371 DOKGETT – An Authoring Tool for Cognitive Model-based Generation of the Knowledge Mehdi Najjar, Philippe Fournier-Viger, André Mayers and Jean Hallé Department of Computer Science, University of Sherbrooke 2500 Bld. de l’Université, Sherbrooke, Qc J1K 2R1 Canada mehdi.najjar@usherbrooke.ca Abstract In this paper we present an authoring tool milieu that permits modelling graphically any subject-matter domain knowledge and transposing it automatically into related XML files. Generated contents serve as a tutor reasoning support when interacting with students engaged in learning activities through virtual learning environments. 1. Introduction An important technological concept is being considered by an increasing number of universities and revolves about the idea of virtual learning environments. Nevertheless, when building such environments that provide specific teaching material and exploit tech-based features, a key issue should be addressed – the need to have tools which ease representing and modeling the knowledge and which are used by professors and experts of the taught domains without the obligation of high capabilities in computer science at their disposal (for example, the mastery of specification and/or programming languages). This paper presents DOKGETT, a DOmain Knowledge GEnerator auThoring Tool which attempts to offer a user- friendly environment that allows to model graphically any subject-matter domain knowledge and to transpose it automatically into related XML files. Those are generated to serve as knowledge support for a tutor reasoning purpose when interacting with students engaged in learning activities through virtual learning environments. The remainder of the article is organised as follows. First, the theoretical approach adopted for the knowledge representation is presented. Next, the DOKGETT environment is described. In section 4, we present a virtual learning environment prototype created from knowledge specifications made by means of DOKGETT, followed by brief early pilot tests description. Finally, current developments are mentioned. 2. The theoretical approach Different approaches in cognitive psychology propose various sets of knowledge representation structures not according to their contents but according to the way in which these contents are handled and used. [5,7]. However, these sets are not necessarily compatible [2,8]. Although there is neither consensus on the number of the subsystems nor on their organisation, the majority of the authors in psychology mentions (in some form or another) three main subsystems presenting, each one, a particular type of knowledge: (i) semantic knowledge [11], (ii) procedural knowledge [12] and (iii) episodic knowledge [13]. Our model regards semantic knowledge as concepts taken in a broad sense. Thus, they can be any category of objects. Moreover, we subdivide concepts in two categories: primitive concepts and described concepts. The first is defined as a syntactically non-split representation; i.e., primitive concept representation cannot be divided into parts. For example, in propositional calculus, symbols "a" and "b" of the expression "(a & b)" are non-split representations of the corresponding proposals. On the other hand, described concepts are seen as syntactically decomposable representations. For example, the previous expression "(a & b)" is a decomposable representation that represents a conjunction between proposal "a" and proposal "b", two primitive concepts. The symbol "&" represents the conjunction logic operator (AND), and is a primitive concept. In this way, the semantic of a described concept is given by the semantics of its components and their associations which take those components as arguments to create the described concept [10]. In opposition to semantic knowledge, which can be expressed explicitly, procedural knowledge is inferred by a succession of actions achieved automatically – following internal and/or external stimuli perception – to reach desirable states. In this sense, a procedure is a mean of satisfying needs without using the attention resources. The automation via the use of procedures reduces the cognitive complexity of problems solving [12]. For example, in propositional calculus, substituting automatically "~T" by "F" making abstraction of the explicit call of the truth constant DOKGETT – An Authoring Tool for Cognitive Model-based Generation of the Knowledge Proceedings of the 5 th IEEE International Conference on Advanced Learning Technologies (ICALT 05). July 5-8, Kaohsiung, Taiwan. 0-7695-1967-8/05 $17.00 © 2005 IEEE