Choose qualified instructor for university based on rule-based weighted expert system
Sana Karimian

TL;DR
This paper presents a fully automatic, rule-based expert system for selecting qualified university instructors, utilizing decision trees, weighting algorithms, and majority voting to improve accuracy and efficiency.
Contribution
It introduces a novel rule-based expert system with weighting and voting mechanisms for instructor selection, enhancing automation and transparency.
Findings
Achieved 85.55% accuracy on real university data.
System demonstrates robustness and low computational complexity.
Features include simplicity of implementation and transparency.
Abstract
Near the entire university faculty directors must select some qualified professors for respected courses in each academic semester. In this sense, factors such as teaching experience, academic training, competition, etc. are considered. This work is usually done by experts, such as faculty directors, which is time consuming. Up to now, several semi-automatic systems have been proposed to assist heads. In this article, a fully automatic rule-based expert system is developed. The proposed expert system consists of three main stages. First, the knowledge of human experts is entered and designed as a decision tree. In the second step, an expert system is designed based on the provided rules of the generated decision tree. In the third step, an algorithm is proposed to weight the results of the tree based on the quality of the experts. To improve the performance of the expert system, a…
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Taxonomy
TopicsEducational Technology and Assessment · Advanced Computational Techniques and Applications
