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Learners' community choice has a crucial role in e‐learning effectiveness. Indeed, individual and structural factors (i.e., learners pre‐existing profiles and networks of interactions) significantly affect how learners develop a collaborative e‐learning environment. In this context, social network analysis, singularly community detection has been a good approach to improve collaborative environment through discovering pertinent learners' communities and new effective relations. However, with social media emergence, many real‐world networks, such as learners' networks, evolve the connections in multiple layers, where each layer represents a different type of relationship. This, combined with their continuous evolution over time, has brought new challenges to the field of community detection. Thus, this paper proposes a new configurable algorithm for detecting collaborative and lifelong communities within dynamic multirelational social learners' networks. To do so, this algorithm is based on a graph model to represent these different interactions as well as different learners' profiles and characteristics. Moreover, by using particle swarm optimization, it aims to optimize a configurable combined metric to detect the most relevant community appropriate to a given situation. Finally, it considers the temporal dimension to find the final lifelong community. By the end of this paper, experimental results using synthetic networks prove that the proposed algorithm achieves better results compared with other community detection algorithms. Therewith, experiments on a real e‐learning network show this algorithm's role in improving collaboration within learners' network.

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Decision making whenever and wherever it is happened is key to organizations success. In order to make correct decision, individuals, teams and organizations need both knowledge management (to manage content) and collaboration (to manage group processes) to make that more effective and efficient. In this paper, we explain the knowledge management and collaboration convergence. Then, we propose a formal description of mixed and multimodal decision making (MDM) process where decision may be made by three possible modes: individual, collective or hybrid. Finally, we explicit the MDM process based on UML-G profile.

In order to make right decision within organizations, individuals and teams need to be supported during decision making (DM) process. The aim in this paper is to support DM by finding out the right people that can be involved to make right decision in more effective and efficient way. In this paper, we explain how it is interesting to use community detection techniques to support DM process. The community can be detected in three phases: the problem formulation and/or the solutions generation and the decision making. To detect community, we propose a simple thresholding algorithm based on Structural/Attribute clustering.