Adaptive intelligent environment for remote experimentation

Abstract

The use of laboratory experiments is a critically important aspect of engineering education where experience has shown that a complementary approach combining theoretical and practical exercises is vital for effective learning. Increasingly, teaching institutions are offering Web based remote access to distant laboratories as part of an overall e-learning strategy. The design and implementation of effective and usable remote experimentation facilities poses unique challenges given the inherent complexities of the learning environment and the constraints imposed by the delivery medium. Developments in recent years have addressed many of these issues. However autonomous learning environments by their very nature offer minimal educator assistance and from a students perspective it is inevitable that at some stage of the experimental process, context specific help will be required. We address this issue in the context of remote experimentation for embedded systems and present an adaptive intelligent learning environment with intelligent user help.