Please, cite this publication as: Burgos, D., & Specht, M. (2006). Adaptive e-learning methods and IMS Learning Design. In Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson & W. Didderen (Eds.), Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies (pp. 1192-1193). July, 5-7, 2006, Kerkrade, The Netherlands.This position paper shows how several classical methods in adaptive learning can be addressed using IMS Learning Design. After a definition of four main questions to classify adaptive educational methods we describe a group of features in the Levels B and C of the specification that make possible diverse types of adaptation.ProLearn Projec

Burgos, Daniel

Specht, Marcus

This position paper shows how several classical methods in adaptive learning can be addressed using IMS Learning Design. After a definition of four main questions to classify adaptive educational methods we describe a group of features in the Levels B and C of the specification that make possible diverse types of adaptation

Open University of the Netherlands Research Portal

Open Universiteit www.ou.nl Adaptive e-learning methods and IMS Learning Design.An integrated approachCitation for published version (APA):Burgos, D., & Specht, M. (2006). Adaptive e-learning methods and IMS Learning Design. An integratedapproach.Document status and date:Published: 18/09/2006Document Version:Peer reviewed versionDocument license:CC BY-NC-NDPlease check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences betweenthe submitted version and the official published version of record. People interested in the research are advised to contact the author for thefinal version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain• You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:https://www.ou.nl/taverne-agreementTake down policyIf you believe that this document breaches copyright please contact us at:pure-support@ou.nlproviding details and we will investigate your claim.Downloaded from https://research.ou.nl/ on date: 29 Nov. 2023Adaptive e-learning methods and IMS Learning Design. An integrated approach   Daniel Burgos, Marcus Specht Open University of the Netherlands PO BOX 2690,  6401 DL Heerlen, The Netherlands { daniel.burgos,marcus.specht}@ou.nl   Abstract  This position paper shows how several classical methods in adaptive learning can be addressed using IMS Learning Design. After a definition of four main questions to classify adaptive educational methods we describe a group of features in the Levels B and C of the specification that make possible diverse types of adaptation.   1. Introduction and adaptive methods  In adaptive educational hypermedia a variety of research works about questions on how to adapt curricula and learning content to individuals and groups of learners has been done [1]. The application of adaptive methods to educational hypermedia applications can mainly be structured according to four main questions: What parts or components of the learning process are adapted? This question focuses on the part of the application that is adapted by the adaptive method. Examples can be the pace of the instruction [2] that can be modified based on diagnostic modules embedded in the learning process or adaptation of content presentations, the sequencing of contents and others. Extensions with new forms of information delivery allow the distribution of learning materials to different learning contexts relevant to the individual user or groups of users. What information does the system use for adaptation? In most adaptive educational hypermedia applications a learner model is the basis for the adaptation of the previously given parameters of the learning process. Nevertheless there are a several examples where the adaptation takes place not only to the learner knowledge, preferences, interests, cognitive capabilities, but also to tasks and learner goals. How does the system gather the information to adapt to? There are a variety of methods to collect information about learners to adapt to. Mainly implicit and explicit methods like described in works from user modeling can be distinguished. A overview can be found at Jameson [3]. Why does the system adapt? This question mainly focuses on the pedagogical models behind the adaptation [4]. Classical educational hypermedia system mainly adapted according for compensation of knowledge deficits, ergonomic reasons, or adaptations to learning styles for an easier introduction into a topic. Furthermore examples for adaptive methods can be found in different research areas as Intelligent Tutoring Systems [5], Adaptive User Interfaces [6], Adaptive Hypermedia [7], Intelligent Multimedia or Intelligent Agents [8] for Learning. Examples found in the literature can be mostly classified to the scheme introduced above. The following section will give an overview with some examples. In the following we will pick out some examples and discuss the possibilities to implement them in IMS Learning Design [9].  2. IMS LD and adaptive e-learning  IMS LD consists of three levels [10]. Each level itself provides specific features to the educational information pack, called Unit of Learning. Furthermore, Level A provides method, plays, acts, roles, role-parts, learning activities, support activities and environments; Level B provides properties, conditions, calculations, monitoring services and global elements; and Level C provides notifications. Every level is built on the previous one.  