中华医学教育杂志

中华医学教育杂志 ›› 2025, Vol. 45 ›› Issue (2): 130-134.DOI: 10.3760/cma.j.cn115259-20230925-00295

• 教育技术 • 上一篇    下一篇

中医药高等院校教师线上教学行为特征及分类研究

张杰1, 车轶文2, 谭曦3, 袁娜4, 焦楠4   

  1. 1北京中医药大学党委宣传部思政科,北京 100029;
    2北京中医药大学第一临床医学院教育处本科办,北京 100700;
    3北京中医药大学深圳医院,深圳 518100;
    4北京中医药大学 全国中医药教育发展中心,北京 100029
  • 收稿日期:2023-09-25 发布日期:2025-01-25
  • 通讯作者: 焦楠, Email: flynny@163.com
  • 基金资助:
    全国中医药高等教育“十四五”规划2021年度教育科研课题(YB-20-02)

The study of the characteristics and classification of online teaching behaviors of teachers in higher education institutions of traditional Chinese medicine

Zhang Jie1, Che Yiwen2, Tan Xi3, Yuan Na4, Jiao Nan4   

  1. 1Department of Ideological and Political work, Publicity Department, Beijing University of Chinese Medicine, Beijing 100029, China;
    2Undergraduate Affairs Office, Department of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China;
    3Shenzhen Hospital, Beijing University of Chinese Medicine, Shenzhen 518100, China;
    4Beijing University of Chinese Medicine, National Center for Chinese Medicine Education Development, Beijing 100029, China
  • Received:2023-09-25 Published:2025-01-25
  • Contact: Jiao Nan, Email: flynny@163.com
  • Supported by:
    The 2021 Annual Education and Research Projects of the National Traditional Chinese Medicine Higher Education ″14th Five-Year Plan″(YB-20-02)

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摘要： 目的 探索中医药高等院校教师的线上教学行为特征并进行分类研究,为提高线上教学质量提供参考。 方法 2021年2月至4月,向全国24所独立设置的中医药高等院校参加线上教学的教师线上发放调查问卷5 621份,回收有效问卷5 225份,有效问卷回收率93.0%。采用描述性统计方法得到4个行为特征的平均评分,用相关性分析法探索教学行为特征之间的关系,用两步聚类分析法对该教师群体进行分类,对比不同类别教师的线上教学行为特点。 结果 教师线上教学行为特征评分为:学业辅导(4.32±0.59)分、课程设计(4.32±0.66)分、教学互动(3.93±0.66)分、自我评价(3.88±0.66)分。线上教学行为特征之间存在正向相关,基于线上教学行为将教师分为高能远虑型(2 153人)、期待回归型(2 278人)和盲目适从型(794人)。 结论 线上教学行为中,课程设计和学业辅导的行为特征更为突出;学业辅导与课程设计、教学互动的关系相对密切;高能远虑型教师具有科学的课程设计、积极的教学互动、丰富的学业辅助和理性的自我评价;期待回归型教师表现为一般化的课程设计、形式化的教学互动、任务式的学业辅助和中性的自我评价;而盲目适从型教师表现为简单的课程设计、消极的教学互动、单一的学业辅助和较低的自我评价。

关键词: 中医药, 线上教学, 行为研究, 聚类分析

Abstract: Objective To explore the characteristics of online teaching behaviors among teachers at Traditional Chinese Medicine colleges and conduct a classification study, to inform the continual improvement of online teaching. Methods From February to April 2021, a survey was distributed online to 5 621 teachers involved in online teaching tasks from 24 independent higher education institutions specializing in traditional Chinese medicine across the country. A total of 5 225 responses were collected, resulting in a response rate of 93.0%. Descriptive statistical methods were used to obtain average scores for four behavioral characteristics. Correlation analysis was applied to explore the relationships between teaching behavior characteristics, and a two-step cluster analysis was employed to classify the teachers, comparing the online teaching behavior characteristics across different categories. Results The average scores for teacher online teaching behavior characteristics were: Academic Guidance (4.32±0.59), Course Design (4.32±0.66), Teaching Interaction (3.93±0.66), and Self-Evaluation (3.88±0.66). There was a positive correlation between the characteristics of online teaching behaviors. Based on these behaviors, teachers were classified as high-energy, long-term thinkers (2 153 individuals), expectation of returnees (2 278 individuals), and blindly compliant (794 individuals). Conclusions In online teaching behaviors, the characteristics of course design and academic guidance are more prominent; academic guidance shows a relatively close relationship with course design and teaching interaction. High-energy, long-term thinkers demonstrate scientific course design, active teaching interaction, rich academic support, and rational self-evaluation; expectation of returnees exhibit generalized course design, formalized teaching interaction, task-based academic support, and neutral self-evaluation; while blindly compliant teachers show simplistic course design, passive teaching interaction, singular academic support, and low self-evaluation scores.

Key words: Traditional Chinese Medicine, Online teaching, Behavioral research, Cluster analysis

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