Flexible and Exploratory Learning by Polyscopic Topic Maps

Flexible and active education calls for a comprehensive restructuring of the traditional university course format. Such restructuring can be done in a natural and coherent way by implementing Topic Maps in e-learning. In this paper we describe Polyscopic Topic Maps, an information structuring method which is being developed by the authors, a university course model called Flexplearn which is based on this approach, and a prototype Flexplearn course where our model is currently being implemented, used and tested. We also present BrainBank Learning, a Topic Map-driven application, for learning of concepts and concept relations that we are implementing with Flexplearn.

• Type: Conference
• Year: 2005
• Where: ICALT
• Authors: Dino Karabeg, Rolf Guescini, Tommy W. Nordeng
• Keywords: ICALT 2005, Machine Learning, Topic Map-driven Application, Topic Maps, University Course