In this study,the questionnaire“Influencing Factors of Classroom Quality in Higher Vocational Education”was self-compiled.By using the questionnaire survey method,the research conclusion was drawn that the main fact...In this study,the questionnaire“Influencing Factors of Classroom Quality in Higher Vocational Education”was self-compiled.By using the questionnaire survey method,the research conclusion was drawn that the main factors affecting the classroom teaching quality in higher vocational education were teaching and student factors,teacher factors,and classroom factors based on 2,683 samples.According to the research results,the measures to improve the quality of classroom teaching are put forward,in order to provide guidance for improving the quality of teaching in higher vocational colleges.展开更多
Purpose-Deep learning-based classroom behavior analysis provides new avenues for monitoring teaching quality in higher education.However,it faces challenges such as low detection accuracy,difficulty in recognizing sma...Purpose-Deep learning-based classroom behavior analysis provides new avenues for monitoring teaching quality in higher education.However,it faces challenges such as low detection accuracy,difficulty in recognizing small objects and handling occlusions as well as the difficulty in balancing real-time performance with accuracy.Design/methodology/approach-This paper proposes an improved YOLOv11 method for classroom state recognition,achieving precise classification and behavior detection through the integration of AFGCAttention,SPDConv and RCSOSA modules.AFGCAttention optimizes feature weight allocation through an adaptive fine-grained channel attention mechanism,SPDConv enhances the processing capabilities for small objects and low-resolution images by converting spatial information into depth information and RCSOSA reduces channel redundancy while improving spatial object attention.Findings-Experiments demonstrate that the YOLO-ASR model excels in precision,recall and mAP50.Compared to other You Only Look Once versions,it shows significantly improved detection accuracy and robustness in complex classroom environments,achieving an mAP50 of 93.8%and an mAP50-95 of 73.1%.Time-series analysis reveals dynamic changes in student behavior across teaching phases,including attention fluctuations,mobile phone use and signs of fatigue.Research limitations/implications-By analyzing student behavior across different classroom phases,patterns in mobile phone use and signs of fatigue were identified.These insights help teachers adjust their strategies,highlighting the method’s significance in monitoring teaching quality.Originality/value-This study optimizes the YOLOv11 model for classroom behavior detection by integrating effective modules to enhance performance.It offers a novel approach for quantitatively assessing teaching effectiveness,providing data support for educational reform and advancing intelligent classroom management and innovative teaching models.展开更多
文摘In this study,the questionnaire“Influencing Factors of Classroom Quality in Higher Vocational Education”was self-compiled.By using the questionnaire survey method,the research conclusion was drawn that the main factors affecting the classroom teaching quality in higher vocational education were teaching and student factors,teacher factors,and classroom factors based on 2,683 samples.According to the research results,the measures to improve the quality of classroom teaching are put forward,in order to provide guidance for improving the quality of teaching in higher vocational colleges.
基金supported by the Natural Science Foundation of Fujian Province(No.2022J01644)the Higher Education Scientific Research Planning Project(No.ZD202309)the Fujian Province Young and Middle-Aged Teachers Education Research Project(No.JAT210651).
文摘Purpose-Deep learning-based classroom behavior analysis provides new avenues for monitoring teaching quality in higher education.However,it faces challenges such as low detection accuracy,difficulty in recognizing small objects and handling occlusions as well as the difficulty in balancing real-time performance with accuracy.Design/methodology/approach-This paper proposes an improved YOLOv11 method for classroom state recognition,achieving precise classification and behavior detection through the integration of AFGCAttention,SPDConv and RCSOSA modules.AFGCAttention optimizes feature weight allocation through an adaptive fine-grained channel attention mechanism,SPDConv enhances the processing capabilities for small objects and low-resolution images by converting spatial information into depth information and RCSOSA reduces channel redundancy while improving spatial object attention.Findings-Experiments demonstrate that the YOLO-ASR model excels in precision,recall and mAP50.Compared to other You Only Look Once versions,it shows significantly improved detection accuracy and robustness in complex classroom environments,achieving an mAP50 of 93.8%and an mAP50-95 of 73.1%.Time-series analysis reveals dynamic changes in student behavior across teaching phases,including attention fluctuations,mobile phone use and signs of fatigue.Research limitations/implications-By analyzing student behavior across different classroom phases,patterns in mobile phone use and signs of fatigue were identified.These insights help teachers adjust their strategies,highlighting the method’s significance in monitoring teaching quality.Originality/value-This study optimizes the YOLOv11 model for classroom behavior detection by integrating effective modules to enhance performance.It offers a novel approach for quantitatively assessing teaching effectiveness,providing data support for educational reform and advancing intelligent classroom management and innovative teaching models.
