Reference:

Gouardères, E., & Gouardères, G. (n.d.). Enhancing group cohesion in Virtual Communities of Practice. https://doi.org/10.1109/WI-IAT.2011.186

Summary:

  • This paper describes software agents that monitor the formation of teams within the “System-Orientated UAV simulator SOUL”. Operators must be trained in virtual teams, working in communities of practice.
  • The SOUL Lab I-Room employ virtual world’s technology , with a particular focus on the planning, process nd task support aids through the implementation of Communities of Practice
  • The CoPs are monitored by agents that can increase collaboration between members, and better support the human resources to compose more homogeneous, more ccohesive and effective teams
  • This work explores the process of knowledge convergence in the context of CoP members collaboration in which members achieve a group training activity mediated by a sharable knowledge and competence tool: the E-Portfolio.
  • They use the KSA Model – (Knowledge, Skills and Attitudes), and the uptake of the ECM (Electronic Companionship Model) to understand how members make sense in their interactions and how they did uptake their newly acquired knowledge or skills in unexpected situations
  • Teamwork and team training models
    • The ECM framework requires that team members be trained within a community of practice under the supervision of training officers or instructors
    • Once all the team members are identified , learners can begin to work on the 3 components – Knowledge, Skills and Attitudes – determining what role each team member plays
  • Agent Based Aiding of Human Teams
    • they expect that the agents have to work in a non-intrusinve manner – they have to be flexible (adapt to user needs) and lightweight (smart)
  • A Contractual Agent Society
    • the main contribution of this paper focuses on implementing the ECM Model with an open smart society of agents built on intelligent-room architecture
    • The ECM is tailored to capture the competence, awareness, knowledge and skills of each individual
    • the AIR framework supports the monitoring and diagnosis of the interactioons between operators and workstations during simulations
    • it can integrate anytime new actors, activities and knowledge assets depending on the target user’s interest, hence triggering collaboration and learning opportunities
    • it can update an ordering of existing entities in a workspace according to their predicted importance to the target user and his/her context, thus increasing the working efficiency
  • The A.I.R Framework:
    • consists of a web service in which the interactions occur between certain members involved in a CoP.
    • each member is defined in terms of his/her behaviour and characteristic variables according to his/her e-Portfolio
    • two kinds of communities are required to qualify learner-acquired knowledge in team training mode: the CoP and the CoC. In CoP, agents engage the learners in social contracts to group learners in the best cohesive CoC.
  • Experiments:
    • Several experiments were conducted to test the system
    • the CoPs were composed of on average 10-12 members which is the norm for UAV missions
    • Roles have been defined with different user’s level, from beginner to expert
    • based on these roles, five training scenarios have been set up
    • two scenarios were wholly simulated, two other scenarios mixed with real and virtual users and one was played with real users only
    • the best results in terms of cohesion came from scenarios with virtual users
    • the presence of real users led to smaller groups than expected, but it is consistent with the assumption of the ECM Model – “trainees are more efficient when working in pairs or small groups of peers.”
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