learner uses these visual knowledge maps to decide on the
own learning process. Furthermore, visual knowledge maps are used to give feedback to the
learner about learning progress and assessment result. Methods and techniques are presented
how these maps can be used for visual guidance and feedback. A set of learning tools have
been developed which implement and demonstrate the proposed approach.
Keywords: Adaptive system, adaptivity, self-regulated learning, skill, competence,
Knowledge Space Theory, information visualisation, open learner model
Introduction
Adaptivity and personalisation provided by adaptive systems capable of tailoring content
and behaviour to characteristics and needs of learners has been an important research strand
in e-learning for a long time. This research field has its origin in technological developments
(computer systems, Internet, hypermedia) and is characterised by research how technology
can support and guide the learning process. However, this approach to e-learning holds the
risk of having the learning process to a large extent controlled by the system. Furthermore, if
the models or structures underlying system behaviour are invalid, then guidance provided
by the system is actually worse than no guidance (De Bra, 2000).
The approach presented in this paper makes use of competence-based models and
structures in a different way than adaptive systems employ them for determining system
behaviour. In contrast to traditional 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 presented to the learner directly in a visual and
interactive way. In contrast to some experiences with open learner models in the last years
(Bull & Kay, 2008), our approach focuses on visualising skill-based models. With the help
of these models the learner gets both more control over the own learning process and
specific information about the own learning progress. Instead of traditional system guidance
the learner gets visual guidance and feedback which does not limit the learner to automated
decisions of the adaptive systems.
A set of learning tools has been developed which follow and demonstrate this
approach. Research and development of these tools are part of the iClass research project
3
(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 the use of other iClass components.
The next section gives an overview on the research fields which are basis for our
approach. Section 2 gives a more detailed description of the knowledge representation
model which is used for our approach. Section 3 explains principles how guidance and
feedback can be realised in a visual way and Section 4 presents some developed learning
tools which implement those principles. Future work and conclusion can be found in
Section 5.
1. Theoretical Foundation and Background
1.1 Competence-based Knowledge Space Theory
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 (Albert et al., 1999). 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 (Korossy, 1996 and Heller et al. 2006). 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. Goal setting can be done by defining skills to be achieved (competence goal) or
problems to be capable of solving. The competence gap to be closed during learning is
represented by the skills which are part of the goal but not part of the competence state of a
learner.
1.2 Adaptive Systems and Guidance
The concept of adaptivity has a long tradition in technology-enhanced learning, for example
it has been applied in Intelligent Tutoring Systems (ITS), 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
4
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.
1.3 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 (Herman et. al, 2000) an overview and survey on graph
visualisation techniques is provided. In contrast to these application areas, information
visualisation is barely used in e-learning applications. For example, in (Kay et al., 2007)
visualisation techniques are described how visual feedback and mirroring can be provided
to group and collaborative learning.
2. Knowledge Representation Model
The knowledge representation model elaborated in the context of the iClass project is based
on two pillars: The first pillar is the domain model which represents the knowledge of the
domain and the second pillar is the user model which represents the knowledge of the
learner. The knowledge representation model for a single domain is depicted in Figure 1.
The domain model is based on CbKST and comprises the structure how the domain is
represented regarding learning objects, problems (assessment items), skills, concepts, and
action verbs. The central elements are skills which are assigned to both learning objects and
assessment items. A skill is defined by a set of domain concepts and an action verb which
specifies the level of expertise regarding the respective concepts (e.g. apply the Pythagorean
Theorem). The prerequisite relations between skills build the competence structure. The
domain model is based on the model described in (Görgün et al., 2005), and has been
slightly simplified in order to allow teachers to easily create their own models of the
domains they teach.
A domain map represents a knowledge domain and consists of domain information
populated in line with the structure of the domain model. This can be done by content
authors as well as by teachers. Domain maps can be differentiated between subjective and
objective maps to indicate their state of validation. Objective maps are empirically validated
(ideal case) and subjective maps are not validated, even if they have been created by domain
experts.