Besides the basic and crucial structure provided by Level A, the elements of Levels B and C become the actual key for adaptation, as they combine properties with conditions and other features that encourage and make more flexible the content and the learning flow. IMS LD is able to carry out six main types of adaptation [11]: Learning flow based, content based, interactive problem solving support, adaptive user grouping, adaptive evaluation and changes in run-time. They are also useful to address complementary issues to adaptive learning, like active learning, collaborative learning, dynamic feedback, run-time tracking, ePortfolios and assessment [12]. All these types of adaptation and specific applications to educational purposes make an intensive use of several features in Levels B and C, as we describe in the next section.  3. Elements in Levels B and C to model adaptive methods  The elements in Level B providing support to adaptation in Units of Learning are categorized as a) properties, b) conditions, c) global elements, d) calculations and e) monitoring services: a) Definition, set-up and use of properties: Properties are taken as variables to store values. There are several types of properties: local, local-personal, local-role, global-personal, global. There is also a property-group that is able to compile a number of the others. b) Conditions: IMS LD is able to define a basic structure if-then-else, or multiple structure with several chained basic if-then-else in a row, for instance to change the value of a property or to show and hide one element. c) Global elements: Global elements provide a communication flow between the imsmanifest.xml, where the different levels of IMS LD are set-up, and other XML files. Mainly, they can get an input from the user and they can show a value of a property. Furthermore, they can manage DIV layers in XHTML, for instance to show and hide specific content. d) Calculations: IMS LD is able to make some basic arithmetic’s (sum, subtraction, multiplication and division) and some combination of a number of them in a row, to get a more complex formula, like a simple average, for instance. e) Monitoring service: The specification allows monitoring any kind of property assigned to a user or a role, for instance. In order to start this action, firstly the component monitor must be set-up inside an environment and later the property can also be monitored. f) Notifications: An action is automatically launched depending on the state of a property or a previous action, i.e., when a student ends an assignment an email is sent to the tutor. 4. Conclusion  IMS LD enables a standardized way for designing personalized learning experiences. The combination of Properties, Conditions, Global Elements, Calculations and Monitoring services allows  modelling a variety of classical adaptive methods mainly based on environment, content, user groups and learning flow, such as reuse of pedagogical patterns, adaptability, navigational guidance, collaborative learning, contextualized and mobile distributed learning, adaptation to stereotypes. The forthcoming research on Level B and Level C aims to provide new expressive ways of modelling traditional adaptative e-learning.  5. References  [1] M. Specht. “Adaptive Methoden in computerbasierten Lehr/Lernsystemen” (Vol. 1). University of Trier, 1998. [2] D. Leutner. Adaptive Lehrsysteme; Instruktionspsychologische Grundlagen und experimentelle Analysen. Weinheim: Beltz, 1992. [3] A. Jameson. Systems that adapt to their users: An integrative Overview. Paper presented at the Tutorial presented at 9th International Conference on User Modelling, Johnstown, PA, USA, 2003. [4] G. Salomon. Heuristische Modelle für die Gewinnung von Interaktionshypothesen. In R. Schwarzer (Ed.), Adaptiver Unterricht. München: Kösel, 1975, pp. 127-145. [5] J.R. Anderson, F.G. Conrad & A.T. Corbett. Skill acquisition and the LISP tutor. Cognitive Science, 13. 1989. [6] P. Brusilovsky, M. Specht & G. Weber. Towards adaptive learning environments. In F. Huber-Wäschle, H. Schauer & P. Widmayer (Eds.), Herausforderungen eines globalen Informationsverbundes für die Informatik: GISI 95. Berlin: Springer-Verlag, 1995, pp.322-329. [7] P. Brusilovsky. Methods and techniques of adaptive hypermedia. User Models and User Adapted Interaction, 6, 1996. [8] J. Rickel & L.W. Johnson. Integrating Paedagogical Agents in a virtual Environment for Training. to appear in the journal Presence, 1997. [9] IMS. Learning Design Specification, from http://www.imsglobal.org/learningdesign/index.html, 2003. [10] R. Koper & C. Tattersall (Eds.). Learning Design: A handbook on modelling and delivering networked education and training. Springer, 2005. [11] D. Burgos, C. Tattersall & R. Koper. Representing adaptive eLearning strategies in IMS Learning Design. Paper presented at the TENCompetence Conference, Sofia, Bulgaria, March, 2006. [12] R. Koper & D. Burgos. Designing Learning Activities: From Content-based to Context-based Learning Services. International Journal on Advanced Technology for Learning, 2(3), 2005.  

