This paper examines some aspects of physical-digital workspaces, focusing on multi-user, multi-touch technologies and how different workspaces impact collaboration. We introduce the concept of globally collaborative work. We chose to use case studies completed by groups of students in an engineering course to test different workspace modalities: the use of a large multi-touch table top in conjunction with a multi-touch board (vertical), the use of tablets with the multi-touch board, and finally the multi-touch board alone. The evaluation criteria are based on modes of interaction which emerge during globally collaborative work sessions: individual work, communication, coordination, cooperation and collaboration. We hypothesized that the workspaces would influence collaborative activity, expecting to see higher rates of collaboration in the table top environment than in the other two modalities studied. However, results showed less co-building and more cooperative work, as students divided their work and later attempted to negotiate a coherent product built on individual contributions. Lastly, we share a few design recommendations based on these results.
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.
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