The learner model characterises knowledge, competences and learning goal of a
learner. It refers to elements of the domain model, since the learner's knowledge is defined
5
through references to domain knowledge. The knowledge state is a set of problems which
the learner is capable of solving, the competence state is a set of skills which the learner has
available, and the competence goal is a set of skills which the leaner should acquire.
teaches (*)
C A
tests (*)
includes (*)
includes (1)
Problem
Skill
Action VerbConcept
prerequisiteOf (*)
Knowledge
State
Competence
State
Learner
includes (*)
includes (*)
has (1)
Competence
Goal
includes (*)
has (1)
has (1)
Learner Model Domain Model
Learning
Object
Learner Knowledge Representation Domain Knowledge Representation
Figure 1: Knowledge Representation Model consisting of domain model and learner model.
3. Visual Guidance and Feedback
The basic idea how to provide learner and teacher support through visual guidance and
feedback is to visualise the elements and relations of the knowledge representation model
which are typically used by adaptive systems for the personalisation process. An interactive
learning tool which renders the models enables the learner to visually interact with
visualised information for several purposes. For example, plans and sequences of learning
objects and their related skills can be created, learning progress and assessment result can be
reported, and a history of the learning experience can be displayed. The learner gets an
overview on both the domain and the own learning progress.
For example, a visual representation of the structure (prerequisite relation) on skills
can be used for both guidance and feedback. In Figure 2 such a structure is depicted as
Hasse diagram for six skills, with ascending sequences of line segments representing
prerequisite relationships. On this structure a learner can see what should be learned, i.e. the
six skills making up the competence goal. Due to the prerequisite relation, it also can be
seen, that there is an order how the skills should be acquired. Before addressing
higher-ordered skills, the relevant prerequisite skills should be learned. Since skills are
assigned to learning objects, learning objects can be searched which teach the respective
skills. In this way, a plan consisting of a meaningful sequence of learning objects can be
created. After the learning process, an assessment can be conducted which detects the
acquired skills of a learner and the result can again be displayed in the visualisation of the
competence structure. In this way, the learner can e.g. reflect on his current competence in
relation to the competence goal (see Figure 2). To complete the learning cycle the missing
(not acquired) skills can immediately be recognised and a new plan of learning objects can
be composed which teach those skills.
6
As outlined in this example, using these structures the number of reasonable choices
can be significantly reduced. Information on the learner’s current competence state can be
utilised for recommending learning options from which to choose that correspond to
possible next steps of learning. In a similar way, the other elements of the knowledge
representation model can be utilised. Concepts and action verbs can visually be related to
skills which supports the understanding of skills.
Figure 2: Structure on skills illustrated as Hasse diagram. In the right figure
available skills of a learner are displayed.
4. Implemented Learning Tools
The principles of visual guidance and feedback described above have been implemented in
the iClass system in various tools to support the learning and teaching process.
4.1 Skill-based Planning Tool
The Planning Tool (Figure 3) supports the learning processes of goal setting and use of task
strategies. This tool visualises the domain skills and their prerequisite relation as Hasse
diagram. 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 about this situation (in terms of coloured skills). It
also 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 relations between learning object and skill.
4.2 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
7
assessment is a (verified) set of skills (competence state) which the learner has available,
which is subsequently visualised and reported to the learner.
4.3 Self-Evaluation Tool
A learner may reflect on what has been 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 has been covered in the learning process so far. (2)
Then for each concept the level of ‘expertise’ is to be indicated which have been acquired so
far according to the learner's estimation – these levels are indicated by the Bloom taxonomy
levels, i.e. the action verbs remember, understand, and apply. (3) The combination of
concepts and Bloom level action verbs results in skills – defined by the learner.
Figure 3: Skill-based Planning Tool visualises the competence structure where the learner can interactively
create the own learning plan.
4.4 Knowledge Representation Tool
This tool presents the skills 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 from the Self-evaluation Tool. The presentation of skills is done in a visual way,
learned and acquired skills (verified and non-verified) are rendered in different colours.
Furthermore, the competence goal (also 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).
