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Adaptive e-Learning and the Learning Grid

Adaptive e-Learning and the Learning Grid
Cord Hockemeyer and Dietrich Albert
Department of Psychology, University of Graz, Austria
One important aim of LeGE-WG is the integration of new eLearning methodologies
into Learning Grid technology. A central issue in these new eLearning
methodologies is the concept of individualised and personalised learning to be
realised by adaptive tutoring systems. The adaptivity of such systems goes far
beyond adapting to the users’ preferences with respect to the user interface; in co-
operation between computer science, psychology, and pedagogy, systems
adapting, e.g., to the individual learners’ current knowledge, cultural background,
learning style, or special needs are developed.
Adaptive tutoring systems can be integrated into a Learning Grid at different levels
of ambitiousness. We will discuss these different levels of integration based on our
prior experiences with respect to reusability of adaptive learning resources.
Keywords: adaptive eLearning, Learning Grid, knowledge structures, competencies.
Adaptive and personalised hypermedia systems have been of increasing interest in recent
research and development in eLearning [1,2]. Integrating adaptive techniques enriches
eLearning systems by adding psychological and pedagogical findings about knowledge and
learning to technical systems.
Connecting adaptive techniques – and the underlying models of learning and knowledge – with
Grid technology brings up new challenges, e.g. in the area of interoperability, but also new
In the following, we will first discuss adaptivity within eLearning in general as well as the
knowledge space based approach pursued in our research group. Subsequently, we will
discuss the issue of interoperability of adaptive eLearning systems based on our previous
experiences. Finally, we look at different levels of integrating adaptive eLearning into a Learning
Using the computer for education allows – comparable to private teachers in former times – a
personalised, adaptive learning, i.e. the teaching system adapts the selection and the
presentation of contents to the individual learner and their learning status, their needs and
preferences (see, e.g., [3]).
Within the EASEL project, many different types of adaptivity have been identified focusing on
objects and objectives of adaptivity [4]. Objects of adaptivity can be, e.g., the selection of
learning objects, their presentation, or the choice of input methods and devices. Objectives of
adaptivity can be, e.g., the learners’ pre-knowledge, their curriculum and aimed knowledge
state, special needs, learning styles, or cognitive styles.
Our research group focuses on adaptivity to learners’ current knowledge based on the theory of
knowledge spaces [5,6]. Knowledge space theory is a psychological model for structuring
domains of knowledge based on prerequisite relationships between content objects. From these
structures, learning paths are derived.
The relational formalisation of this model allows an easy transfer to computer systems,
especially to relational databases. Thus, it can easily be applied to adaptive eLearning [7].
1st LEGE-WG international workshop on e-Learning and GRID Technologies:
Educational models for GRID based services. 1
Adaptive e-Learning and the Learning Grid
Currently, there are two adaptive eLearning systems on the Web which are based on this
theory, RATH [8] and ALEKS (see [6]).
More recent extensions of knowledge space theory distinguish between concrete learning
objects or observable performances on the one side and underlying skills or competencies on
the other side (see, e.g., [9-11]). These extensions also use mathematical formalisations, thus
they can be applied for adaptive eLearning easily [12]. Such an application is discussed in the
context of interoperability in the next section.
Reusability and interoperability of computer based learning resources have received an
increasing interest in recent years. This can, for instance, be seen in the development of
standards and standardisation organisations in this area (see, e.g., [13,14]). However, all these
specifications are oriented towards static material. One aim of the EASEL project in the FP5 IST
programme was to extend existing interoperability standards to cover also adaptive features of
learning resources [15].
In a first step, an extension to learning object metadata (LOM) standards was developed that
allows a generic description of adaptive features of the material, i.e. this adaptivity element is
not limited to specific types of adaptivity [16,17]. These metadata specifications are primarily
used for search and retrieval but they can also be used by the eLearning system for realising
the adaptivity itself [18].
The second step deals with interoperability between material and learning management system
(LMS) directly. Adaptive material often needs to store and retrieve more information about the
learner than static material. Based on AICC/SCORM [19,20] standards on such information
exchange, a set of information usually needed by adaptive resources has been defined that can
be stored in and retrieved from the LMS through the SCORM API [21].
Based on these developments, the APeLS system was developed which is based on knowledge
space theory extended by the competencies approach. This system is ready to be used from an
LMS which then also does user identification and similar administrative tasks [18].
