Rachid Belmeskine

Work place: MIS Laboratory, UPJV, Amiens, France, University of Picardie Jules Verne, 33, rue St Leu, 80039 Amiens-France

E-mail: rachid.belmeskine@gmail.com


Research Interests: Computational Science and Engineering, Computer systems and computational processes, Computational Learning Theory, Database Management System, Data Structures and Algorithms


Rachid Belmeskine received a Master’s Degree in computer engineering, from the university of Mohammed Premier, Oujda, Morocco, and Ph.D in computer science from the university of Picardie Jules Verne, Amiens, France, and the university of Sidi Mohamed Ben Abdellah, Fez, Morocco. He works as Data Scientist from 2016. His current activities are focused mainly on Machine learning, Deep learning and Big Data.

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

By Dominique Groux-Leclet Ahlame Begdouri Rachid Belmeskine

DOI: https://doi.org/10.5815/ijisa.2018.07.03, Pub. Date: 8 Jul. 2018

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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