=Paper=
{{Paper
|id=None
|storemode=property
|title=A Recommendation-based Approach for Communities of Practice of
E-learning
|pdfUrl=https://ceur-ws.org/Vol-867/Paper28.pdf
|volume=Vol-867
|dblpUrl=https://dblp.org/rec/conf/icwit/BerkaniNC12
}}
==A Recommendation-based Approach for Communities of Practice of
E-learning==
https://ceur-ws.org/Vol-867/Paper28.pdf
A Recommendation-based Approach for Communities
of Practice of E-learning
Lamia Berkani1, 2, Omar Nouali3 and Azeddine Chikh4
1
Department of Computer Science, USTHB University, Bab-Ezzouar, Algiers, Algeria
2
Higher National School of Computer Science, ESI, Oued Smar, Algiers, Algeria
3
Department of Research Computing, CERIST, Algiers, Algeria
4
Department of Information Systems, KSU University, Riyadh, Saudi Arabia
l_berkani@hotmail.com, onouali@mail.cerist.dz, az_chikh@ksu.edu.sa
Abstract. The paper presents a recommendation-based approach for knowledge
resources in Communities of Practice of E-learning (CoPEs). The proposed
approach is based on the hybrid semantic information filtering (IF), integrating
the content-based filtering, the collaborative filtering and the ontology-based
filtering approaches. The main idea is to apply a multi-level filtering, where
three dimensions have been proposed for the profile: collaborative, social and
semantic.
Keywords: CoP of e-learning, knowledge resource, recommendation,
information filtering, ontology-based filtering, profile.
1 Introduction
According to Wenger [1], Communities of Practice (CoPs) are “groups of people
who share a concern, a set of problems, or a passion about a topic, and who deepen
their knowledge and expertise in this area by interacting on an ongoing basis”. CoPs
allow members to share their practices, to develop their knowledge and skills. They
are embedded within all areas and domains including education, engineering,
management, health, etc. They are seen as a new organizational structure offering
innovative means for creating and sharing knowledge.
The authors in [2, 3] extended the application of this concept to the domain of e-
learning. They considered CoPs of e-learning (CoPEs) as a virtual framework for
exchanging and sharing techno-pedagogic knowledge and know-how between actors
of e-learning. CoPEs give the possibility for professionals in e-learning to gather,
collaborate, and organize themselves in order to: (i) share information and
experiences related to e-learning development and use; (ii) collaborate in order to
solve together e-learning problems and to build techno-pedagogic knowledge and best
practices; (iii) learn from each other and develop their competences and skills in their
domain of expertise.
In order to participate effectively to the knowledge management and learning
processes in a CoPE, members need guidance to find and synthesize information.
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They need to find the adequate resources for their activities within the CoPE or to be
used for example to design their courses within the e-learning platform.
This paper will focus on the recommendation of knowledge resources using
Information Filtering (IF) approach that will attempt to present to the member
information items, according to his interests.
The rest of this paper is organized as follows: Section 2 presents the background
and related work about IF approaches. Section 3 discusses the application of IF in
CoPEs and proposes a hybrid semantic IF approach for the recommendation of
knowledge resources in CoPEs. Finally the conclusion highlights the main results of
this work and presents some perspectives.
2 Information Filtering
We present in this section the different IF techniques and some related works close to
our context of study.
2.1 Background
Information filtering (IF) is the process allowing, starting from an incoming volume
of dynamic information, to extract and present the only information interesting either
a user or a group of users having relatively similar interests. The filtering system
makes a "prediction" about the usefulness of the information to the user. This
prediction is based on the "profile" of the user and leads to a decision-making:
"recommend" or "not recommend" information [4]. The problem of IF can be
expressed as follows [5]: C is a set of users, S a set of documents to be recommended,
and u a function which measures the importance that represents a document s to a user
c. The objective is to search about documents s’ so as to maximize the utility function
u, as described formally:
U: C x S→ R
∀ c ∈ C, s’c = args∈S max u(c,s)
The IF systems are classified into three categories: the content-based filtering
systems, the collaborative filtering systems, and the hybrid ones.
The content-based filtering systems recommend the similar documents to those the
user has already liked. This is calculated by comparing the interests of users
introduced explicitly (e.g. through a questionnaire) or implicitly (through a
behavior supervision) with the characteristics of the documents [6].
