SEMANTICS AND KNOWLEDGE CAPITALIZATION IN

ONLINE COMMUNITIES OF PRACTICE OF E-LEARNING

Lamia Berkani

1, 2

, Azeddine Chikh

and Omar Nouali

Department of Computer Science, USTHB University, Bab-Ezzouar, Algiers, Algeria

Higher National School of Computer Science, ESI, Oued Smar, Algiers, Algeria

Department of Information System, KSU University, Riyadh, Saudi Arabia

Department of Research Computing, CERIST, Algiers, Algeria

Keywords: Communities of practice, CoP, CoP of e-learning, Knowledge management, Knowledge capitalization,

Ontology-based framework, Semantic annotation.

Abstract: Knowledge management in Communities of Practice of E-learning (CoPEs) is challenged by several issues:

the complexity of knowledge, considered as interdisciplinary (psycho-cognitive, pedagogic, software-

oriented, and hardware-oriented), the difficulty to access and reuse that knowledge, and the complexity of

the knowledge capitalization process. Most of the knowledge exchanged is mainly tacit, based on direct

communication between members, and therefore needs to be elicited and represented in a formal way to be

capitalized. Explicit knowledge is generally shared and accessible through the CoPE’s repositories.

However, it is not always well elicited and organized. In this paper, we propose an ontology-based

framework for capitalizing knowledge for reuse in CoPEs. We show through an example of use how

semantics can contribute to the management of the tacit knowledge that the community members own and

therefore to the improvement of the learning process in CoPEs.

1 INTRODUCTION

Recent researches show that Communities of

Practice (CoPs) play an important role in the

management of the tacit knowledge that the

community members own (Wenger, 2004; Kimble

and Hildreth, 2004). A CoP has become associated

with knowledge management, in particular as a way

of transferring tacit knowledge. CoPs have several

characteristics that distinguish them from formal

organizations. In fact, according to Wenger (2004)

such communities are groups of people who share a

concern, a set of problems, or a passion about a topic

(the domain of the community), deepen their

expertise and practical knowledge (the practice of

the community), and interact on an ongoing basis

(the community itself).

Communities of Practice of E-learning (CoPEs)

(Chikh et al., 2007; 2008) are considered as a virtual

framework for exchanging and sharing techno-

pedagogic knowledge and know-how between actors

of e-learning (e.g. teachers, tutors, administrators,

etc.). Recently, we can see the emergence of CoPEs.

For example: the CoPe-L (CoP of e-learning at

Luxembourg), has been created in the framework of

Palette project (2006), and whose objective is to

share practices and promote e-learning activities;

CoP of tutors Learn-Nett (Learning Network for

Teachers and Trainers - http://learn-nett.org), is

focused on a shared course and aims at preparing

future teachers or trainers for educative uses of

Information and Communication Technologies.

By using advanced technology, online CoPEs

have the potential to bring members together

virtually, to learn from each other, collaborate and

share expertise and techno-pedagogic practices.

We address in this paper the problem of

capitalization of knowledge, both tacit and explicit,

in a way that facilitates its access and reuse. Due to

the informal character of learning within a CoPE,

most of the knowledge is mainly tacit, based on

direct communication between members, and then

needs to be elicited and represented in a formal way

to be capitalized. Moreover, explicit knowledge is

generally shared and accessible through the CoPE’s

repositories. But, it is not always well elicited and

organized and then needs to be more explicit, so as

to improve access, sharing and reuse of this

Berkani L., Chikh A. and Nouali O..

SEMANTICS AND KNOWLEDGE CAPITALIZATION IN ONLINE COMMUNITIES OF PRACTICE OF E-LEARNING.

DOI: 10.5220/0003676500960104

In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2011), pages 96-104

ISBN: 978-989-8425-81-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

knowledge.

We propose in this paper an ontology-based

framework for capitalizing knowledge for reuse in

CoPEs. Ontologies, generally defined as a

representation of a shared conceptualization of a

particular domain (Gruber, 1993), is a major

component of the semantic web. The role of

ontologies is to assist persons and organizations by

providing a common vocabulary, to achieve

interoperability between different environments, and

to improve consistency information retrieval. In our

context, the ontology-framework will provide a

common backbone for capitalization of knowledge,

both tacit and explicit, allow to annotate the CoPE’s

knowledge resources in order to facilitate their

retrieval and reuse; provide a shared understanding

between the different actors of e-learning; and

facilitate exchanges between the CoPE environment

and the Learning Management Systems (LMS).

