Adaptive System for Collaborative Online Laboratories

Christophe Gravier (1) , Jacques Fayolle (1) , Jeremy Lardon (1) , Martin J. O'Connor (2)

1 LT2C - Laboratoire Telecom Claude Chappe
2 Stanford University

In the last decade, researchers in the Online Engineering field have attempted to provide hands-on, web-based approaches for Distance Learning. The primary goal of this research is to produce online laboratories that serve as the educational substitute for in situ laboratories. A limitation of existing online laboratories, however, is that they generally only allow a single user to be connected at a time. Since group learning activities, such as peer assistance, peer emulation, and collaborative experimental setup, are core dimensions of the traditional laboratory experience, this shortcoming is a significant pedagogical bottleneck. Recent research has focused on creating Collaborative Online Laboratories (COL) which attempt to address this shortcoming by focusing on the group awareness aspect of the laboratory learning experience. This paper discusses how group awareness can serve as a key component in replicating the collaborative aspect of learning in local laboratories. We discuss strategies for describing group awareness and how these strategies are associated both with a tutor's pedagogical objectives and in the management of the group of collaborating students. We describe an experimental system that we have developed that uses Semantic Web technologies to define a knowledge-driven system that allows researchers to describe and execute a variety of collaborative strategies for online laboratories.

Domaines: Web

Christophe Gravier, Jacques Fayolle, Jeremy Lardon, Martin J. O'Connor. Adaptive System for Collaborative Online Laboratories. IEEE Intelligent Systems, 2012, 27 (4), pp.11-17. ⟨hal-00991028⟩