Title :

Interaction Model and Respect of Rules to Enhance Collaborative Brainstorming Results

Author(s) :

Gidel, Thierry [Auteur]
Connaissance Organisation et Systèmes TECHniques [COSTECH]
Tucker, Andrea [Auteur]
Centre Interuniversitaire de Recherche en Education de Lille - ULR 4354 [CIREL]
Fujita, Shigeru [Auteur]
Moulin, Claude [Auteur]
Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]
Sugawara, Kenji [Auteur]
Chiba Institute of Technology [CIT]
Suganuma, Takuo [Auteur]
Kaeri, Yuki [Auteur]
Chiba Institute of Technology [CIT]
Shiratori, Norio [Auteur]
Research Institute of Electrical Communication [Sendai] [RIEC]

Journal title :

Advances in Science, Technology and Engineering Systems Journal

Pages :

484-493

Publisher :

Advances in Science Technology and Engineering Systems Journal (ASTESJ)

Publication date :

2020

HAL domain(s) :

Sciences de l'ingénieur [physics]/Autre
Sciences de l'Homme et Société/Education

English abstract : [en]

This paper presents a collaborative interaction model (CIAO) and several behaviour rules that could enhance brainstorming results. The model is composed of five elements which may be used during brainstorming sessions, ...
Show more >This paper presents a collaborative interaction model (CIAO) and several behaviour rules that could enhance brainstorming results. The model is composed of five elements which may be used during brainstorming sessions, consisting of activities performed by participants which are characteristic of different modes of interaction. Some sequences of these interactions may be considered more or less adequate than others. In particular, in order to get better results during brainstorming activities participants must respect certain rules when they write their ideas and when they consider notes written by somebody else. We argue that a multi-agent system can recognize different interaction modes and verify the respect of these rules by analyzing videos and notes produced by the participants in real time. Such a system must be trained as a machine learning system before being used during actual meetings. This system can simplify the role of the meeting facilitator. It can send a summary of the meeting situation, such as the proportion of each mode of interaction and identify behaviors that may need to be addressed. We present how feedback could be sent individually or addressed to the entire team. We will begin by presenting the interaction model, then propose an automatic recognition of these modes from video recordings and log analysis. We also address the necessity of rules and the structure of a multi-agent system which is able to verify whether or not the participants are respecting them. Finally, we propose how experiments could show the level of acceptance of such a system by users. The goal of this prospective research is to define a non-intrusive system that can be used during brainstorming sessions, based on an interaction model to enhance the quality of meeting results.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre Interuniversitaire de Recherche en Éducation de Lille (CIREL) - ULR 4354

Source :

Harvested from HAL