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Artificial Intelligence Approach to Evaluate Students' Answerscripts Based on the Similarity Measure between Vague Sets
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Journal of Educational Technology & Society Volume 10, Number 4, ISSN 1176-3647 e-ISSN 1176-3647

Abstract

In this paper, we present two new methods for evaluating students' answerscripts based on the similarity measure between vague sets. The vague marks awarded to the answers in the students' answerscripts are represented by vague sets, where each element u[subscript i] in the universe of discourse U belonging to a vague set is represented by a vague value. The grade of membership of u[subscript i] in the vague set A is bounded by a subinterval [t[subscript A](u[subscript i]), 1 - f[subscript A] (u[subscript i])] of [0, 1]. It indicates that the exact grade of membership [mu][subscript A](u[subscript i]) of u[subscript i] belonging the vague set A is bounded by t[subscript A](u[subscript i]) [less than or equal to] [mu][subscript A](u[subscript i]) [less than or equal to] 1 - f[subscript A](u[subscript i]), where t[subscript A](u[subscript i]) is a lower bound of the grade of membership of u[subscript i] derived from the evidence for u[subscript i], f[subscript A](u[subscript i]) is a lower bound of the negation of u[subscript i] derived from the evidence against u[subscript i], t[subscript A](u[subscript i]) + f[subscript A](u[subscript i]) [less than or equal to] 1, and u[subscript i][is an element of] U. An index of optimism [lambda] determined by the evaluator is used to indicate the degree of optimism of the evaluator, where [lambda] [is an element of] [0, 1]. Because the proposed methods use vague sets to evaluate students' answerscripts rather than fuzzy sets, they can evaluate students' answerscripts in a more flexible and more intelligent manner. Especially, they are particularly useful when the assessment involves subjective evaluation. The proposed methods can evaluate students' answerscripts more stable than Biswas's methods (1995). (Contains 10 tables and 3 figures.)

Citation

Wang, H.Y. & Chen, S.M. (2007). Artificial Intelligence Approach to Evaluate Students' Answerscripts Based on the Similarity Measure between Vague Sets. Journal of Educational Technology & Society, 10(4), 224-241. Retrieved November 22, 2019 from .

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