Contextual Support for Remote Cooperative Troubleshooting: Lessons From a Naturalistic Study

Distributed troubleshooting often requires experts to support non-experts at a distance. The resulting separation between collaborators can impoverish their understanding of each other’s task contexts, impeding their collaboration. Consequently, this paper argues that developing effective troubleshooting support systems requires determining (1) what contextual information the participants require to perform their tasks, and (2) how support systems can be designed to help provide that contextual information. Based on an ethnographic study of real-world remote diagnosis of electronic devices by ad hoc teams, we have identified 12 types of contextual information affecting the remote interaction of expert and non-expert troubleshooters, and we argue that software tools for remote cooperative troubleshooting should be explicitly designed to bridge the gap between participant contexts by capturing and transmitting these types of information. As a means for performing this task, we propose a support framework based on conversational case-based reasoning. The paper illustrates the application of this approach in an implemented testbed system.