Enhancing the Performance of Vocational Education in the Digital Economy with the Application of Fuzzy Logic Algorithm

Abstract

Abstract Vocational education improves the skill and efficiency of students/learners in addition to their regular courses. Within a short period of such courses, the performance has to be improved for providing professional development. In this article, the fuzzy-based performance improvement validation method (FPIVM) is introduced. This method excels in analyzing the performance of instructor-centered vocational education improvements for varied learners. In this process, the differential performance between various training and learning sessions is identified for identifying the gap in skill improvement. The fuzzy process operates using continuous intervals for performance measures based on instructor and learner scores. This is synchronized based on the existing learner’s skill and the instructor’s efficiency in meeting the vocational course study level. In particular, the fuzzification over the independent (learner and trainer) skill score is updated for new intervals. Such skill scores are classified as high or low compared to the previous outcomes. This improves the change in instructor or mode of education for successive sessions. Thus, the quality and performance of the sessions are retained unanimously for providing better outcomes. The outcomes are revised after each session for sustaining a high learning score regardless of student density.

Keywords

• Fuzzy logic
• Linear derivatives
• Skill analysis
• Vocational training