English

Adaptive e-learning methods and IMS Learning Design. An integrated approach

NARCIS 

DSpace at Open Universiteit Nederland

Adaptive e-learning methods and IMS Learning Design. An integrated 
approach 
 
 
Daniel Burgos, Marcus Specht 
Open University of the Netherlands 
PO BOX 2690,  6401 DL Heerlen, The Netherlands 
{ daniel.burgos,marcus.specht}@ou.nl 
 
 
Abstract 
 
This position paper shows how several classical 
methods in adaptive learning can be addressed using 
IMS Learning Design. After a definition of four main 
questions to classify adaptive educational methods we 
describe a group of features in the Levels B and C of 
the specification that make possible diverse types of 
adaptation.  
 
1. Introduction and adaptive methods 
 
In adaptive educational hypermedia a variety of 
research works about questions on how to adapt 
curricula and learning content to individuals and 
groups of learners has been done [1]. The application 
of adaptive methods to educational hypermedia 
applications can mainly be structured according to four 
main questions: 
What parts or components of the learning process 
are adapted? This question focuses on the part of the 
application that is adapted by the adaptive method. 
Examples can be the pace of the instruction [2] that 
can be modified based on diagnostic modules 
embedded in the learning process or adaptation of 
content presentations, the sequencing of contents and 
others. Extensions with new forms of information 
delivery allow the distribution of learning materials to 
different learning contexts relevant to the individual 
user or groups of users. 
What information does the system use for 
adaptation? In most adaptive educational hypermedia 
applications a learner model is the basis for the 
adaptation of the previously given parameters of the 
learning process. Nevertheless there are a several 
examples where the adaptation takes place not only to 
the learner knowledge, preferences, interests, cognitive 
capabilities, but also to tasks and learner goals. 
How does the system gather the information to 
adapt to? There are a variety of methods to collect 
information about learners to adapt to. Mainly implicit 
and explicit methods like described in works from user 
modeling can be distinguished. A overview can be 
found at Jameson [3]. 
Why does the system adapt? This question mainly 
focuses on the pedagogical models behind the 
adaptation [4]. Classical educational hypermedia 
system mainly adapted according for compensation of 
knowledge deficits, ergonomic reasons, or adaptations 
to learning styles for an easier introduction into a topic. 
Furthermore examples for adaptive methods can be 
found in different research areas as Intelligent Tutoring 
Systems [5], Adaptive User Interfaces [6], Adaptive 
Hypermedia [7], Intelligent Multimedia or Intelligent 
Agents [8] for Learning. Examples found in the 
literature can be mostly classified to the scheme 
introduced above. The following section will give an 
overview with some examples. In the following we 
will pick out some examples and discuss the 
possibilities to implement them in IMS Learning 
Design [9]. 
 
2. IMS LD and adaptive e-learning 
 
IMS LD consists of three levels [10]. Each level 
itself provides specific features to the educational 
information pack, called Unit of Learning. 
Furthermore, Level A provides method, plays, acts, 
roles, role-parts, learning activities, support activities 
and environments; Level B provides properties, 
conditions, calculations, monitoring services and 
global elements; and Level C provides notifications. 
Every level is built on the previous one.  
Besides the basic and crucial structure provided by 
Level A, the elements of Levels B and C become the 
actual key for adaptation, as they combine properties 
with conditions and other features that encourage and 
make more flexible the content and the learning flow. 
IMS LD is able to carry out six main types of 
adaptation [11]: Learning flow based, content based, 
interactive problem solving support, adaptive user 
grouping, adaptive evaluation and changes in run-
time. They are also useful to address complementary 
issues to adaptive learning, like active learning, 
collaborative learning, dynamic feedback, run-time 
tracking, ePortfolios and assessment [12]. All these 
types of adaptation and specific applications to 
educational purposes make an intensive use of several 
features in Levels B and C, as we describe in the next 
section. 
 
