Abstract.Several approaches using fuzzy techniques have been proposed to provide a practical method for evaluating student academic performance. However, these approaches are largely based on expert opinions and are difficult to explore and utilize valuable information embedded in collected data. This paper proposes a new method for evaluating student academic performance based on data-driven fuzzy rule induction. A suitable fuzzy inference mechanism and associated Rule Induction Algorithm is given. The new method has been applied to perform Criterion-Referenced Evaluation (CRE)and comparisons are made with typical existing methods, revealing significant advantages of the present work. The new method has also been applied to perform NormReferenced Evaluation (NRE), demonstrating its potential as an extended method of evaluation that can produce new and informative scores based on information gathered from data.

The use of fuzzy quantifiers in linguistic fuzzy models helps to build fuzzy systems that use linguistic terms in a more natural way. Although several fuzzy quantification techniques have been developed, the application of the existing techniques seems very limited. This paper proposes an application of fuzzy quantification to replace crisp weights in subsethood-based fuzzy rule models. I n addition to the concern that fuzzy models should have high accuracy rate, attention has also been taken t o maintain the simplicity of the generated fuzzy model. The objective is to produce quantifier-based fuzzy models which are not only readable but also practically applicable. T h e quantifier based fuzzy model is then applied to classification tasks. The classification accuracies of fuzzy models that use crisp weights, continuous quantifiers, multi-valued quantifiers and two-valued quantifiers are compared. Experimental results show that the classification accuracy of the fuzzy model that uses continuous quantifiers is: 1) as good as the classification accuracy of the fuzzy models that use crisp weights, and 2) in most cases, better than fuzzy models that use multi-valued quantifiers or two-valued quantifiers.

No abstract

Likert-type scale that employs ordinal values to represent linguistics terms has been very popular in the studies on job satisfaction evaluation. In this work, it is argued that the use of ordinal values in Likert scale does not offer the best way in representing the linguistic terms. This paper proposes the use of fuzzy sets to represent linguistic terms in Likert-type scale and employs the technique using fuzzy conjoint method in job satisfaction evaluation. Experimental results show that the analysis using fuzzy conjoint method produced consistent result compared to the analysis using the percentage. However, the fuzzy membership values obtained from fuzzy conjoint method can be used to compare the decisions between criteria used to measure job satisfaction, and hence is very useful in providing additional information for decision-making.