2 RESEARCH PROBLEM

In a CoPE, members can openly discuss and

brainstorm about their problems and experiences,

related to the development and use of online

learning systems. The interactions are conducive to

developing new knowledge, stimulating innovation,

or sharing existing tacit and/or explicit knowledge

between e-learning actors.

On one hand, those actors have tacit techno-

pedagogic knowledge which they learnt from their

experience in different e-learning projects. However,

that knowledge is not always capitalized in the

memory. Sharing such knowledge is considered as a

big challenge: it must be efficiently and effectively

represented in order to be further exploited.

On the other hand, explicit knowledge, which

includes learning resources, is generally shared and

accessible through the CoPE’s repositories.

However, it is not well elicited and organized (e.g.

lack of information related to feedback use, validity

and assessment).

In the context of a CoPE, we distinguish two

types of knowledge reuse: the explicit knowledge

reuse (e.g. reuse of knowledge resources) and the

tacit knowledge reuse (e.g. reuse of some hints

provided by another member having more

experience). Our objective consists to make the

reuse explicit and to well organize it, so as to make

it more efficient.

Consequently, our main research question is:

“How to represent knowledge, tacit and explicit,

within the framework of a CoPE, so as to facilitate

its access and reuse?”

In this paper we try to answer the following sub-

questions:

 How can we help members to formalize and

capitalize tacit knowledge?

 How do we organize the CoPE memory in

order to enhance the reuse of its content by

members?

 How do we enrich learning resources with

metadata in order to improve their reuse?

3 RELATED WORK

Recently, a lot of research works was interested to

knowledge management and capitalization within a

CoP, to name but a few: the Palette project (2006),

where several knowledge management services were

proposed to support CoPs. These services rely on a

semantic web-based approach using ontologies

(Tifous et al., 2007), for annotating knowledge in

order to facilitate their transfer and sharing. Other

works are based on the concept of organizational

learning memory to capitalize tacit knowledge

(Leblanc and Abel, 2008).

In the context of CoPEs, Quénu-Joiron and

Condamines (2009) developed a web community

platform dedicated to knowledge capitalization and

on-line know-how transfer between experienced

teachers and beginners. While Quénu-Joiron and

Leclet (2010), implemented a CoP dedicated to

project based pedagogy tutors using a case-based

reasoning approach.

4 KNOWLEDGE

CAPITALIZATION IN COPES

4.1 Knowledge Capitalization Process

The process of knowledge capitalization can be seen

as a cycle with several steps. Grundstein (1992)

summarizes this process in four steps: detection,

preservation, exploitation, and actualization. In

(Oladejo et al., 2010) the authors propose the

“Dynamic Capitalization” approach (see figure 1).

There are five major phases in this approach and

each phase is dynamic with respect to evaluation and

validation of knowledge resources by actors.

Knowledge can be elicited using the process of

declaration and annotation. Knowledge resources are

represented with the aid of a conceptual knowledge

model. The acquired knowledge resource is stored

SEMANTICS AND KNOWLEDGE CAPITALIZATION IN ONLINE COMMUNITIES OF PRACTICE OF

E-LEARNING

with temporal attributes, in a knowledge repository

(i.e. for dynamic and non-volatile capitalization).

The storage facilitates the reuse of knowledge

through exploitation process. Acquired and stored

knowledge resources can be exploited for reuse and

sharing.

Figure 1: Dynamic capitalization approach, from (Oladejo

et al., 2010).

The dynamic capitalization approach seems to be

well adapted for our context of study, as it proposes

a structure for knowledge reuse and encourages and

favours the collaboration between actors.

4.2 Knowledge Capitalization Process

in CoPEs

The capitalization process in a CoPE is considered

as the result of continuous update from knowledge

reuse and capitalization of lessons learnt by the

community members. We discuss below the

different steps of the dynamic capitalization

approach applied to the context of a CoPE:

 Elicitation, Acquisition, Validation:

knowledge is elicited from members through

discussion using the annotation process (e.g.

analysis comments). This supports the

understanding and validation of knowledge

among them.

 Representation: knowledge resources are

represented using ontologies, knowledge

models, etc.

 Storage: an organizational learning memory is

used to store all the CoPE’s knowledge

resources.

 Exploitation: refers to the reuse of knowledge

resources from the memory. For example, in

the case of problem-solving, knowledge

exploitation involves mining and visualization

of knowledge for new cases of problems.

