Towards an Authoring Methodology in Large-Scale E-learning Environments on the Web

Abstract

In this position paper, we make a critical evaluation of some assumptions and paradigms adopted by the AI community during the last three decades, mainly examining the gap between perception and description. In particular, we focus on AI-ED research in the context of distributed learning environments speculating about the content annotation process in authoring systems. The problem of authoring educational content for limited and controlled communities has been extensively studied. This paper tackles the broader problem of authoring for large-scale, distributed, fuzzy communities, as those emerging in modem e-Learning systems on the Web. Differently from other approaches in such authoring environments, we consider epistemological aspects regarding the construction of a domain knowledge model. After this, we deal with aspects of knowledge engineering. Then, this paper describes steps towards a new authoring environments along with a methodology for content annotation in large-scale e-Learning environments on the Web. To support such a methodology, a multi-dimensional approach to model domain knowledge is defined aiming to provide its association with a multi-agent society. © Springer-Verlag 2004.