The developments described in the previous sections provide already a large step for the
integration of adaptive material and adaptive services into a Learning Grid. However, the
integration can be performed at different levels.
At a minimal level, the Grid provides only user authentification while all other functionality is kept
within an independent adaptive server.
In a second step toward higher integration, also other information about the learner are stored
within the Grid and are thus exchanged between different adaptive servers belonging to the
same Grid.
While in the aforementioned levels adaptivity is basically still provided through a self-contained
adaptive server, the third level of integration starts realising adaptivity in a distributed service
through content repositories distributed over the Grid while the core adaptive system is still
located at one server.
Finally, adaptivity itself could be realised in a distributed way through co-operation of different
adaptive servers and services over the Grid. Methods for such distributed adaptive services
have yet to be developed. However, integrating adaptivity into Learning Grid technology at such
a high level promises to bring forward also a higher level of adaptivity than it could be realised
on stand-alone services.
1st LEGE-WG international workshop on e-Learning and GRID Technologies:
Educational models for GRID based services. 2
Adaptive e-Learning and the Learning Grid
1st LEGE-WG international workshop on e-Learning and GRID Technologies:
Educational models for GRID based services. 3
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hypertext WWW--environment based on knowledge space theory, in Christer Alvegård, Eds,
CALISCE`98: Proceedings of the Fourth International Conference on Computer Aided Learning
in Science and Engineering, pp. 417-423, Göteborg, Sweden. Chalmers University of
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[12] Hockemeyer, C., (2002) Extending the Competence-Performance-Approach for Building
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[13] IEEE Learning Technology Standards Committee (LTSC), .
[14] IMS Global Learning Consortium Inc., .
[15] EASEL (Educator Access to Services in the Electronic Landscape), EC Grant IST-1999-
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educational metadata schemas to describe adaptive learning resources, in Hugh Davies,
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in Education ICCE/SchoolNet2001, volume 1, pp. 205-210, 2001.
[18] Conlan, O., Hockemeyer, C., Wade, V., and Albert. D. Metadata driven approaches to
facilitate adaptivity in personalized eLearning systems. International Journal on Information
Systems in Education, to appear.
[19] Aviation Industry CBT Committee (AICC), .
[20] SCORM, Sharable Content Object Reference Model, (January 2001) Version 1.1,
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[21] Conlan, O., Hockemeyer, C., Wade, V., and Albert, D (2002) An architecture for integrating
adaptive hypermedia service with open learning environments, in Proceedings of ED-MEDIA,
World Conference on Educational Multimedia, Hypermedia, & Telecommunications.
... An authoring application for creating personalized role playing simulations would enable authors to create re-usable elearning resources with a view to enhancing students' retention of specific concepts and improvement in learning. Hockemeyer and Albert [9] recommend that personalized technology enhanced learning resources would effectively enable reusability. ...
Full-text available
The human computer interaction issues associated with the creation of personalized role playing simulations are discussed in this paper. This paper is aimed at those who are interested in building authoring applications which enable educators to build role playing simulated e-learning resources to use with their students. One of the main issues which have come to our attention is that many learning designers and educators do not understand what exactly it is we are trying to achieve by creating personalized role playing simulations. Also, how to gauge the pedagogic merits which can be achieved by using these e-learning resources. Potential users require guidance on the most appropriate uses for this authoring application. The provision of exemplars of use of such personalized e-learning activities would assist potential users in creating their own role playing simulations. Other issues which are to be addressed in authoring applications for creating personalized e-learning activities are: documentation; training materials; preview mechanisms; integration; usability; and the use of clear and relevant terminology. Human acceptance is paramount to the effective use of educational software which is designed to facilitate the creation of personalized e-learning resources. In conclusion, if the realization of an authoring application for creating personalized role playing simulations is to be achieved the following issues must be resolved: relevance to the learning experience; efficiency in production; and improvements in the human computer interaction.
Full-text available
Personalized eLearning Systems tailor the learning experience to characteristics of individual learners. These tailored course offerings are often comprised of discrete electronic learning resources, such as text snippets, interactive animations, diagrams, and videos. An extension of standard metadata schemas developed for facilitating the discovery and reuse of such adaptive learning resources can also be utilized by the eLearning systems for realizing the adaptivity. An important feature of such reuse supporting adaptive systems is the clear distinction of separate models and components within the teaching process.
