Session T3A LEARNING ACHIEVEMENT EVALUATION STRATEGY USING FUZZY MEMBERSHIP FUNCTION

Therefore, evaluation series of:
(3) (4)
E (V ) = φ ( P1 ), φ ( P2 ), L, φ ( Pn )
Uφ (P ) = φ ( P ),φ (P
i i1 i =1
n
i2
),L, φ (Pim )
A teacher first puts each student in the GOOD or BAD categories, where further there are such sub-evaluation components as complexity(COMPLEX and SIMPLE); importance(IMPORTANT and NOT IMPORTANT); and difficulty(DIFFICULT and EA SY). These sub-components (complexity, importance, and difficulty) are set as linguistic
INTRODUCTION
With the marked-down CPU price and the highly-developed computer producing technology, the number of personal computers(PCs) has been on the constant rise. And due to the development of multimedia computing technology, the single media -based Computer Assisted Instruction(CAI) system has been furthered into a multi media -based CAI system. CAI has been studied from the early 1970s, and with the recent assistance of Artificial Intelligence(AI), the Intelligent CAI(ICAI) has become a learners' auxiliary study aid[6]. The following are the four major CAI components: domain expertise module, student model module, tutoring module, and interface module, all of which have structured sub-functions. Of them the evaluation function in domain expertise module has a special importance, because a proper evaluation has to be made to set an appropriate learnercentered strategy in fostering the best learning environment[2]. Having a procedural convenience, the conventional learning achievement evaluation method, using the grading system (A, B, C, etc), has been commonly practiced in spite of its drawback in the human teachers’ different evaluation criteria. However, there has been a lot of talk about a new evaluation system at elementary schools, for it aims at an enhanced evaluation system in which ways of linguistic expressions are priced. Adopting the Zadeh and Yager’s fuzzy theories[8]-[9],
0-7803-6669-7/01/$10.00 © 2001 IEEE October 10 - 13, 2001 Reno, NV 31 st ASEE/IEEE Frontiers in Education Conference T3A-19
Session T3A
variables, and at the same time IMPORTANT, MEDIUM, EASY are set to be fuzzy variables. Allotting fuzzy variables to given lingual variables, this new evaluation method makes up for the conventional formulas and rules. Precedent Studies Focusing on the previously-mentioned components: importance and complexity, Weon led a studious research on a reliable fuzzy evaluation method, which has a drawback that cannot integrate each independent fuzzy environment[7]. Law also proposed the learning grading method using fuzzy numbers, yet this demands complex mathematical process, nor does it integrate various fuzzy environments, either[4]. Kitagaki, in Japan, is a proponent of fuzzy evaluation method[3]. Nagaoka proposed a fuzzy evaluation method using a matrix system, which analyzes each student’ s response time[5]. Hajime later suggested fuzzy evaluation method that has subjective evaluation elements like calligraphy[1]. These methods, however, ended up at an experimental stage or could not integrate various fuzzy environments. As a consequence, the development of a new evaluation module that can reflect the demand of the educational field has been necessary.
INTELLECTUAL EVALUATION
Concepts of Intellectual Evaluation Learning achievement evaluation indicates how much a learner achieves by a set standard. Generally, learning achievement evaluation can be defined as a composite and hierarchical process where each element has sub-elements. In this study, if E stands for learning achievement evaluation, the evaluation function E of the subevaluation elements
1 2
Pi (1 i n) becomes φ ( P ) [1].
(1)
E = φ ( P1 , P2 ,L, Pn )
And evaluation elements
Pi (1 i n) of E equal ψ ( Pij ) of the sub-evaluation elements Pij (1 j m). Pi = ψ ( Pi1 , Pi 2 ,L , Pim )
Sunghyun Weon, Catholic University of Pusan, Sc hool of Information Engineering, Pusan, Korea, 609-757 shwon@cup.ac.kr Jinil Kim, Dongeui University, Department of Computer Engineering, Pusan, Korea, 614-714 jikim@dongeui.ac.kr
Session T3A LEARNING ACHIEVEMENT EVALUATION STRATEGY USING FUZZY MEMBERSHIP FUNCTION
Sunghyun Weon1 and Jinil Kim 2
Abstract In this paper, we suggest a new learning achievement evaluation strategy in student's learning procedure. We call this as fuzzy evaluation. We may assign fuzzy lingual variables to each question pertaining to its importance, complexity and difficulty by using fuzzy membership functions. Then we can evaluate a score depending on the membership degree of uncertainty factors in each question. In addition, we consider the time consuming element for solving a question. We adapt inverse sigmoid function to consider time consuming element, fuzzy concentration and dilation function for importance, a sigmoid function for complexity, and fuzzy square method for difficulty. Index Terms response accuracy, fuzzy theory, CAI, learning achievement evaluation, fuzzy membership grade this study aims to propose a reliable fuzzy evaluation method which can make up for the conventional approaches’ shortcomings: these methods barely consider such vague factors as complexity, importance, and difficulty. To this aim, this study comes up with the evaluation module, setting an evaluation formula for which fuzzy membership function is used. This module evaluates the learning achievements automatically, thus giving the concurrent evaluation results to students.