高等药学教育研究 ›› 2026, Vol. 44 ›› Issue (2): 49-54.

• 学科与课程建设 • 上一篇    下一篇

以赛促学、知行合一:破解高等数学学习困境的教学改革框架设计

  

  1. 沈阳药科大学 医疗器械学院,辽宁 沈阳 110016
  • 收稿日期:2025-12-15 出版日期:2026-06-25 发布日期:2026-06-18
  • 通讯作者: 翟明明

Competition-driven learning, integration of knowledge and practice: A teaching reform framework for addressing challenges in advanced mathematics education

  1. School of Medical Devices, Shenyang Pharmaceutical University, Shenyang 110016, China
  • Received:2025-12-15 Online:2026-06-25 Published:2026-06-18

摘要:

针对高等数学课程教学中长期存在的“学用脱节”问题,笔者构建了一个以“以赛促学、以学促赛”为核心的闭环教学改革框架。该框架以数学建模竞赛的能力要求为逆向牵引,通过重构以问题为导向的专题化教学内容,创新案例与项目式学习相融合的教学方法,建立过程性、应用性与激励性相结合的多元评价体系,拓展课程思政与创新实践相融合的育人路径,对课程体系进行系统性重构,旨在构建理论教学与实践应用互哺的教学新生态。并进一步系统阐述了该框架的内在逻辑、实施路径与预期成效,以期为推动高等数学教学改革与人才培养模式转型提供系统性参考。

关键词:

Abstract:

To address the long-standing problem of "disconnection between learning and application" in advanced mathematics teaching, the authors construct a closed-loop teaching reform framework centered on "competition-driven learning and learning-facilitated competition". Taking the competency requirements of mathematical modeling competitions as a reverse driver, the framework reconstructs problem-oriented thematic teaching content, innovates teaching methods that integrate case studies and project-based learning, establishes a diversified evaluation system combining process-based, application-based, and incentive-based assessment, and expands educational pathways integrating curriculum ideological and political education with innovation practice, systematically restructuring the curriculum system. The aim is to build a new teaching ecosystem where theoretical instruction and practical application mutually nurture each other. The authors further systematically elaborate on the internal logic, implementation pathways, and expected outcomes of this framework, providing systematic reference for promoting teaching reform in higher mathematics and transforming talent cultivation models.

Key words: advanced mathematics, teaching reform, competition-driven learning, mathematical modeling, framework design

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