 Feedback exploitation strategy: members can

be guided to externalize the knowledge

derived from the reuse of knowledge

resources in form of feedback.

5 THE ONTOLOGY-BASED

FRAMEWORK

We present in this section, an ontology-based

framework for knowledge reuse in CoPEs.

5.1 OntoCoPE – An Ontology for

CoPEs

A general conceptual model for a CoPE, called

OntoCoPE ontology, is based on the O’CoP

ontology conceptual model for CoPs (Tifous et al.,

2007) defined in the Palette project (2006) and on

partial conceptual models for CoPEs proposed in

(Berkani and Chikh, 2009).

Figure 2: Main concepts of CoPEs.

5.2 Ontology-based Framework for

CoPE Memory

The organizational memory refers to the place where

the organization’s information and knowledge

resources are found. The use of ontologies helps the

organization to become a “semantic learning

organization”. MEMORAe project (Organizational

Memory Applied to the e-learning) illustrated the

importance of using ontologies to represent an

organizational learning memory in the context of an

e-learning training (Abel et al., 2004) and for a

community of learners (Leblanc and Abel, 2008).

To implement the CoPE memory, we propose an

ontology-based framework in order to define a

common vocabulary and to annotate the knowledge

resources, and we provide a means of storage and

indexing of knowledge resources. We propose to

structure the memory into three layers, as shown in

figure 3.

Elicitation

Acquisition

Validation

Feedback

Evolution

Storage with

temporal attribute

Exploitation

Representation

KMIS 2011 - International Conference on Knowledge Management and Information Sharing

Figure 3: Ontology-based Framework for CoPE memory.

The CoPE memory is seen as a “semantic learning

organization”, described using an ontology-based

framework structured into three layers as shown in

figure 3. The framework provides a common

vocabulary between the CoPE’s members, a

semantic support to annotate the CoPE’s knowledge

resources in order to facilitate their retrieval and

reuse, and a means of storage and indexing the

different data. We describe in the following the three

layers:

5.2.1 The Ontology Layer

The ontology layer is based on several ontologies:

 OntoCoPE, an ontology for CoPEs (Berkani

and Chikh, 2009), describes the main concepts

of a CoPE: “Community”, “Actor”, “Role”,

“Profile”, “Activity”, “Process”, “Resource”,

“Service and Tool”, “knowledge”,

“Competency”.

 An e-learning ontology, describing the

concepts related to the domain of e-learning.

This ontology will facilitate exchanges and

transfer of knowledge between the CoPE

environment and LMS.

 A knowledge model ontology, describes the

different kinds of knowledge models such as

lessons learnt and which can be developed for

example using patterns and case-based

reasoning.

 An application domain ontology, concerns a

specific course (e.g. mathematics, software

engineering, etc.).

 A specification languages, such as Learning

Object Metadata (LOM, 2002) for describing

learning objects, IMS Learning Design

specification (IMS-LD, 2003) for describing

learning designs, etc.

5.2.2 The Semantic Annotation Layer

Semantic annotations are generated automatically

and assign knowledge resources of the content layer

to concepts of ontologies included in the ontology

layer. Information is represented as a triplet

<Ontology concept, Annotation, Knowledge

resource>.

5.2.3 The Content Layer

The content layer includes several repositories to

store the different data:

 Members’ profile: includes some attributes

such as: experience; cognitive characteristics;

communication skills; learning competences;

learning objectives.

 Lessons learnt: correspond to positive or

negative lessons learnt, related respectively to

best or bad practices regarding the different

stages of the development lifecycle (analysis,

design, implementation and utilization) of an

e-learning product.

 Learning Objects (LOs): are described using

the standard LOM (2002). A semantic

description of a LO using ontologies is

proposed in (Jovanović et al., 2007). In the

next sub-sections we present a model of LO in

the context of CoPEs, in order to depict the

specificities of a CoPE.

 Learning Designs: also called learning

scenarios, are represented using the standard

IMS-LD (2003) by identifying the necessary

LOs

LDs

Lessons

learnt

Specification

Languages

(

LOM, IMS-LD…)

Knowledge model

Ontology

E-learning

Ontology

OntoCoPE

Ontology

Application domain ontology

“Content layer”

(Knowledge Resource)

Members’ profile

“Ontology layer”

(

Ontologies)

“Semantic annotation layer”

<Ontology Concept, Annotation,

Knowledge Resource>

Annotations

SEMANTICS AND KNOWLEDGE CAPITALIZATION IN ONLINE COMMUNITIES OF PRACTICE OF

E-LEARNING

learning activities and assigning LOs to those

activities in order to achieve the specified

learning objective.

