Abstract
An intelligent learning environment (ILE) can be viewed as a space of interactions between rational agents. The task of the ILE designer is to analyse the structure of this interaction space. In this chapter, we propose to structure it as a sequence of hierarchically integrated sub-spaces. Each interaction sub-space associates a command language with a description language according to a simple chaining principle: the description language of each sub-space corresponds to the command language of the next sub-space. This structure instantiates the neo-Piagetian theory of R.Case, which takes into account both the qualitative changes that occur between two stages (or sub-spaces) with the quantitative change that happen within a stage. The quantitative changes result from the attempt to solve increasingly complex problems and lead to the saturation of the learner’s working memory. It is then necessary to move to a new sub-space that provides the learner with operators for solving similar problems with a reduced cognitive load. The ability to use these new operators is based on the reflective activities triggered by the description language.
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Dillenbourg, P., Mendelsohn, P. (1992). A Genetic Structure for the Interaction Space. In: Costa, E. (eds) New Directions for Intelligent Tutoring Systems. NATO ASI Series, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77681-6_2
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DOI: https://doi.org/10.1007/978-3-642-77681-6_2
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