Abstract
AbstractIn this chapter we outline a decisions-making approach (DMA) that is based on the representation and simulation of causal phenomena. It applies an extension of the traditional Fuzzy Cognitive Maps called Rules-based Fuzzy Cognitive Maps (RBFCM). This version depicts the qualitative flavor of the object to be modeled and is grounded on the well-sounded fuzzy logic. As a result of a case study in the educational field, we found empirical evidence of the RBFCM usefulness. Our DMA offers decision-making services to the sequencing module of an intelligent and adaptive web-based educational system (IAWBES). According to the student-centered education paradigm, an IAWBES elicits learners’ traits to adapt lectures to enhance their apprenticeship. This RBFCM based DMA models the teaching-learning scenery, simulates the bias exerted by authored lectures on the student’s learning, and picks the lecture option that offers the highest achievement. The results reveal that the experimental group reached higher learning than the control group.KeywordsDomain KnowledgeLearning ObjectIntelligence QuotientFunctional SystemContent ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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