FIS/IFIS Modeling in Professional and Collaborative Learning: A Systemic Approach

FIS/IFIS Modeling in Professional and Collaborative Learning: A Systemic Approach

Copyright: © 2015 |Pages: 29
DOI: 10.4018/978-1-4666-8705-9.ch012


In this chapter, the capability of the fuzzy inference systems (FISs) to model and provide evaluations in the educational context is further explored through the merits of the intuitionistic fuzzy inference systems (IFISs). The Intuitionistic Fuzzy Logic enables the capture and expression of uncertainty and hesitancy with an IFIS model, thus it extends the fuzzy logic capabilities. In this chapter, the purpose and function of the FIS/IFIS modeling, when embedded in an instructional design (ID), is further examined from Boulding's systemic perspective. Elaborations of the latter provide a framework for handling the complexity of the above interplay and clarify the aim and the role of the presented modeling approaches. The ID and FIS/IFIS modeling upon experimental data from their materialization in two educational cases in the area of professional learning and computer supported collaborative learning, respectively, serve as the test-bed for the potentiality of the presented explorations.
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According to Reigeluth (1997, p. 44), instruction is “anything that is done to help someone learn,” and Instructional Design (ID) aims at offering “guidance for improving the quality of that help.” ID refers to the practice of analysis of the learning needs upon which tools and content are systematically built, in order to facilitate learning. This chapter presents efforts in two levels, to enhance the ID with new insights of the fuzzy logic (FL) modeling.

In the first level, a broad systemic perspective of the ID, based on Boulding’s typology, provides opportunities for its innovative enhancement in a more parsimonious, yet efficient design that enriches its learning ecology (Bronfenbrenner, 1979). Boulding’s typology of system complexity (Boulding, 1956) provides a framework for the realization of the structure and function of any complex system within a hierarchy of nine level subsystems. Checkland’s (1981) elaboration of the typology allowed for a deeper understanding of its function, whereas further elaboration of the typology by Gabriele (1997, 2010) contributed to the realization of the structure and function of all the levels, specifically for school and classroom complex systems. When the tangible and/or intangible artefacts that facilitate learning (Wartofsky, 1979; Engeström, 1999) are reflected to the proposed levels of Boulding’s typology (see chapter 10 for examples of their implementation in computer supported collaborative learning (CSCL)), new insights to their design and function can be realized. This approach entails new interpretations to the FIS models, which serve as artefacts that are integrated in the ID.

In the second level, further elaboration of the Fuzzy Inference System (FIS) modeling is explored, on the basis of the enhancement of modeling the expert knowledge that it functions upon. As it is described in previous chapters (see chapters 8 and 10), the evaluation inferences of a FIS are performed on the basis of modeling the knowledge of the domain expert (e.g., the professor). Moreover, when these evaluations are of formative character, they can facilitate learning by providing intelligent support. However, the vagueness of evaluating the human behavior might provide some hesitance to the expert, while s/he is expressing it. On the basis of the Intuitionistic Fuzzy Logic (IFL), Intuitionistic FIS (IFIS) can model this hesitance and, thus, provide better modeling of the reality, i.e., the expert's knowledge (see chapter 8).

In this chapter, two cases of ID with FIS/IFIS modeling embedded to them are presented, in order to exemplify the above elaborations, as follows:

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