Abstract submission for the
2nd Annual Workshop on System Aspects of Sharing a Virtual Reality,
Collaborative Virtual Environments 1998 (CVE'98)
(An ascii-only version is available)
Patrick Jermann and Daniel Schneider
Faculté de psychologie et des sciences de l'éducation,
Université de Genève
9 route de Drize, CH-1227 Carouge, Switzerland
Email: Patrick.Jermann@tecfa.unige.ch and Daniel.Schneider@tecfa.unige.ch
In Computer supported collaborative learning (CSCL) and groupware research a lot of effort has been spent on developing appropriate WYSIWIS ("what you see is what I see) technology for collaboration. It enables to see what the other sees, what the other does and where the other looks, i.e. to share the same object and work with it from the same perspective. In a MUVE context, pure WYSIWIS does usually not make much sense, since people might look at objects from different perspectives (especially in 3D environments) or perform other tasks in parallel. However "relaxed" WYSIWIS does, especially in the context of people working together on the same task in a same "room". In a recent research proposal we suggested to study three kinds of awareness in a VRML-based 3-D environment: location awareness (displaying a map showing the position of each participant), pointing/selection awareness (high-lighting the object the partner is pointing to or has selected) and gaze awareness (a small window displaying the partners view of the scene).
More generally, there is a wide range of potential for awareness tools. As group-researcher Greenberg points out: we need group-aware versions of single user widgets and novel widgets to support particular aspects of group work, like participant status (e.g. comings and goings), telepointers (e.g. gesturings with the mouse to point out relations between the artifacts on the display), workspace awareness (informing a participant about where other people are working in the shared work-surface and what they are doing).
We also need tools to collect and display synthetic information to the participants and that will help them to collaborate better, and accordingly we plan to implement special agents for this task.
In the AI-Ed community agents refer to tutors, coaches, co-learners and such whereas in the WWW community they often name some kind of smart search engine. Similar types of agents are now investigated in Internet-based MUVE systems. We classify several types of agents and propose a new type of agent, an observer, that calculates and displays statistics regarding collaboration: