A Computational Cognitive-Based Approach to Represent Knowledge within Intelligent Tutoring Systems

In this paper we present some characteristics of a new computational model to represent knowledge within intelligent tutoring systems. This model is inspired (1) by research in artificial intelligence on modelling, structuring and organising knowledge and (2) by theories of cognitive psychology which explain the human cognitive activity in terms of memory subsystems and their processes. Here, we describe one originality of our novel approach : the parsimonious use of cognitive structures suggested by psychology to encode the knowledge to be taught. Primitive units of semantic and procedural knowledge, chosen with a small level of granularity, are used to build complex knowledge entities which are dynamically combined in order to represent the learner cognitive activity. Traces of this activity are used as episodic knowledge which is specific to each learner. Episodic knowledge analysis allows to retrieve semantic and procedural knowledge and to establish their acquisition level in function of their context of usage.