创新创业理论研究与实践 ›› 2025, Vol. 8 ›› Issue (14): 166-168.

• 创新方法 • 上一篇    下一篇

人工智能技术在课堂教学行为分析中的应用研究

张阳   

  1. 吉林工业职业技术学院,吉林省吉林市 132013
  • 出版日期:2025-07-25 发布日期:2025-08-25
  • 作者简介:张阳(1986—),男,满族,吉林省吉林市人,硕士研究生,讲师,研究方向:人工智能、大数据分析。
  • 基金资助:
    吉林省高等教育学会2024年度高教科研课题“人工智能技术在课堂教学行为分析中的应用研究——以Python编程课堂为例”(JGJX24D1133)

Research on the Application of Artificial Intelligence Technology in Classroom Teaching Behavior Analysis

ZHANG Yang   

  1. Jilin Vocational College of Industry and Technology, Jilin City, Jilin Province, 132013, China
  • Online:2025-07-25 Published:2025-08-25

摘要: 随着智能教育的发展,课堂教学行为的分析已不再局限于传统的观察和反馈方式,人工智能技术,特别是语音识别和面部表情分析,提供了更加精准和全面的评价工具。该文通过结合识别言语数据和面部分析数据,探索如何提高课堂互动的准确性与有效性,并为教师优化教学策略提供支持。研究方法主要包括数据采集技术、深度学习模型及多模态数据融合分析。研究表明:综合分析言语和面部数据能够更全面地评估学生的学习状态、课堂氛围及教师的教学表现,为教育实践提供实时反馈。人工智能技术能够显著提高教学行为分析的精度,推动课堂管理和教学方法创新,具有广泛的应用前景。

关键词: 人工智能, 课堂教学行为, 行为分析, 互动课堂, 数据分析, 大数据

Abstract: With the development of intelligent education, the analysis of classroom teaching behavior is no longer limited to traditional observation and feedback methods. Artificial intelligence technology, especially speech recognition and facial expression analysis, provides more accurate and comprehensive evaluation tools. This article explores how to improve the accuracy and effectiveness of classroom interaction by combining recognition speech data and facial analysis data, and provides support for teachers to optimize teaching strategies. The research methods mainly include data acquisition techniques, deep learning models, and multimodal data fusion analysis. Research has shown that comprehensive analysis of speech and facial data can more comprehensively evaluate students' learning status, classroom atmosphere, and teachers'teaching performance, providing real-time feedback for educational practice. Artificial intelligence technology can significantly improve the accuracy of teaching behavior analysis, promote innovation in classroom management and teaching methods, and has broad application prospects.

Key words: Artificial intelligence, Classroom teaching behavior, Behavior analysis, Interactive classroom, Data analysis, Big data

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