3. Elements in Levels B and C to model 
adaptive methods 
 
The elements in Level B providing support to 
adaptation in Units of Learning are categorized as a) 
properties, b) conditions, c) global elements, d) 
calculations and e) monitoring services: 
a) Definition, set-up and use of properties: 
Properties are taken as variables to store values. There 
are several types of properties: local, local-personal, 
local-role, global-personal, global. There is also a 
property-group that is able to compile a number of the 
others. 
b) Conditions: IMS LD is able to define a basic 
structure if-then-else, or multiple structure with several 
chained basic if-then-else in a row, for instance to 
change the value of a property or to show and hide one 
element. 
c) Global elements: Global elements provide a 
communication flow between the imsmanifest.xml, 
where the different levels of IMS LD are set-up, and 
other XML files. Mainly, they can get an input from 
the user and they can show a value of a property. 
Furthermore, they can manage DIV layers in XHTML, 
for instance to show and hide specific content. 
d) Calculations: IMS LD is able to make some 
basic arithmetic’s (sum, subtraction, multiplication and 
division) and some combination of a number of them 
in a row, to get a more complex formula, like a simple 
average, for instance. 
e) Monitoring service: The specification allows 
monitoring any kind of property assigned to a user or a 
role, for instance. In order to start this action, firstly 
the component monitor must be set-up inside an 
environment and later the property can also be 
monitored. 
f) Notifications: An action is automatically 
launched depending on the state of a property or a 
previous action, i.e., when a student ends an 
assignment an email is sent to the tutor. 
4. Conclusion 
 
IMS LD enables a standardized way for designing 
personalized learning experiences. The combination of 
Properties, Conditions, Global Elements, Calculations 
and Monitoring services allows  modelling a variety of 
classical adaptive methods mainly based on 
environment, content, user groups and learning flow, 
such as reuse of pedagogical patterns, adaptability, 
navigational guidance, collaborative learning, 
contextualized and mobile distributed learning, 
adaptation to stereotypes. The forthcoming research on 
Level B and Level C aims to provide new expressive 
ways of modelling traditional adaptative e-learning. 
 
5. References 
 
[1] M. Specht. “Adaptive Methoden in computerbasierten 
Lehr/Lernsystemen” (Vol. 1). University of Trier, 1998. 
[2] D. Leutner. Adaptive Lehrsysteme; 
Instruktionspsychologische Grundlagen und experimentelle 
Analysen. Weinheim: Beltz, 1992. 
[3] A. Jameson. Systems that adapt to their users: An 
integrative Overview. Paper presented at the Tutorial 
presented at 9th International Conference on User Modelling, 
Johnstown, PA, USA, 2003. 
[4] G. Salomon. Heuristische Modelle für die Gewinnung 
von Interaktionshypothesen. In R. Schwarzer (Ed.), 
Adaptiver Unterricht. München: Kösel, 1975, pp. 127-145. 
[5] J.R. Anderson, F.G. Conrad & A.T. Corbett. Skill 
acquisition and the LISP tutor. Cognitive Science, 13. 1989. 
[6] P. Brusilovsky, M. Specht & G. Weber. Towards 
adaptive learning environments. In F. Huber-Wäschle, H. 
Schauer & P. Widmayer (Eds.), Herausforderungen eines 
globalen Informationsverbundes für die Informatik: GISI 95. 
Berlin: Springer-Verlag, 1995, pp.322-329. 
[7] P. Brusilovsky. Methods and techniques of adaptive 
hypermedia. User Models and User Adapted Interaction, 6, 
1996. 
[8] J. Rickel & L.W. Johnson. Integrating Paedagogical 
Agents in a virtual Environment for Training. to appear in 
the journal Presence, 1997. 
[9] IMS. Learning Design Specification, from 
http://www.imsglobal.org/learningdesign/index.html, 2003. 
[10] R. Koper & C. Tattersall (Eds.). Learning Design: A 
handbook on modelling and delivering networked education 
and training. Springer, 2005. 
[11] D. Burgos, C. Tattersall & R. Koper. Representing 
adaptive eLearning strategies in IMS Learning Design. Paper 
presented at the TENCompetence Conference, Sofia, 
Bulgaria, March, 2006. 
[12] R. Koper & D. Burgos. Designing Learning Activities: 
From Content-based to Context-based Learning Services. 
International Journal on Advanced Technology for Learning, 
2(3), 2005. 
 


