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融合OBE理念和思政元素的混合式“微观交通仿真”课程优化研究

Study on the Optimization of Hybrid "Micro-traffic Simulation" Course Integrating Outcome-based Education and Ideological and Political Elements

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【作者】 陈倩; 游尔康; 姚佼; 赵靖; 马晓旦;

【Author】 CHEN Qian;YOU Erkang;YAO Jiao;ZHAO Jing;MA Xiaodan;School of Management, University of Shanghai for Science and Technology;

【机构】 上海理工大学管理学院;

【摘要】 通过成果导向教育(OBE)理念与课程思政相结合,创新交通工程专业课思政教学设计,深度挖掘“微观交通仿真”课程中蕴含的思政元素,以学生为中心,教师发挥主导作用,实现专业课程价值引领、知识传授、能力培养“三位一体”的教育教学目标。文章以微观交通仿真软件VISSIM为依托,首先,有机融合课程主要内容与思政元素;其次,教学方法上应用案例教学和项目实践,教学形式上采用线上线下相结合的模式;最后,对教学模式进行量化评估和质性评估。为新时代我国交通工程专业教育教学过程中融合课程思政提供理论借鉴和经验参考,以更好推进课程思政在高校教学中的应用。

【Abstract】 By combining the concept of outcome-based education(OBE) with curriculum ideological and political education, we innovate the design of ideological and political education for transportation engineering professional courses, deeply explore the ideological and political elements contained in the "micro transportation simulation" course, take students as the center, and teachers play a leading role to achieve the educational and teaching goal of "trinity" of professional curriculum value guidance,knowledge transmission, and ability cultivation. Based on the micro traffic simulation software VISSIM, this paper firstly organically integrates the main content of the course with ideological and political elements; secondly, in terms of teaching methods, case teaching and project practice should be applied, and the teaching form should adopt a combination of online and offline modes;finally, conduct quantitative and qualitative evaluations of the teaching mode. To provide theoretical and empirical references for the integration of curriculum ideology and politics in the education and teaching process of transportation engineering majors in China in the new era, in order to better promote the application of curriculum ideology and politics in university teaching.

【基金】 2023年上海高校青年教师培养资助计划项目;2023年上海市“教师专业发展工程”高校教师培养计划项目;2023年上海理工大学本科教学研究与改革项目;上海市青年科技启明星计划项目“基于Transformer的城市区域路网边界鲁棒控制策略研究”(23YF1429700);国家自然科学基金项目“基于多模态深度强化学习的复杂环境下智能网联车协同控制”(62303325)
  • 【文献出处】 物流科技 ,Logistics Sci-Tech , 编辑部邮箱 ,2023年23期
  • 【分类号】G642;U491
  • 【下载频次】122
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