大学化学 >> 2025, Vol. 40 >> Issue (5): 283-290.doi: 10.12461/PKU.DXHX202407070

教学研究与改革 上一篇    下一篇

一流学科建设背景下“材料化学成分分析”课程教学改革

庄媛1, 厉文辉2, 李杰1   

  1. 1 北京科技大学自然科学基础实验中心, 北京 100083;
    2 北京科技大学化学与生物工程学院, 北京 100083
  • 收稿日期:2024-07-22 修回日期:2024-09-18 发布日期:2025-05-21
  • 通讯作者: 庄媛 E-mail:zhuangyuan@ustb.edu.cn
  • 基金资助:
    北京科技大学2020年度本科教育教学改革与研究面上项目(JG2020M55)；北京科技大学2021年度校级规划教材建设项目(JC2021YB042)；北京科技大学2023年度课程思政教材建设项目(KCSZJC2023YB008)

Curriculum Reform of “Chemical Composition Analysis of Materials” under Background of First-Class Discipline Construction

Yuan Zhuang1, Wenhui Li2, Jie Li1   

  1. 1 Basic Experimental Center for Natural Science, University of Science and Technology Beijing, Beijing 100083, China;
    2 School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China
  • Received:2024-07-22 Revised:2024-09-18 Published:2025-05-21
  • Contact: Yuan Zhuang E-mail:zhuangyuan@ustb.edu.cn

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摘要： 基于一流学科建设对人才培养的要求，开展“材料化学成分分析”课程建设。采用“线上+线下”和“理论+实验”结合的方式，将大型仪器引入实验教学，构建“课堂学习-线上自学-小组设计-开展实验”的教学体系和多元化评价方法，强调学生的主体地位，并开展课程思政建设，培养学生专业素养、科研思维、创新与实践能力。

关键词: 一流学科, 课程思政, 混合式教学, 大型仪器, 设计型实验

Abstract: This study implements teaching reforms for the “Chemical Composition Analysis of Materials” course in alignment with the talent cultivation objectives of first-class discipline construction. We developed an integrated pedagogical framework combining online-offline learning with theoretical-practical instruction, incorporating sophisticated instrumentation into experimental teaching. The established “classroom learning - online self-study-group design - experimental implementation” teaching system is supported by a multidimensional evaluation approach. This reform emphasizes student-centered learning while integrating ideological and political elements, effectively enhancing students' professional competencies, scientific thinking, and innovation capabilities through design-oriented experiments.

Key words: First-class discipline, Ideology and political education, Blended learning, Large-scale instrument, Design-oriented experiment