Open Universiteit www.ou.nl Adaptive e-learning methods and IMS Learning Design.An integrated approachCitation for published version (APA):Burgos, D., & Specht, M. (2006). Adaptive e-learning methods and IMS Learning Design. An integratedapproach.Document status and date:Published: 18/09/2006Document Version:Peer reviewed versionDocument license:CC BY-NC-NDPlease check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences betweenthe submitted version and the official published version of record. People interested in the research are advised to contact the author for thefinal version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain• You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:https://www.ou.nl/taverne-agreementTake down policyIf you believe that this document breaches copyright please contact us at:pure-support@ou.nlproviding details and we will investigate your claim.Downloaded from https://research.ou.nl/ on date: 15 Jul. 2023Adaptive e-learning methods and IMS Learning Design. An integrated approach   Daniel Burgos, Marcus Specht Open University of the Netherlands PO BOX 2690,  6401 DL Heerlen, The Netherlands { daniel.burgos,marcus.specht}@ou.nl   Abstract  This position paper shows how several classical methods in adaptive learning can be addressed using IMS Learning Design. After a definition of four main questions to classify adaptive educational methods we describe a group of features in the Levels B and C of the specification that make possible diverse types of adaptation.   1. Introduction and adaptive methods  In adaptive educational hypermedia a variety of research works about questions on how to adapt curricula and learning content to individuals and groups of learners has been done [1]. The application of adaptive methods to educational hypermedia applications can mainly be structured according to four main questions: What parts or components of the learning process are adapted? This question focuses on the part of the application that is adapted by the adaptive method. Examples can be the pace of the instruction [2] that can be modified based on diagnostic modules embedded in the learning process or adaptation of content presentations, the sequencing of contents and others. Extensions with new forms of information delivery allow the distribution of learning materials to different learning contexts relevant to the individual user or groups of users. What information does the system use for adaptation? In most adaptive educational hypermedia applications a learner model is the basis for the adaptation of the previously given parameters of the learning process. Nevertheless there are a several examples where the adaptation takes place not only to the learner knowledge, preferences, interests, cognitive capabilities, but also to tasks and learner goals. How does the system gather the information to adapt to? There are a variety of methods to collect information about learners to adapt to. Mainly implicit and explicit methods like described in works from user modeling can be distinguished. A overview can be found at Jameson [3]. Why does the system adapt? This question mainly focuses on the pedagogical models behind the adaptation [4]. Classical educational hypermedia system mainly adapted according for compensation of knowledge deficits, ergonomic reasons, or adaptations to learning styles for an easier introduction into a topic. Furthermore examples for adaptive methods can be found in different research areas as Intelligent Tutoring Systems [5], Adaptive User Interfaces [6], Adaptive Hypermedia [7], Intelligent Multimedia or Intelligent Agents [8] for Learning. Examples found in the literature can be mostly classified to the scheme introduced above. The following section will give an overview with some examples. In the following we will pick out some examples and discuss the possibilities to implement them in IMS Learning Design [9].  2. IMS LD and adaptive e-learning  IMS LD consists of three levels [10]. Each level itself provides specific features to the educational information pack, called Unit of Learning. Furthermore, Level A provides method, plays, acts, roles, role-parts, learning activities, support activities and environments; Level B provides properties, conditions, calculations, monitoring services and global elements; and Level C provides notifications. Every level is built on the previous one.  