Fuzzy Logic Theory and Applications in Uncertainty Management of Linguistic Evaluations for Students

Fuzzy logic can deal with information arising from perception and cognition that is uncertain, imprecise, vague, partially true, or without sharp boundaries. Fuzzy logic can be used for assigning linguistic grades and for decision making and data mining with those linguistic grades by teachers, instructors, and professors. Many aspects of fuzzy logic including fuzzy sets, linguistic variables, fuzzy rules, fuzzy math, fuzzy database queries, computational theory of perceptions, and computing with words are useful in uncertainty management of linguistic evaluations for students. This chapter provides many examples of this after describing the theory of fuzzy logic.

Computing with words for student peer assessment in oral presentation

Peer assessment in an oral presentation can motivate and give more sense of responsibility to students. In recent years, various methods have been proposed to evaluate peers. In this paper, a novel peer online assessment method is proposed for oral presentation using perceptual computing. The output of the proposed system can be a numerical score for the overall assessment of a student in the presentation, which allows comparison and ranking of student performance. Furthermore, a linguistic evaluation that describes the student's performance is obtained from the system. A case study has been conducted to show the effectiveness of the proposed method; then the results are analyzed and reviewed.

A Consensus Model for Extended Comparative Linguistic Expressions with Symbolic Translation

Consensus Reaching Process (CRP) is a necessary process to achieve agreed solutions in group decision making (GDM) problems. Usually, these problems are defined in uncertain contexts, in which experts do not have a full and precise knowledge about all aspects of the problem. In real-world GDM problems under uncertainty, it is usual that experts express their preferences by using linguistic expressions. Consequently, different methodologies have modelled linguistic information, in which computing with words stands out and whose basis is the fuzzy linguistic approach and their extensions. Even though, multiple consensus approaches under fuzzy linguistic environments have been proposed in the specialized literature, there are still some areas where their performance must be improved because of several persistent drawbacks. The drawbacks include the use of single linguistic terms that are not always enough to model the uncertainty in experts’ knowledge or the oversimplification of fuzzy information during the computational processes by defuzzification processes into crisp values, which usually implies a loss of information and precision in the results and also a lack of interpretability. Therefore, to improving the effects of previous drawbacks, this paper aims at presenting a novel CRP for GDM problems dealing with Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) for modelling experts’ linguistic preferences. Such a CRP will overcome previous limitations because ELICIT information allows both fuzzy modelling of the experts’ uncertainty including hesitancy and performs comprehensive fuzzy computations to, ultimately, obtain precise and understandable linguistic results. Additionally, the proposed CRP model is implemented and integrated into the CRP support system so-called A FRamework for the analYsis of Consensus Approaches (AFRYCA) 3.0 that facilitates the application of the proposed CRP and its comparison with previous models.