通讯作者: 李姣, li.jiao@imicams.ac.cn

DOI：10.12201/bmr.202509.00006

Exploring an Industry-Education-Research Collaborative Teaching Model for the Course “R Programming and Fundamentals of Machine Learning” under the New Medical Education Initiative

• 摘要：目的/意义 探索新医科建设背景下,依托医学数据挖掘类课程构建产教研协同教学机制,推动复合型医学创新人才的系统化培养。 方法/过程 以医学专业本科生为对象,围绕《R语言程序设计与机器学习基础》课程设置“会学—会用—会创造”三位一体的课程目标,构建实践驱动、双师联动、技术赋能、多元评价的协同教学路径。通过企业教师深度参与、AI教学助手辅助教学、项目式学习驱动等方式,提升医学生的跨学科综合能力与实践创新能力。结果/结论 最新一届授课学生的数据显示,能够独立完成模型构建的学生比例从66.7%提升至85.0%,代码正确率由71.6%提升至91.7%,建模平均耗时由58分钟缩短至43分钟。问卷调查显示,91.7%的学生对课程表示“非常满意”,88.3%的学生认可双师型教学,93.3%的学生认为实践环节促进了理论知识的理解与应用。本研究构建的产教研协同教学模式有效提升了医学生在医学数据挖掘与智能技术领域的综合能力,具备良好的可复制性和推广价值,可为新医科背景下医学教育改革提供实践参考。

  关键词： 产教研融合; 新医科; 医学数据挖掘; 复合型人才培养

   

  Abstract: Purpose/Significance Under the background of New Medical, medical universities are introducing medical data mining courses aligned with industry demands to cultivate compound medical talents. Method/Process This study targets undergraduate medical students and defines a triadic curriculum goal of “learning to learn, learning to apply, and learning to create” within the “R Programming and Fundamentals of Machine Learning” course. We developed a collaborative teaching pathway characterized by strong practice orientation, dual-teacher engagement, technological empowerment, and multi-dimensional assessment. By integrating deep involvement of industry mentors, AI-driven teaching assistants, and project-based learning, we aimed to enhance students’ interdisciplinary competencies and practical innovation skills. Result/Conclusion In the most recent cohort, 85.0 % of students in the experimental group independently completed model construction—significantly higher than 66.7 % in the control group—while code accuracy rose from 71.6 % to 91.7 % and average modeling time fell from 58 to 43 minutes. Survey data indicate that 91.7 % of participants were “very satisfied” with the course, 88.3 % endorsed the dual-teacher model, and 93.3 % agreed that practice activities facilitated their theoretical understanding and application. Our industry-education-research collaborative model demonstrably improves medical students’ capabilities in data mining and intelligent technologies, offers strong replicability and scalability, and provides practical guidance for curriculum reform under the New Medical Science paradigm.

  Key words: Industry-Education-Research Collaborative; New Medical Education Initiative; Medical Data Mining; Interdisciplinary Talent Cultivation

  提交时间：2025-09-01

  版权声明：作者本人独立拥有该论文的版权，预印本系统仅拥有论文的永久保存权利。任何人未经允许不得重复使用。
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• 序号	提交日期	编号	操作
  1	2025-06-30	bmr.202509.00006V1	下载

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