Lamia Berkani

Towards an Ontology for Supporting Communities of Practice of E-Learning CoPEs: A Conceptual Model

The Community of Practice of E-learning (CoPE) represents a virtual space for exchanging, sharing, and resolving problems faced by actors in e-learning. One of the major concerns of CoPEs is to favor practices of reuse and exchange through the capitalization of techno-pedagogical knowledge and know-how. In this paper, we present a conceptual model of CoPEs. This model constitutes the theoretical platform upon which an ontology dedicated to CoPEs will be built. This ontology aims to annotate the CoPEs knowledge resources and services, so as to enhance individual and organizational learning within CoPEs.

A Semantic Based Approach for Knowledge Capitalization in Communities of Practice of E-Learning

Communities of Practice of E-learning CoPEs are considered as a virtual framework for exchanging and sharing techno-pedagogic knowledge and know-how between actors of e-learning. However, after analyzing of knowledge management modalities in some CoPEs, the authors noticed that knowledge is often represented in a way that does not facilitate its access and reuse. Accordingly, this paper focuses on knowledge capitalization in CoPEs and proposes an ontology-based framework aiming to facilitate knowledge sharing and reuse. This framework is structured into three layers: 1 the ontology layer, 2 the semantic annotation layer, and 3 the asset layer. It provides respectively, a common vocabulary within a CoPE aiming to enable a shared understanding between its members, a semantic support to annotate its knowledge assets facilitating their retrieval and reuse, and a means of storage and indexing its different assets. The paper is illustrated with a case study related to a semantic adaptive wiki, a service proposed for a CoPE made up of a teaching staff in computer science at the USTHB University in Algeria.

Using an Ontological and Rule-Based Approach for Contextual Semantic Annotations in Online Communities

Recently, a lot of research focuses on knowledge management and reuse. Pertinent reuse can facilitate learning, knowledge creation and sharing. In this research, we focus on the knowledge capitalization and reuse within Communities of Practice of E-learning (CoPEs). These communities are a virtual framework for exchanging and sharing techno-pedagogical knowledge and know-how between e-learning actors. In this chapter, we propose and discuss a knowledge capitalization approach for knowledge reuse within a CoPE. Our approach is based on contextual semantic annotations to model CoPEs members’ tacit and explicit knowledge. The context dimension represents the situation in which the members create or reuse annotations. To illustrate our approach, we have developed a prototype of knowledge capitalization system based on contextual semantic annotations, called CoPEAnnot. Ontological and rule-based context reasoning have been used to improve knowledge reuse by adapting CoPEAnnot features according to the current activity context of members. Preliminary tests and experimentation of CoPEAnnot conducted within a CoPE made up of members from the Algerian Higher National School of Computer Science show advantages and benefits.

Recommendation of Learning Resources and Users Using an Aggregation-Based Approach

The present paper proposes our recommendation approach for the actors of e-learning. It is based on the collaborative filtering approach and some characteristics of e-learning, namely: the roles and interests of actors as well as the representation of learning resources. The main idea is to exploit, in one hand, the roles and the interests to aggregate the actors in communities of roles and/or interests in order to make an initial recommendation (when we do not have the preferences of the actors). On the other hand, the metadata descriptions are used to recommend a new learning resource added (as we have any rating about it) for the appropriate users by computing the similarity between metadata of the new resource and the metadata of the other ones that are considered as favourite resources for the actors.

Communities of Practice of E-learning CoPE Definition and Concepts

Communities of practice (CoPs) focus on several fields such as engineering, management, and teaching. They offer numerous advantages: exchanging and sharing expertise, collaboration, training and joint development of tacit and explicit knowledge. Our purpose is to extend the application of these communities to the e-learning field. The result of this extension is a new category of CoPs called CoPE (communities of practice of e-learning) which combines two fields: 1) CoPs as basic field; and 2) eE-learning as an application domain. We define the concept of CoPE and the underlying concepts such as learning situations, actors and their roles, activities and possible types of interactions, as well as the environment composed of services, tools and resources.

Semantic Annotations and Context Reasoning to Enhance Knowledge Reuse in e-Learning

We address in this paper the need of improving knowledge reusability within online Communities of Practice of E-learning (CoPEs). Our approach is based on contextual semantic annotations. An ontological-based contextual semantic annotation model is presented. The model serves as the basis for implementing a context aware annotation system called “CoPEAnnot”. Ontological and rule-based context reasoning contribute to improving knowledge reuse by adapting CoPEAnnot’s search results, navigation and recommendation.The proposal has been experimented within a community of learners.

Social Validation of Learning Objects in Online Communities of Practice Using Semantic and Machine Learning Techniques

The present paper introduces an original approach for the validation of learning objects (LOs) within an online Community of Practice (CoP). A social validation has been proposed based on two features: (1) the members’ assessments, which we have formalized semantically, and (2) an expertise-based learning approach, applying a machine learning technique. As a first step, we have chosen Neural Networks because of their efficiency in complex problem solving. An experimental study of the developed prototype has been conducted and preliminary tests and experimentations show that the results are significant.

Toward a Context-Aware Multilingual Personalized Search

In recent years, personalized search has widely been used in Information Retrieval Systems (IRS) to provide the end user with more sophisticated and accurate search results. A basic element that plays an important role in personalized search is the user context which contains several aspects such as the user preferences, navigation history, habits, etc. A user may express his information needs in various languages. This requires the IRS to be able to consider all the contextual information provided in these languages. In this work, we present M-CAIRS, a Multilingual Context-aware Information Retrieval System that takes into account multilingual user contexts to better model the user search interests. Experimental results show a strong correlation between the user’s relevance judgment and the automatic results obtained by our system, which proves the consistency and adequacy of our proposal.

Social Validation of Solutions in the Context of Online Communities

Online Communities are considered as a new organizational structure that allows individuals and groups of persons to collaborate and share their knowledge and experiences. These members need technological support in order to facilitate their learning activities (e.g. during a problem solving process).We address in this paper the problem of social validation, our aim being to support members of Online Communities of Learners to validate the proposed solutions. Our approach is based on the members’ evaluations: we apply three machine learning techniques, namely a Genetic Algorithm, Artificial Neural Networks and the Naive Bayes approach. The main objective is to determine a validity rating of a given solution. A preliminary experimentation of our approach within a Community of Learners whose main objective is to collaboratively learn the Java language shows that Neural Networks represent the most suitable approach in this context.