Open AccessComputerized Adaptive Test based on Sugeno Fuzzy Inference System
Author(s) -
Wrastawa Ridwan,
Ifan Wiranto,
Rahmat Deddy Rianto Dako
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1098/3/032077
Subject(s) - fuzzy inference system , computerized adaptive testing , inference , test (biology) , fuzzy logic , adaptive neuro fuzzy inference system , fuzzy inference , artificial intelligence , mathematics , computer science , subject (documents) , fuzzy control system , machine learning , statistics , paleontology , library science , biology , psychometrics
Along with the development of information and communication technology, assessment of student learning outcomes is no longer carried out in the form of written examinations but instead carried out with a Computerized Adaptive Test (CAT). CAT is adaptive because it allows the items given are selected according to the ability of students. Therefore, the CAT needs a method to estimate student ability. This research aims to design a Sugeno Fuzzy Inference System (SFIS) to estimate student ability. This fuzzy system consists of twelve IF-THEN rules, with four inputs, namely the student’s answer, the probability of students being able to answer correctly, the level of difficulty and discrimination of the questions. The output is an estimated ability, divided into five levels, namely very low, low, average, great, and excellent. Fuzzy system simulation is performed on linear algebra course (consist of six subject) with multiple-choice questions. The simulation results show that the SFIS can estimate the ability of students by working on a maximum of six items each subject. The estimated value of student ability obtained which is not much different from previous research (with twenty four rules). Therefore, this proposed system is more efficient.