Collaborative filtering or social recommender systems recommend data items to a
user by taking into account the opinions of other users [7]. Instead of
recommending data items because they are similar to items the user preferred in
the past (content-based recommendation), collaborative approaches generate
recommendations about data items that users with similar interests liked in the
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past. In order to estimate user’s preference for an item, collaborative filtering
systems collect ratings through explicit means (e.g. the user is asked to rate the
item), implicit means (e.g. the system infers user’s preference by observing user’s
actions) or both. More formally, the utility of a document s to a user c, u (c, s) will
be calculated based on the uj (cj, s) that are similar. The prediction function F uses
the vote matrix C × S and proceeds in two steps [8]: (1) calculate the similarity
between the users and infer communities, (2) predict notes for a few documents
and select only those with a high score.
There are two major collaborative approaches, an approach based memory (the
note given by a potential user to a document is calculated based on ratings given
by other users for the same document) and another based model (learn a
descriptive model linking users, documents and votes). With the growth of e-
commerce, collaborative filtering techniques have become well known through
their use in commercial web sites such as Amazone.com.
The hybrid systems, combine in different ways the two previous approaches and
try to overcome their shortcomings: the “cold start” problem when there are not
enough ratings, the inability to recommend non-textual documents that do not have
information about their content, quality criteria and reliability of the source are not
considered in the content-based systems, etc.
Recently, with the emergence of the semantic Web, a new generation of
recommender systems has emerged [9]: (1) the ontology-based IF systems
(conversion from a description of the documents by key words to a semantic
description based on concepts; (2) the collaborative annotations systems (assigning
to resources a set of words called tags or annotations to describe their content or
provide a more contextual and semantic information); (3) the social networks-based
IF systems (managing the friends lists and expressing their interests such as in
Facebook, and LinkedIn, encouraged the reuse of this social data in the IF systems).
2.2 Related work
The sate of the art shows an important number of proposed recommender systems.
We present some works related to our context of study.
QSIA (Questions Sharing and Interactive Assignments) for learning resources
sharing, assessing and recommendation has been developed by Rafaeli et al. [10].
This system is used in the context of online communities, in order to harness the
social perspective in learning and to promote collaboration, online recommendation,
and further formation of learner communities.
ReMashed is a recommender system that addresses learners in informal learning
networks [11; 12]. The authors created an environment that combines sources of users
from different Web2.0 services and applied a hybrid recommender system that takes
advantage of the tag and rating data of the combined Web2.0 sources.
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3 Contribution
We propose a recommendation system based on the hybrid semantic IF (see Fig.1). In
the CoPE, one member or a group of members need a recommendation of knowledge
resources in the following situations: (1) information retrieval; (2) when a new
resource has been added to the memory and that can be interesting for the member;
(3) during an activity (e.g. design of a learning scenario); and (4) for a new member
who integrate the community.
Accordingly, we propose a recommendation system based on the hybrid semantic
IF. The main idea is to apply a multi-level filtering approach and to consider a multi-
level profile according to the need, context, and conditions (availability of
information), so as to make an effective recommendation. As illustrated in Fig. 1,
resources are represented semantically using OntoCoPE ontology [13] and three
dimensions are considered for the profile: collaborative (implicit/explicit evaluations),
social (a set of personal information: name, specialty, email, a set of contacts…), and
semantic (members’ interests represented in the form of concepts with weight
corresponding to their degrees of importance). Each dimension produces a set of
recommendations that can be classified, using for example an adaptive classification:
u(c,s) = α . u Coll (c, s) + β . u Social (c, s) + γ . u Sem (c, s); where : α+β+γ=1
Inscription
Member Profile OntoCoPE
Concepts
Evaluation /
Extraction
Profile update
Interaction
Semantic
representation
Collaborative Social Semantic
dimension dimension dimension Community
memory
(Resources)
Collaborative Social Semantic
clustering clustering clustering
Recommendations
Classification
Resources
Resources
Fig. 1. The Hybrid semantic filtering system adapted from [9].
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The social recommendation has the priority, if there are no or not enough
evaluations or if the semantic dimension is not yet well defined. The collaborative
recommendation has the priority, if we want to discover new interests to a member.
Otherwise, the semantic recommendations will have the priority as they more
correspond to the members’ interests.
4 Conclusion
The paper presents proposes a recommendation-based approach for knowledge
resources in CoPEs, using the hybrid semantic IF. The main idea is to apply a multi-
level filtering, where three dimensions has been proposed for the profile:
collaborative, social and semantic. However, the proposed approach needs to be
evaluated in a real situation. We envisage in a near future to develop the
recommendation system and to evaluate its performance using a learning community
of students within the USTHB University in Algeria.
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