A Training Model for Urban Railway Transit Disciplines Postgraduate Based on Real-Problem-Driven Approach
Journal Title: Urban Mass Transit - Year 2025, Vol 28, Issue 1
Abstract
[Objective] As an important part of urban transportation, the urban rail transit system is closely related to people′s travel and urban development. The continuous acceleration of urbanization and people′s constant pursuit of travel efficiency have raised higher requirements for talent training in the urban rail transit field. Currently, the training model of urban rail transit disciplines postgraduate students has problems such as disconnection with industrial needs, irrelevant topic selection of actual needs, and low degree of integration between industry and academia. These problems not only restrict the comprehensive development of students and the improvement of their professional qualities but will also impact technology innovation and development in the field of urban rail transit. It is necessary to study the innovative training model of urban rail transit disciplines postgraduate students. [Method] Characteristics of the real-problem-driven postgraduate training model are analyzed. Taking the China Urban Rail Transit Science and Technology Innovation and Entrepreneurship Competition as an example, combines the current situation and problems of urban rail transit disciplines postgraduate students training model, an innovative model for postgraduate training driven by real problems is proposed. [Result & Conclusion] After introducing real problem situations, students can deeply participate in actual projects and further cooperate with enterprises, thereby cultivating their practical abilities and innovative spirit. The real-problem-driven urban rail transit disciplines postgraduate training model can stimulate students′ learning interest and practical motivation, train their innovative thinking, and improve their ability to solve practical problems and innovative thinking.
Authors and Affiliations
Shize HUANG, Zhang SUN, Yi ZHANG, Junhua XIAO, Liang ZOU, Dan WU
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