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基于GSEQ 技术的初中乒乓球课堂教学行为模式挖掘与优化

Mining and Optimization of Teaching Behavior Patterns in Middle School Table Tennis Classes Based on GSEQ Technology

韩海娟

Education Study / 2026,8(6): 617-622 / 2026-06-15 12 4

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• Information:

  哈尔滨体育学院，哈尔滨

• Keywords:
  GSEQ technology; Lagged sequence analysis; Middle school table tennis; Classroom teaching behavior; Behavioral patterns
  GSEQ技术; 滞后序列分析; 初中乒乓球; 课堂教学行为; 行为模式
• Abstract: Middle school table tennis classroom teaching behaviors exhibit characteristics such as skill standardization, intensive practice frequency, and real-time interactivity. Traditional static frequency analysis struggles to capture temporal transition patterns between behaviors. This study employs lagged sequence analysis and GSEQ technology to identify dynamic patterns in middle school table tennis classroom teaching behaviors. Using two “table tennis forehand stroke” lessons with different instructional designs as samples, behavioral coding was performed using BORIS software; 5-second time slices and sequence reconstruction were conducted via Python scripts; and lagged sequence analysis was performed with GSEQ software (lag=1, Z-score>1.96). The results identified 19 significant behavioral sequences across both lessons, revealing two distinct patterns: Lesson A follows a “teacher-led – behavioral inertia” model, while Lesson B exhibits a “student-initiated – teacher-responsive” pattern. Common issues emerged in both lessons: teacher explanations accounted for over 30%, student interaction was insufficient (20%), technical assistance utilization was below 3.5%, and problems included self-repetitive teacher explanations, students’ behaviors failing to trigger teacher feedback, and isolated use of technical aids. The study demonstrates that the integrated BORIS-Python-GSEQ framework effectively analyzes dynamic temporal structures in physical education classroom behaviors. Based on these findings, this paper proposes a three-stage improvement mechanism (“analysis-diagnosis-optimization”), a differentiated teacher support system, and pathways for deep integration of technology and teaching practices, providing data-driven insights and practical recommendations for enhancing middle school table tennis classroom instruction quality. 初中乒乓球课堂教学行为具有技能规范性、练习密集性与互动即时性等特征，传统静态频次分析难以捕捉行为之间的时序转换规律。本研究运用滞后序列分析与GSEQ技术，挖掘初中乒乓球课堂教学行为的动态模式。以两节“乒乓球正手攻球”同课异构课程为样本，通过BORIS软件进行行为编码，利用Python脚本完成5秒时间切片与序列重构，采用GSEQ软件进行滞后序列分析（Lag=1，Z-score>1.96）。结果显示，两课堂共识别出19条显著行为序列，呈现两种典型模式：课堂A为“教师主导—行为惯性”型，课堂B为“学生触发—教师响应”型。两节课堂存在共性问题：教师讲解占比均超30%，学生互动不足占比20%，技术辅助占比低于3.5%，且存在教师讲解自身循环、学生行为未能主动触发教师反馈、技术辅助形成“孤岛”等问题。研究表明，“BORISPython-GSEQ”集成分析框架能有效解析体育课堂教学行为的动态时序结构。据此，本文提出“分析—诊断—优化”三阶段改进机制、差异化教师支持体系及技术与教学深度融合路径，为提升初中乒乓球课堂教学质量提供数据支撑与实践参考。
• DOI: 10.35534/es.0806109 (registering DOI)
• Cite: 韩海娟．基于GSEQ技术的初中乒乓球课堂教学行为模式挖掘与优化［J］．教育研讨，2026，8（6）：617-622．