8
4.5 Domain Map Editor
All presented tools rely on the knowledge representation model consisting of skills, learning
objects, assessment question, concepts, and action verbs. Creating the domain map for a
certain knowledge domain is usually the task of teachers and domain authors. A tool has
been developed which allows for easily creating domain maps by again employing
visualisation techniques. For example, defined prerequisite relations between skills can be
immediately visualised, and the assignment of skills to learning objects is depicted in a
fish-eye visualisation where all learning objects including the assigned skills are shown and
the selected learning object is magnified (Figure 4).
Figure 4: Domain Map Editor. All leaning objects (activities) of a domain are displayed together with the
assigned skills. A fish-eye distortion magnifies the selected learning object.
5. Conclusion and Outlook
In this paper a novel approach has been presented how learning can be supported and
stimulated by using visualisation techniques. This approach makes use of concepts of
adaptive systems and related research in order to integrate guidance in learning processes. 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 novel approach in order to support
particular self-regulated learning processes.
The presented approach is supposed to have great potential for further work and can
enrich the lively research field of information visualisation by new possibilities of visual
9
guidance and feedback. More relations between elements of the knowledge representation
model can be visualised and used for the further learning tools. Furthermore, the presented
approach is also supposed to have great influence to the field of self-regulated learning.
Visual guidance can stimulate the self-regulated planning process of a learner, and visual
feedback can support self-monitoring and reflection on learning
Acknowledgements
The work presented in this paper is supported by European Community under the
Information Society Technologies (IST) program of the 6th FP for RTD - project iClass
contract IST-507922. 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.
References
[1] Albert, D., & Lukas, J. (Eds.) (1999). Knowledge Spaces: Theories, Empirical Research, and
Applications. Mahwah, NJ: Lawrence Erlbaum Associates.
[2] Bull, S., & Kay, J. (2008). Metacognition and Open Learner Models. In: The 3rd Workshop on
Meta-Cognition and Self-Regulated Learning in Educational Technologies, at ITS2008.
[3] Brusilovsky, P. (1996). 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,
[4] Brusilovsky, P. (2000). 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, pp. 1-7, Berlin: Springer Verlag.
[5] De Bra, P. (2000). 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.
[6] De Bra, P., Aroyo, L., Cristea, A. (2004). Adaptive Web-based Educational Hypermedia, In: Mark
Levene, Alexandra Poulovassilis (Eds.) Web Dynamics, Adaptive to Change in Content, Size, Topology
and Use, pp. 387-410, Springer-Verlag.
[7] Doignon, J.-P., & Falmagne, J-C (1999). Knowledge Spaces. Heidelberg, Berlin, New York:
Springer-Verlag.
[8] Görgün, I., Türker, A., Ozan, Y., & Heller, J. (2005). Learner Modeling to Facilitate Personalized
E-Learning Experience. Proceedings of Cognition and Exploratory Learning in Digital Age (CELDA
2005) (pp. 231-237).
[9] Heller, J., Steiner, C., Hockemeyer, C., & Albert, D. (2006). Competence-Based Knowledge Structures
for Personalised Learning, In: International Journal on E-Learning, 5(1), (2006), pp. 75-88.
[10] Herman, I., Melancon, G., & Marshall, M. (2000). Graph Visualisation and Navigation in Information
Visualisation: A Survey. IEEE Transactions on Visualization and Computer Graphics, 6(1), pp. 24-43).
[11] Hockemeyer, C. (2003). 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.
[12] iClass (2008): The iClass research project, http://www.iclass.info/
[13] Kay, J., Kalina, Y., & Reinmann,.P. (2007). Visualisations for Team Learning: Small Teams Working on
Long-term Projects. Proceedings of the Conference on Computer-supported Collaborative Learning
(CSCL 2007), International Society of the Learning Sciences.
[14] Korossy, K. (1997). Extending the Theory of Knowledge Spaces: A Competence-Performance
Approach. Zeitschrift für Psychologie, 205, pp. 53-82.
[15] Shneiderman, B. (1996). 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.
[16] [Zimmerman, 2002] Zimmerman, B.J.: Becoming a Self-regulated Learner: An Overview, In: Theory
into Practice, 41(2), (2002), Springer-Verlag.
