Intelligent User Support in Autonomous Remote Experimentation Environments

Abstract

Industrial manufacturing systems and processes-related courses cover subject areas that are as diverse as electrical and product engineering and industrial automation. The proliferation of Web-based distance education courses in this area in recent years poses unique challenges for the teaching of a discipline that traditionally involves a high level of practical work. Increasingly, Web-based distance education courses are on offer, augmented by the provision of remote experimentation laboratories facilitating distant access to campus-based physical resources. The design and implementation of effective and usable remote experimentation facilities is a difficult task, given the inherent complexities of the learning environment and the constraints imposed by a Web-based delivery medium. Developments in recent years have addressed many of these issues as this constant innovation has necessitated educational institutions and other training providers to continually reassess the content and delivery of engineering curricula in the context of this developing field. However, autonomous learning environments, by their very nature, offer minimal educator assistance, and from a student's perspective, it is inevitable that, at some stage of the experimental process, context-specific help will be required. This paper seeks to address this issue in relation to the practical aspect of Web-based engineering-related courses and presents an intelligent help system to support students in the practical use of autonomous learning environments for remote experimentation.