Full-text available
Knowledge space theory provides a formal model for representing students' knowledge and describing the structure of a domain of knowledge. A similar formal structure can be used to described the structure of hypertexts. The combination of knowledge space theory and the formal hypertext model leads to a framework for intelligent tutoring systems which provides individualized learning paths to a student. Using powerful procedures from relational database systems and from knowledge space theory, we get e. g. an efficient selection of appropriate teaching documents. 1 Introduction and previous results Doignon and Falmagne [1] introduced the theory of knowledge spaces which provides a mean to formally describe the structure of a given domain of knowledge. We introduce the theoretical concepts in Section 1.1 below. The basic idea is the description of a student's knowledge by the set of problems (items) he or she is able to solve. The set of possible knowledge states is restricted by prereq...
Suppose that Q is a set of problems and S is a set of skills. A skill function assigns to each problem q i.e. to each element of Q — those sets of skills which are minimally sufficient to solve q; a problem function assigns to each set X of skills the set of problems which can be solved with these skills (a knowledge state). We explore the natural properties of such functions and show that these concepts are basically the same. Furthermore, we show that for every family K of subsets of Q which includes the empty set and Q, there are a set S of (abstract) skills and a problem function whose range is just K. We also give a bound for the number of skills needed to generate a specific set of knowledge states, and discuss various ways to supply a set of knowledge states with an underlying skill theory. Finally, a procedure is described to determine a skill function using coverings in partial orders which is applied to set A of the Coloured Progressive Matrices test (Raven, 1965).
The information regarding a particular field of knowledge is conceptualized as a large, specified set of questions (or problems). The knowledge state of an individual with respect to that domain is formalized as the subset of all the questions that this individual is capable of solving. A particularly appealing postulate on the family of all possible knowledge states is that it is closed under arbitrary unions. A family of sets satisfying this condition is called a knowledge space. Generalizing a theorem of Birkhoff on partial orders, we show that knowledge spaces are in a one-to-one correspondence with AND/OR graphs of a particular kind. Two types of economical representations of knowledge spaces are analysed: bases, and Hasse systems, a concept generalizing that of a Hasse diagram of a partial order. The structures analysed here provide the foundation for later work on algorithmic procedures for the assessment of knowledge.
We have learned from Theorem 2.2.4 that any learning space is a knowledge space, that is, a knowledge structure closed under union. The ∪-closure property is critical for the following reason. Certain knowledge spaces, and in particular the finite ones, can be faithfully summarized by a subfamily of their states. To wit, any state of the knowledge space can be generated by forming the union of some states in the subfamily. When such a subfamily exists and is minimal for inclusion, it is unique and is called the ‘base’ of the knowledge space. In some cases, the base can be considerably smaller than the knowledge space, which results in a substantial economy of storage in a computer memory. The extreme case is the power set of a set of n elements, where the 2n knowledge states can be subsumed by the family of the n singleton sets. This property inspires most of this chapter, beginning with the basic concepts of ‘base’ and ‘atoms’ in Sections 3.4 to 3.6. Other features of knowledge spaces are also important, however, and are dealt with in this chapter.
The knowledge structures theory developed by Doignon & Falmagne is a purely descriptive approach to the representation of knowledge and is free of any cognitive interpretation. The aim of this paper is to show one possible way in which this theory can be reconciled with traditional explanatory features of knowledge assessment by extending it to a competence-performance conception. Performance is conceived as the observable solution behavior of a person on a set of domain-specific problems, whereas competence (ability, skills) is understood as a theoretical construct explaining performance. The basic concept is a mathematical structure termed a diagnostic, that creates a relationship between a family of competence states and a family of performance states. A diagnostic is said to be a union-stable diagnostic, when the family of competence states as well as the family of performance states is union-stable and when there exists a union-preserving function that maps the set of competence states onto the set of performance states. Several properties of union-stable diagnostics are presented. An empirical investigation is reported which illustrates the practical application of union-stable diagnostics. Finally, some benefits and problems of the introduced modeling approach are discussed.
RATH ---a relational adaptive tutoring hypertext WWW--environment based on knowledge space theory
  • C Hockemeyer
  • T Held
Hockemeyer, C., Held, T., and Albert, D. (June 19980 RATH ---a relational adaptive tutoring hypertext WWW--environment based on knowledge space theory, in Christer Alvegård, Eds, CALISCE`98: Proceedings of the Fourth International Conference on Computer Aided Learning in Science and Engineering, pp. 417-423, Göteborg, Sweden. Chalmers University of Technology.