5.3 Annotation Model

We use the annotations in the CoPE for the

capitalisation of tacit knowledge. Annotations aim to

evaluate and improve the understanding about

knowledge resources, artefacts, processes, etc.

The model of the annotation is created on the

basis of some previous works on annotations:

DAML Ontology Library (2000) and (Fogli et al,

2005). We consider that an annotation may annotate

one or more knowledge resources, a part of a

knowledge resource or another annotation. An

annotation may be related to several other

annotations.

We have defined three kinds of annotations: (1)

the analysis annotation, where members can write

some comments highlighting their personal remarks

and understanding; (2) the evaluation annotation,

where members can evaluate the knowledge

resources according a scale (1-5), from a very good

one to a very bad one. Moreover, members can give

their personal feedback regarding the use of any

knowledge resource; and (3) the results annotation,

shows the lessons learnt from this use (i.e. positive

and negative lessons learnt).

For the analysis annotation for example, we

propose the following annotation properties (see

figure 4):

 Author: is the member who writes the

annotation.

 Date-Time: corresponds to the date of creation

(or update) of the annotation.

 Annotation-body: is the element concerned by

the annotation (a Knowledge resource, a part

of a knowledge resource or an annotation).

 Sharing: the author may share or not his

annotation.

 Annotation-type: which may be a “Question”,

asking for additional information; a

“Comment”, adding some remarks; an

“Explanation”, adding further clarification; an

“Example”, illustrating the annotation body,

Each annotation is associated to one or more

concepts of an ontology. This allows mapping of the

annotations to the elements of the ontology.

OWL-DL language may be used to implement

our ontology, as it offers the consistency checking of

our model and the querying which provides an

improved exploitation and knowledge retrieval from

complex knowledge bases. Thus, members can

formulate complex queries such as: ‘retrieve

knowledge resources of a given topic and having

high levels of score’.

Figure 4: Analysis annotation model.

5.4 LO Model in the Context of a CoPE

The adoption of the standard LOM (2002), promotes

exchange of LOs among different LMS, and offers

higher potentials for finding existing learning

content. However, decisions about reuse involve a

broad set of issues about content, context and

pedagogy that cannot be fully expressed in the

LOM’s metadata fields. The authors in (Jovanović et

al., 2007) developed an ontology-based framework

aimed at explicit representation of context-specific

metadata. The core part of the proposed framework

is a LO context ontology, that leverages a range of

other types of ontologies (e.g., user modeling

ontology and content structuring ontology to capture

the information about specific context of use of a LO

inside a learning design). Information of this kind

can be rather useful for personalization of learning

process in the LMS.

In the context of a CoPE, members need not only

to find and reuse LOs in their courses, but moreover,

to find the comments and feedback about LOs

expressed by members having used them; the results

of tests and experimentations in the LMS;

information about how to use the LO (i.e. the

context of use and contexts of possible reuse, etc.).

In our solution, we propose to adopt the standard

LOM (2002) and the existing ontologies to annotate

the LO. Moreover, we add a concept, called “LO-

Reuse”, to capture all the above mentioned

elements: members’ comments, their feedback,

results, and so on.

Figure 5 shows the description of a LO’s related

metadata. The proposed metadata concerns: the LO

information context, gives general information about

the origin of the LO (developed in the CoPE or

imported from any other source), its subject and

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Member with

Profile “P1”

Figure 5: LO-Reuse metadata in the context of a CoPE.

Figure 6: Example of knowledge capitalization for reuse.

topic domain; and a set of annotations, analysis,

evaluation and results, provided by a member or

group of members who have used the LO.

Using this concept, allows us to recommend the

adequate LOs for members according to their

profiles and needs. Moreover, when a member is

seeking about LOs related to any subject and /or

topic domain, it is possible to recommend him

automatically LOs jugged as interesting (i.e.

evaluated with a high level score) by other members

having the same profile. The recommendation of the

relevant LOs will help members in their activities

and will improve their learning using the experience

and feedback of other members, through the

different annotations.

6 EXAMPLE OF USE

We consider a use case within Learn-Nett (Learning

Network for Teachers and Trainers), a CoPE

focused on a shared course and aims at preparing

future teachers or trainers for educative uses of

Information and Communication Technologies. The

ontology-framework proposed will be useful for

annotating the CoPE’s knowledge resources such as

LOs (e.g. a pedagogical and/or technical guide for

the course). We suppose one teacher member (M1)

of Learn-Nett having the profile (P1) wants to

prepare a course concerning Software Engineering.