10
... It supports a variety of different learning object (LO) formats and question types (herein referred to as assessment items (AIs)). Despite these advantages, Moodle is course-based and does not cater for the individual needs of students [2]. However, its extensibility (plug-ins and modules) offers a promising possibility to introduce personalised learning support, which gives the learner more freedom to control their own learning process. ...
... While competence-based learning focuses on the subject domain level, SRL emphasises the learners' own ability to control and regulate each step of their learning process. A common SRL model regards learning as a cyclic process divided into three phases [7]: (1) Forethought (also referred to as planning phase), (2) Performance (also referred to as learning phase), and (3) Self-Reflection. In addition to acquiring domain knowledge, the learner applies meta-cognitive activities when taking control over-and reflecting on learning. ...
... The planning phase in SRL includes strategic planning and goal setting ("Forethought Phase" [7]). According to [2] goal setting can be implemented by defining a set of competences which are expected to be achieved (competence goal) or by defining a set of problems which Figure 1: Learning Process consisting of n Learning Iterations a learner should be capable of solving. In our approach, the planning phase is supported by the selection of a predefined learning profile and an initial competence assessment. ...
Conference Paper
Full-text available
Adaptive learning systems aim to address a learner's specific needs, considering factors such as prior knowledge, learning efficiency, learning goals and motivation. Especially in distance education, often directed to adult learners with full-time jobs, it is very important to provide assistance to counteract high dropout rates. This paper describes an approach on how to support adult learners through the adoption of personalisation and guidance in Moodle. The implementation grounds on the combination of two pedagogical theories, competence-based learning and Self-Regulated Learning (SRL). Three-phases were used to roughly frame the design of the SRL learning flow, where the individual phases are supported by competence-based guidance. In this way Moodle is extended from a teacher and course management to a learner-centric system. This work has been implemented and evaluated in the course of a European project that targets vocational training of heat pump installers.
... It supports a variety of different learning object (LO) formats and question types (herein referred to as assessment items (AIs)). Despite these advantages, Moodle is course-based and does not cater for the individual needs of students [2]. However, its extensibility (plug-ins and modules) offers a promising possibility to introduce personalised learning support, which gives the learner more freedom to control their own learning process. ...
... While competence-based learning focuses on the subject domain level, SRL emphasises the learners' own ability to control and regulate each step of their learning process. A common SRL model regards learning as a cyclic process divided into three phases [7]: (1) Forethought (also referred to as planning phase), (2) Performance (also referred to as learning phase), and (3) Self-Reflection. In addition to acquiring domain knowledge, the learner applies meta-cognitive activities when taking control over-and reflecting on learning. ...
... The planning phase in SRL includes strategic planning and goal setting ("Forethought Phase" [7]). According to [2] goal setting can be implemented by defining a set of competences which are expected to be achieved (competence goal) or by defining a set of problems which Figure 1: Learning Process consisting of n Learning Iterations a learner should be capable of solving. In our approach, the planning phase is supported by the selection of a predefined learning profile and an initial competence assessment. ...
Conference Paper
Full-text available
Adaptive learning systems aim to address a learner's specific needs, considering factors such as prior knowledge, learning efficiency, learning goals and motivation. Especially in distance education, often directed to adult learners with full-time jobs, it is very important to provide assistance to counteract high dropout rates. This paper describes an approach on how to support adult learners through the adoption of personalisation and guidance in Moodle. The implementation grounds on the combination of two pedagogical theories, competence-based learning and Self-Regulated Learning (SRL). Three-phases were used to roughly frame the design of the SRL learning flow, where the individual phases are supported by competence-based guidance. In this way Moodle is extended from a teacher and course management to a learner-centric system. This work has been implemented and evaluated in the course of a European project that targets vocational training of heat pump installers.
