Didactic Networks: A Proposal for e-learning Content Generation

The Didactic Networks proposed in this paper are based on previous publications in the field of the RSR (Rhetorical-Semantic Relations). The RSR is a set of primitive relations used for building a specific kind of semantic networks for artificial intelligence applications on the web: the RSN (Rhetorical-Semantic Networks). We bring into focus the RSR application in the field of elearning, by defining Didactic Networks as a new set of semantic patterns oriented to the development of elearning applications. The different lines we offer in our research fall mainly into three levels: (1) The most basic one is in the field of computational linguistics and related to Logical Operations on RSR (RSR Inverses and plurals, RSR combinations, etc), once they have been created. The application of Walter Bosma's results regarding rhetorical distance application and treatment as semantic weighted networks is one of the important issues here. (2) In parallel, we have been working on the creation of a knowledge representation and storage model and data architecture capable of supporting the definition of knowledge networks based on RSR. (3) The third strategic line is in the meso-level, the formulation of a molecular structure of knowledge based on the most frequently used patterns. The main contribution at this level is the set of Fundamental Cognitive Networks (FCN) as an application of Novak's mental maps proposal. This paper is part of this third intermediate level, and the Fundamental Didactic Networks (FDN) are the result of the application of rhetorical theory procedures to the instructional theory. We have formulated a general set of RSR capable of building discourse, making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. The Instructional knowledge can then be elaborated in the same way. This network structure expressing the instructional knowledge in terms of RSR makes the objective of developing web-learning lessons semi-automatically possible, as well as any other type of utilities oriented towards the exploitation of semantic structure, such as the automatic question answering systems.