面向实践能力提升的交通大数据理论与方法类课程教学改革研究

Teaching Reform Research on Theoretical and Methodological Courses of Transportation Big Data for Enhancing Practical Capabilities

摘要: 为应对智慧交通发展对专业人才能力提出的新要求，本文围绕大数据理论与方法类课程，系统开展了以案例重构为核心的实践教学改革。在分析课程现状与教材特点的基础上，提出在理论教学基础上，重点围绕支持向量机、决策树、聚类分析、人工神经网络等核心算法，构建了遵循真实性、问题导向、流程完整与能力阶梯原则的教学案例体系。通过典型业务场景实现“算法–场景–能力”的深度融合，形成了从问题导入、模型构建到业务应用的完整教学闭环。同时，结合线上/线下混合式教学模式与多元化考核体系，构建了一套系统可行的教学改革方案。实践表明，该方案在有效提升学生工程实践与创新能力的同时，其案例设计思路与实施路径具有较强的可推广性，可为同类工科专业课程应对新工科建设需求提供可借鉴的改革范式。

Abstract: In order to cope with the new requirements put forward by the development of smart transportation on the capabilities of professional talents, this paper systematically carries out practical teaching reforms with case reconstruction as the core around the courses of “Big Data Theory and Methods”. On the basis of analyzing the current situation of the course and the characteristics of the teaching materials, it is proposed to build a teaching case system that follows authenticity, problem orientation, process integrity and ability ladder principles based on theoretical teaching, focusing on core algorithms such as support vector machines, decision trees, clustering analysis, and artificial neural networks. The deep integration of “algorithm-scenario-capabilities” is achieved through typical business scenarios, forming a complete closed loop of teaching from problem introduction, model construction to business applications. At the same time, combining the online/offline hybrid teaching model and the diversified assessment system, a systematic and feasible teaching reform plan has been built. Practice has shown that while this plan effectively improves students’ engineering practice and innovation capabilities, its case design ideas and implementation paths are highly generalizable, and can provide a reform paradigm that can be used for similar engineering professional courses to meet the needs of new engineering construction.