... Other projects have analysed how the performance of a PLE can support the cognitive and meta-cognitive activities involved in self-regulation. The iClass 2 project offers a visual guide of the interface which mediates self-regulation on two levels: (2) it reflects the learner's intentions in regards to their studies; and (2) it provides feedback to the learner on the regulatory structures used in their studies (Albert, Nussbaumer, & Steiner, 2008;Türker & Zingel, 2008). Another project, called ROLE (Responsive Open Learning Environments) 3 , incorporates four support strategies into the development and maintenance of self-regulated learning skills: (1) recommendations for resources and learning activities; (2) an SRL Toolset for self-regulation activities, such as setting goals or time management; (3) feedback and display of the learner's performance during learning; and (4) an SRL Logbook or journal of selfregulation and self-reflection activities conducted throughout the process (Kroop, Berthold, Nussbaumer, & Albert, 2012;Nussbaumer, Albert, & Kirschenmann, 2011;Nussbaumer, Kravcik, & Albert, 2012). ...
Article
This article provides the foundation and describes the pedagogical and functional design of a PLE within the Just4Me project. This environment integrates tools and functionalities to support learning across and throughout life. The main goal of the pedagogical design is to encourage self-managed learning, regardless of whether it occurs in formal or informal settings. It aims to integrate learning experiences developed within different contexts.
Chapter
In the knowledge society, autonomous and Self-Directed Learning (SDL) have become particularly important for professional development and lifelong learning. This kind of learning can take place in physical and virtual spaces that may belong to formal institutions but also to extended communities and networks. In virtual spaces, self-directed learning and self-regulation skills and capacities play an important role in learners’ performance. For this reason, it is highly recommended to empower students to design and deploy educational spaces and projects able to fuse formal and informal contexts. The use of Personal Learning Environments (PLEs) can support learners to gain control over their experiences through Web-based tools and a task-orientated environment. It is known that time management is one relevant component of self-regulated learning. There are many Web-based tools that can be used to control time investment and promote planning but little research that takes into account time management in the design and use of PLEs. This chapter describes the results of the Just4me project1, aimed at designing and developing a PLE to support self-regulated learning dealing with time management as an important dimension in lifelong learning. From this perspective, this chapter contributes to the operationalization and analysis of the time factor in online learning regarding time management in self-regulated learning processes supported by PLEs.
Article
Full-text available
In recent years, various recommendation algorithms have been proposed to support learners in technology-enhanced learning environments. Such algorithms have proven to be quite effective in big-data learning settings (massive open online courses), yet successful applications in other informal and formal learning settings are rare. Common challenges include data sparsity, the lack of sufficiently flexible learner and domain models, and the difficulty of including pedagogical goals into recommendation strategies. Computational models of human cognition and learning are, in principle, well positioned to help meet these challenges, yet the effectiveness of cognitive models in educational recommender systems remains poorly understood to this date. This thesis contributes to this strand of research by investigating i) two cognitive learner models (CbKST and SUSTAIN) for resource recommendations that qualify for sparse user data by following theory-driven top down approaches, and ii) two tag recommendation strategies based on models of human cognition (BLL and MINERVA2) that support the creation of learning content meta-data. The results of four online and offline experiments in different learning contexts indicate that a recommendation approach based on the CbKST, a well-founded structural model of knowledge representation, can improve the users? perceived learning experience in formal learning settings. In informal settings, SUSTAIN, a human category learning model, is shown to succeed in representing dynamic, interest based learning interactions and to improve Collaborative Filtering for resource recommendations. The investigation of the two proposed tag recommender strategies underlined their ability to generate accurate suggestions (BLL) and in collaborative settings, their potential to promote the development of shared vocabulary (MINERVA2). This thesis shows that the application of computational models of human cognition holds promise for the design of recommender mechanisms and, at the same time, for gaining a deeper understanding of interaction dynamics in virtual learning systems.
Chapter
In the knowledge society, autonomous and Self-Directed Learning (SDL) have become particularly important for professional development and lifelong learning. This kind of learning can take place in physical and virtual spaces that may belong to formal institutions but also to extended communities and networks. In virtual spaces, self-directed learning and self-regulation skills and capacities play an important role in learners' performance. For this reason, it is highly recommended to empower students to design and deploy educational spaces and projects able to fuse formal and informal contexts. The use of Personal Learning Environments (PLEs) can support learners to gain control over their experiences through Web-based tools and a task-orientated environment. It is known that time management is one relevant component of self-regulated learning. There are many Web-based tools that can be used to control time investment and promote planning but little research that takes into account time management in the design and use of PLEs. This chapter describes the results of the Just4me project1, aimed at designing and developing a PLE to support self-regulated learning dealing with time management as an important dimension in lifelong learning. From this perspective, this chapter contributes to the operationalization and analysis of the time factor in online learning regarding time management in self-regulated learning processes supported by PLEs.
