From the Collaborative Environment of Remote Laboratories to the Global Collaboration

Jan Machotka and Zorica Nedic

The remote laboratory (RL) can be considered as a modern collaborative learning environment, where students acquire skills required for efficient collaboration and communication on a local and global scale, both today and in the near future. The majority of current existing RLs are not constructed to allow the involved participants to collaborate in real time. This paper describes the collaborative RL NetLab, developed at the University of South Australia (UniSA), which allows up to three onshore and/or offshore students to conduct remote experiments at the same time as a team. This allows the online RL environment to become very similar, if not nearly identical to its real laboratory counterpart. The collaboration in the real laboratory is replaced by the “global” on-line collaboration.

Coping with collaborative and competitive episodes within collaborative remote laboratories

Christophe Gravier, Jacques Fayolle and Bernard Bayard

In this paper, we provide an original approach to the support of group awareness within collaborative remote laboratories. Computer Supported Collaborative Learning sessions present successively collaborative and emulation episodes. The idea developed here is the elaboration of an architecture for dealing with those two aspects of collaborative sessions for practical remote hands-on approaches. Our purpose is to manage and enhance the learning experience brought to the students who are using collaborative remote laboratories by managing several synchronous accesses made on the remote laboratories platform itself. This contribution relies on an original domain ontology and the associated knowledge management system.

Trends in Discrete Event Simulation

Eduard Babulak and Ming Wang

Discrete event simulation technologies have been up and down as global manufacturing industries went through radical changes. The changes have created new problems, challenges and opportunities to the discrete event simulation. On manufacturing applications, it is no longer an isolated model but the distributed modeling and simulation along the supply-chain. In order to study the hybrid manufacturing systems, it is critical to have capability to model human performance with different level of skills and under various working conditions. On service applications, the most critical part is to model knowledge workers and their decision making process. This paper reviews the discrete event simulation technologies; discusses challenges and opportunities presented by both global manufacturing and the knowledge economy.