As shown in figure 6, the teacher M1 can retrieve

LOs from the memory. The “knowledge resources

search service” uses the ontology framework to seek

about the relevant resources that meets the needs of

M1. One or more LOs can be found and displayed

for the member, who will have the possibility to

consult and/or download them. However, once using

those resources, the member will have the possibility

to annotate any of them using the “knowledge

resource annotation service” (AnnotatKR).

In the following, we’ll show through a series of

screenshots how members interact with AnnotatKR

to annotate a LO.

Annotation

Repository

Members

Repository

Learning

objects

(LOs)

Query

LOs

Repository

Store

Fetches

Member’s

Profile "P1"

Ontology-

Framework

Fetches

Annotation

SEMANTICS AND KNOWLEDGE CAPITALIZATION IN ONLINE COMMUNITIES OF PRACTICE OF

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101

After using the LO in this course, the member

M1 can write some comments highlighting his

personal experience and analysis about it. He can

also give some comments that will serve to improve

it, ask a question, or give an example from his

practice, using the “Analysis annotation module”

(see figure 7).

Figure 7: Analysis annotation module.

Moreover, the member M1 can participate to the

evaluation of the LO by giving his own feedback

and score using the “Evaluation annotation module”,

as shown in the figure 8.

Figure 8: Evaluation annotation module.

Finally, the member M1 can use the “Results

annotation module” to note positive and/or negative

aspects from reflective analysis of the supervision

methods throughout his effective experience of

tutoring students (see figure 9).

Figure 9: Results annotation module.

Once the LO’s reuse-related metadata are fulfilled,

the anchoring information and the information on

the teacher are stored in the annotation repository.

Another teacher member (M2) can retrieve the

same LO, and then he will have the possibility to

reuse, not only the LO content but also the

comments found on it. Moreover, he will have an

idea about the degree of interest of the LO according

to the given scores. He will have access to the results

deduced from its use by other members. Finally, he

can also create annotations on that resource.

7 DISCUSSION

The work presented in this paper aims to capitalize

the tacit knowledge owned by members of a CoPE,

using semantic annotations. We focused in this

paper on LOs just in order to illustrate the process of

capitalizing knowledge using annotations. However,

this approach can be used to annotate all the

knowledge resources, artefacts and processes in the

CoPE. For instance, we can annotate the proposed

solutions during the problem-solving process.

Our main objective is to facilitate the

capitalization of tacit knowledge (know-how,

experience, feedback, etc.) of members when using

knowledge resources so as to facilitate the

knowledge access and reuse. We can summarise the

main results expected by this approach as follows:

 Members can share and reuse their tacit

knowledge through the analysis annotation.

 They can have an idea about the different

feedback and evaluations of other members

who have used the knowledge resources.

 The knowledge resource search service can

use the scores of a knowledge resource from

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the different evaluation annotations and then

recommend those having a high level of

scores to other members.

 The service can refine the search process by

finding the resources that meet the needs of

members and for what members having the

same profile used them and evaluated them

with a high level of scores.

 The manager of the CoPE can have an idea

about the utilisation of knowledge resources

by members and about the participation of

members too in the annotation process.

 Members can learn from the experience of

others through the results annotations. This

will help them to improve their expertise and

practical knowledge.

Furthermore, in addition to this different forms

of knowledge acquisition and reuse, this initiative,

will help members to improve their engagement in

the CoPE. Indeed, this will motivate them to

collaborate and participate actively in the

community. The collaboration here concerns

especially the reification process of tacit knowledge

in the community memory. In addition, we can

consider the annotations as a trigger for other

activities in the community, as they can open further

discussions and exchanges among members.

8 CONCLUSIONS

The present paper described an ontology based-

framework for knowledge capitalization and reuse

within online CoPEs. The ontology allows to

annotate knowledge resources in order to facilitate

their retrieval and reuse by CoPEs’ members. Our

main objective is to organize the process of

capitalization of knowledge and to allow a

knowledge elicitation through manual and automatic

annotation of knowledge resources by capturing the

members’ experience and feedback. We have

proposed a model for manual annotation.

In our future work, we envisage to complete our

ontology, integrate a SPARQL engine to allow

querying of the knowledge base, and to allow an

automatic and semi-automatic annotation of

knowledge resources.

