在海事教育中实施创新的方法和学习方法

IF 0.4 Q4 EDUCATION & EDUCATIONAL RESEARCH
Valentina Grancharova, Siyana Lutzkanova
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引用次数: 0

摘要

海事教育一般有两个发展方向:海员培训和港口专家培训。如今,航运业正在迅速变化,在数字化时代,工作人员应该能够使用计算机化信息系统,并了解自动化程序的功能,以响应该领域现有和未来的需求。其主要目标是培养能够在数字时代不断变化的环境中工作的高技能人才。海事人员就业市场的现状要求采用新的方法和更实际的指导学习方法。获得海员或港口专家所需的基础科学和基础工程课程是不够的。他们需要在整个教育和培训课程中获得更多的实践和认知技能。本文讨论了一些用于学习和培训的创新方法和途径,考虑到海事行业的重要变化和正在进行的数字化,并结合必要的软技能管理。所提出的自适应自我纠正培训课程模型是基于作者与学生的海事教育经验以及与具有长期实践经验的海员和港口专家一起进行的实证研究和系统分析方法而开发的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementing Innovative Approaches and Learning Methods in Maritime Education
The maritime education is generally developed in two directions: training of seafarers and port specialists. Nowadays, the shipping business is changing rapidly and in the era of digitalization the working staff should be able to use computerized information systems and to understand the capabilities of automated programs in order to respond to existing and future requirements in this area. The main goal is to prepare highly skilled personnel trained to work in continuous change in the digital era. The current state of the job market for maritime personnel requires the introduction of new approaches and more practically directed learning methods. The acquisition of the basic science and fundamental engineering courses required for seafarers or port specialists are not sufficient. They need to achieve more practical and cognitive skills throughout their education and training courses. This paper discusses some innovative methods and approaches used for learning and training considering important changes and ongoing digitalization in the maritime industry combined with necessary soft skills management. The proposed adaptive self-correcting model of training course is developed based on the authors’ experience in maritime education with students and on empirical method of research and system analyses performed with seafarers and port specialists with long practical experience.
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来源期刊
Pedagogika-Pedagogy
Pedagogika-Pedagogy EDUCATION & EDUCATIONAL RESEARCH-
自引率
33.30%
发文量
72
审稿时长
20 weeks
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