Using Reinforcement Learning Algorithms to Optimize Practical Skills Development in Higher Vocational and Technical Education

Abstract

Optimizing practical skills development in higher vocational and technical education involves a multifaceted approach. Firstly, curriculum design should integrate hands-on learning experiences, industry-relevant projects, and internships to bridge the gap between theoretical knowledge and real-world application. Secondly, institutions should invest in state-of-the-art facilities, equipment, and technology to provide students with a simulated work environment conducive to skill mastery. This paper, explored the integration of reinforcement learning algorithms within the Seahorse Optimization Probability Education (SHOPE) framework to optimize practical skills development in higher vocational and technical education. Through extensive experimentation and analysis, we investigate the effectiveness of various reinforcement learning algorithms in enhancing vocational teaching strategies, skill assessment processes, and classification tasks within educational contexts. Our findings highlight the capability of SHOPE to iteratively refine teaching methodologies, leading to significant improvements in student skill acquisition, success rates, and classification accuracy over multiple epochs. With the adaptive teaching strategies, and optimized vocational education programs tailored to individual student needs. For instance, our results demonstrate an average skill improvement ranging from 35% to 65% across different reinforcement learning algorithms. Moreover, success rates for mastering targeted skills reach levels between 75% and 92%.