Finally, it is necessary to check the usefulness of

the framework and to describe the experience in the

members’ feedback and point of view.

To do so,

we’ll evaluate our approach in a CoPE to be created

in the University of Science and Technology

(USTHB) called A-CoPE (Algerian CoP of E-

learning), and whose main objective is to promote e-

learning in higher education context.

REFERENCES

Abel, M. H., Barry, C., Benayache, A., Chaput, B., Lenne,

D., Moulin, C., 2004. Ontology-based Organizational

Memory for e-learning. In Educational Technology &

Society, vol.7.

Berkani, L., Chikh, A., 2009. Towards an Ontology for

Supporting Communities of Practice of E-learning

"CoPEs": A Conceptual Model. In U. Cress, V.

Dimitrova, and M. Specht (Eds.) EC-TEL 2009. LNCS

5794, pp. 664–669. Springer, Heidelberg,

Chikh, A., Berkani, L., Sarirete, A., 2007. Modeling the

communities of practice of e learning – CoPEs. In 4th

Annual Conference proceedings of Learning

International Networks Consortium, pp. 428-441,

Jordan

Chikh, A., Berkani, L., Sarirete, A., 2008. Communities of

Practice of E-learning “CoPE” – Definition and

Concepts. In International Workshop on Advanced

Information Systems for Enterprise, pp. 31-37.

DAML Ontology Library, 2000. http://www.w3.org/

2000/10/annotation-ns#

Fogli, Fresta, G., Mussio, P., Marcante, A., Padula, M.,

2005. Annotation in cooperative work: from paper-

based to the web one” In: International Workshop

Annotation for Collaboration, pp. 24-25, Paris France.

Gruber, T.R., 1993. A Translation Approach to Portable

Ontology Specifications. In Knowledge Acquisition,

vol. 5, no. 2, pp. 199-220.

Grundstein, M., 1992. Knowledge Engineering within the

Company: An Approach to Constructing and

Capitalizing the Knowledge Assets of the Company”.

In Proceedings of 3

Annual Symposium of the

International Association of Knowledge Engineering,

Washington DC.

IMS-LD, 2003: IMS Learning Design specification,

http://www.imsglobal.org/learningdesign/index.cfm

Jovanović, J., Gašević, D., Knight, C., Richards, G., 2007.

Ontologies for Effective Use of Context in e-Learning

Settings. In Educational Technology & Society, vol.

10, no. 3, pp.47-59.

Kimble, C., Hildreth, P., 2004. Communities of practice:

Going one step too far? In AIM, pp. 1-7, 2004

Leblanc, A., Abel, M.H., 2008. E-MEMORAe2.0: an e-

learning environment as learners communities support.

In International Journal of Computer Science &

Applications. Vol. 5, pp. 108-123

LOM, 2002: Learning Object Metadata, http://ltsc.

ieee.org/wg12/

Oladejo, B. F., Odumuyiwa, V. T., David, A. A., 2010.

Dynamic capitalization and visualization strategy in

collaborative knowledge management system for EI

process. In International Conference of Knowledge

Economy and Knowledge Management, France.

SEMANTICS AND KNOWLEDGE CAPITALIZATION IN ONLINE COMMUNITIES OF PRACTICE OF

E-LEARNING

103

PALETTE IST project, 2006. Pedagogically sustained

Adaptive Learning through the Exploitation of Tacit

and Explicit Knowledge, http://www.ecrim.palette.org

Quénu-Joiron, C., Condamines, T., 2009. Facilitate On-

Line Teacher Know-How Transfer Using Knowledge

Capitalization and Case Based Reasoning. In 4th

European Conference proceedings on Technology

Enhanced Learning: Learning in the Synergy of

Multiple Disciplines.

Quénu-Joiron, C., Leclet, D., 2010. How to Instrument a

Community of Practice Dedicated to Project Based

Pedagogy Tutors: A Solution Based on Case Based

Reasoning. In 10th IEEE International Conference

proceedings on Advanced Learning Technologies, pp.

344-348.

Tifous, A., El Ghali, A., Dieng-Kuntz, R., Giboin, A.,

Evangelou, C., Vidou, G., 2007. An ontology for

supporting communities of practice. In: 4th

International Conference on Knowledge Capture,

Canada.

Wenger, E., 2004. Knowledge management as a doughnut:

Shaping your knowledge strategy through

communities of practice. In Ivey Business Journal.

KMIS 2011 - International Conference on Knowledge Management and Information Sharing

104