Chapter
A student is facing a teacher, who is probing his1 knowledge of high school mathematics. The student, a new recruit, is freshly arrived from a foreign country, and important questions must be answered. To which grade should the student be assigned? What are his strengths and weaknesses? Should the student take a remedial course in some subject? Which topics is he ready to learn? The teacher will ask a question and listen to the student’s response. Other questions will then be asked. After a few questions, a picture of the student’s state of knowledge will emerge, which will become increasingly sharper in the course of the examination.
Article
The paper explores some pedagogical affordances of machine-processable competency models. Self-assessment is a crucial component of learning. However, creating effective questions is time-consuming because it may require considerable resources and the skill of critical thinking. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. Using ontologies and Semantic Web technologies certain limitations in automation, integration, and reuse of data across diverse applications can be overcome. This paper presents a system for automatically generating questions from a competency framework. This novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including the level of cognitive ability and the structure of the subject matter. This makes it possible to guide learners in developing questions for themselves, and to provide authoring templates which speed the creation of new questions for self-assessment. The system generates a list of all the questions that are possible from a given learning outcome. Such learning outcomes were collected from the INFO1013 'IT Modeling' course at the University of Southampton. The way in which the system has been designed and evaluated is discussed, along with its educational benefits.
Book
Learning spaces offer a rigorous mathematical foundation for various practical systems of knowledge assessment. An example is offered by the ALEKS system (Assessment and LEarning in Knowledge Spaces), a software for the assessment of mathematical knowledge. From a mathematical standpoint, learning spaces as well as knowledge spaces (which made the title of the first edition) generalize partially ordered sets. They are investigated both from a combinatorial and a stochastic viewpoint. The results are applied to real and simulated data. The book gives a systematic presentation of research and extends the results to new situations. It is of interest to mathematically oriented readers in education, computer science and combinatorics at research and graduate levels. The text contains numerous examples and exercises, and an extensive bibliography. © Springer-Verlag Berlin Heidelberg 2011. All rights are reserved.
Article
Full-text available
This paper describes a competence -based approach to adaptive e-learning based on know ledge space theory and its competence-performance extension. Assigning to each learning object sets of required and taught competencies, one can easily build a course adapting in each step to the learner's current know- ledge state. The competence assignments implicitly define a prerequisite structure on the set of competencies. At the same time, a decentralised storage of these assignments allows (i) for easy changes of learning objects and courses in dynamically changing domains and (ii) for reuse of adaptive material in different courses by implementing the competence assignments through slightly extended metadata standards. An example implementation will be briefly presented.
Article
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.
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.
Conference Paper
Full-text available
This article describes a learner modeling strategy that is employed by an adaptive learning system in order to provide each learner with a personalized e-Learning experience. Parameters related to the learners' prior knowledge, goal and learning style constitute the basis of the personalization and the adaptivity of the mentioned learning system. The present paper focuses on the mechanisms concerning the learner's prior knowledge and the goal parameters. The learner modeling that takes into account the prior knowledge of the learners is achieved by developing an ontological abstraction. Based on this ontological abstraction, a knowledge base is constructed in order to introduce the knowledge representations of the domain model and the curricular model, the knowledge and the learning structures. These knowledge representations specify how the prior knowledge of a learner will be represented, and also how it will be assessed and continuously updated.
Article
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.
Conference Paper
We have developed a set of visualisations mirroring the activity of small teams engaged in a task. These provide a bird's-eye view of what is happening in a small team, giving insights into the way that each individual is contributing to the group and the ways that team members interact with each other. We report on our first experience of using these visualisations for a semester-long software development project course. The study revealed that students, especially those with leadership roles, found the visualizations informative and helpful and that over a third of students modified their behaviour accordingly.