Toward Maximizing Access Knowledge in Learning: Adaptation of Interactions in a CoP Support System

Belmeskine, Rachid; Leclet-Groux, Dominique; Begdouri, Ahlame

doi:10.1007/978-3-319-11200-8_64

Cited by 1 publication

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“…According to Wenger et al, only 25% of members represent the hard core of a CoP, 30% are considered as active members who participate less regularly, while 45% are considered as peripheral members who typically learn by observing the interactions between the core and the active members [25]. In our previous works, we have identified the main demotivating factors that can lead to a lack of participation of members to the community interactions [15][26] [27]. We summarize them in the following points:…”

Section: A a Problem Of Demotivationmentioning

confidence: 99%

Dynamic Selection Approach to Overcome the Demotivation of Learners in a Community Learning System

Groux-Leclet¹,

Begdouri²,

Belmeskine³

2018

IJISA

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Community of Practice (CoP) is a very rich concept for designing learning systems for adults in relation to their professional development. In particular, for community problem solving. Indeed, Communities of Practice are made up of people who engage in a process of collective learning in a shared domain. The members engage in joint activities and discussions, help each other, and share information. They build relationships that enable them to learn from each other. The most important condition for continuing to learn from a CoP is that the community should live and be active. However, one of the main factors of members demotivation to continue interacting through the CoP is the frequent receipt of a large number of aid requests related to problems that they might not be able to solve. Thing that may lead them to abandon the CoP. In an attempt to overcome this problem, we propose an approach for selecting a group of members who are the most appropriate to contribute to the resolution of a given problem. In this way, the aid request will be sent only to this group. Our approach consists of a static rules-based selection complemented with a dynamic selection based on the ability to solve previous similar problems through analysis of the history of interactions.

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Section: A a Problem Of Demotivationmentioning

confidence: 99%