Vocational Education Skill Assessment and Intelligent Assistance: A Study on the Application of Machine Learning Algorithms in the Assessment of Vocational Information Literacy Teaching Ability

Abstract

Investigating vocational educators' knowledge-based teaching skills across China's Vocational Education (VE) institutions, this research focuses on the practical use of Machine Learning (ML) algorithms. Instructors' efficacy must be evaluated, and this work addresses the gap. VE performs an essential role in connecting learning abilities with the demands of industry. The investigation plans on developing an adaptable, subjective assessment technique that extends within the boundaries of conventional subjective evaluation methods using modern ML techniques such as Support Vector Machines (SVM), Decision Trees (DT), and Neural Networks (NN). Each ML model's accuracy, reliability, and feasibility have been determined using data collected from 120 vocational educators encompassing various fields and regions. Researchers predict that our findings will provide perspective on how to improve vocational education settings' teaching methods and governance.