Abstract
These days, because of the coronavirus, all countries are introducing online university systems. Online universities have the advantage of allowing students to take classes anytime, anywhere, 24 h a day, but lectures are given in a non-face-to-face manner between instructors and students. Thus, while students are taking classes on a web-based basis, the problem arises that concentration on the lectures is significantly reduced. Therefore, in order to solve these problems, in this paper, we propose a level-wise learning algorithm based on the difficulty level of the test problem, and we present the simulation results. In order to improve this problem, in this paper, we propose an automatic music recommendation algorithm based on fuzzy reasoning that can improve the level of learning and lecture concentration, and we show our results on developing a web-based, smart e-learning software. As a result of computer simulation, it was proved that the learning test method, considering by level the difficulty of the test and the incorrect answer rate, was more effective than the existing test method, judged the student’s grades fairly, and improved the risk of unfairly failing the test by 30%.
Highlights
The MATLAB-based fuzzy inference system is based on the fuzzy set mindset, introduced by Professor L
We propose and simulate a level-based learning algorithm based on fuzzy rules
We proposed a level-specific learning algorithm that uses fuzzy reasoning rules for test difficulty and student incorrect answer rate, so that even students with 50 points can pass the test if the difficulty of the test question is difficult and the student error rate is high
Summary
The MATLAB-based fuzzy inference system is based on the fuzzy set mindset, introduced by Professor L. A. Zadeh of the University of Berkeley in 1965, to present ambiguous phenomena, such as natural language, in an open and quantitative manner [1]. In addition using fuzzy theory, products applied to smart home appliances, unmanned vehicles, and automatic military control have emerged. In our commonly known proposition or set, we only deal with objectively clear-meaning things such as true or false. Unlike such an ideal situation, in real life, there are problems where nothing is classified as true or false. Fuzzy theory was born to solve these ambiguous criteria [2]
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