Besides the basic and crucial structure provided by Level A, the elements of Levels B and C become the actual key for adaptation, as they combine properties with conditions and other features that encourage and make more flexible the content and the learning flow. IMS LD is able to carry out six main types of adaptation [11]: Learning flow based, content based, interactive problem solving support, adaptive user grouping, adaptive evaluation and changes in run-time. They are also useful to address complementary issues to adaptive learning, like active learning, collaborative learning, dynamic feedback, run-time tracking, ePortfolios and assessment [12]. All these types of adaptation and specific applications to educational purposes make an intensive use of several features in Levels B and C, as we describe in the next section.  3. Elements in Levels B and C to model adaptive methods  The elements in Level B providing support to adaptation in Units of Learning are categorized as a) properties, b) conditions, c) global elements, d) calculations and e) monitoring services: a) Definition, set-up and use of properties: Properties are taken as variables to store values. There are several types of properties: local, local-personal, local-role, global-personal, global. There is also a property-group that is able to compile a number of the others. b) Conditions: IMS LD is able to define a basic structure if-then-else, or multiple structure with several chained basic if-then-else in a row, for instance to change the value of a property or to show and hide one element. c) Global elements: Global elements provide a communication flow between the imsmanifest.xml, where the different levels of IMS LD are set-up, and other XML files. Mainly, they can get an input from the user and they can show a value of a property. Furthermore, they can manage DIV layers in XHTML, for instance to show and hide specific content. d) Calculations: IMS LD is able to make some basic arithmetic’s (sum, subtraction, multiplication and division) and some combination of a number of them in a row, to get a more complex formula, like a simple average, for instance. e) Monitoring service: The specification allows monitoring any kind of property assigned to a user or a role, for instance. In order to start this action, firstly the component monitor must be set-up inside an environment and later the property can also be monitored. f) Notifications: An action is automatically launched depending on the state of a property or a previous action, i.e., when a student ends an assignment an email is sent to the tutor. 4. Conclusion  IMS LD enables a standardized way for designing personalized learning experiences. The combination of Properties, Conditions, Global Elements, Calculations and Monitoring services allows  modelling a variety of classical adaptive methods mainly based on environment, content, user groups and learning flow, such as reuse of pedagogical patterns, adaptability, navigational guidance, collaborative learning, contextualized and mobile distributed learning, adaptation to stereotypes. The forthcoming research on Level B and Level C aims to provide new expressive ways of modelling traditional adaptative e-learning.  5. References  [1] M. Specht. “Adaptive Methoden in computerbasierten Lehr/Lernsystemen” (Vol. 1). University of Trier, 1998. [2] D. Leutner. Adaptive Lehrsysteme; Instruktionspsychologische Grundlagen und experimentelle Analysen. Weinheim: Beltz, 1992. [3] A. Jameson. Systems that adapt to their users: An integrative Overview. Paper presented at the Tutorial presented at 9th International Conference on User Modelling, Johnstown, PA, USA, 2003. [4] G. Salomon. Heuristische Modelle für die Gewinnung von Interaktionshypothesen. In R. Schwarzer (Ed.), Adaptiver Unterricht. München: Kösel, 1975, pp. 127-145. [5] J.R. Anderson, F.G. Conrad & A.T. Corbett. Skill acquisition and the LISP tutor. Cognitive Science, 13. 1989. [6] P. Brusilovsky, M. Specht & G. Weber. Towards adaptive learning environments. In F. Huber-Wäschle, H. Schauer & P. Widmayer (Eds.), Herausforderungen eines globalen Informationsverbundes für die Informatik: GISI 95. Berlin: Springer-Verlag, 1995, pp.322-329. [7] P. Brusilovsky. Methods and techniques of adaptive hypermedia. User Models and User Adapted Interaction, 6, 1996. [8] J. Rickel & L.W. Johnson. Integrating Paedagogical Agents in a virtual Environment for Training. to appear in the journal Presence, 1997. [9] IMS. Learning Design Specification, from http://www.imsglobal.org/learningdesign/index.html, 2003. [10] R. Koper & C. Tattersall (Eds.). Learning Design: A handbook on modelling and delivering networked education and training. Springer, 2005. [11] D. Burgos, C. Tattersall & R. Koper. Representing adaptive eLearning strategies in IMS Learning Design. Paper presented at the TENCompetence Conference, Sofia, Bulgaria, March, 2006. [12] R. Koper & D. Burgos. Designing Learning Activities: From Content-based to Context-based Learning Services. International Journal on Advanced Technology for Learning, 2(3), 2005.  

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