摘要
在传统体育教学质量评价基础上引入实时生理监测过程,评估心率(HR)、动脉血氧饱和度(SaO_2)、最大摄氧量(VO2_max)等因素对大学体育教学质量产生的干预作用及影响程度。结果表明,实施生理监测干预教学分组相对常规教学分组教学质量评价结果表现出显著差异;增加生理指标可有效提升机器学习对于教学效果的判正灵敏度:引入心率指标,教学总体判定正确率上升11.9%,引入心率及血氧指标,判定正确率上升至85.7%,增加心率及血氧饱和度交互指标,判正率提升至92.9%,引入最大吸氧量指标未影响判正效率。得出结论:生理指标监控干预过程对大学体育教学质量具有显著影响;评价各类生理指标的干预程度,共线性检验具有必要性。
The introduction of real-time monitoring of physiological processes in the traditional sports teaching quality evaluation based on the assessment of heart rate(HR),arterial oxygen saturation intervention(SaO_2),maximal oxygen uptake(VO_2max)and other factors on the quality of teaching and influence generated University degree.The results showed that the implementation of physiological monitoring intervention teaching packet relatively conventional teaching group teaching quality evaluation of the results showed a significant difference.Increased physiological indexes can effectively enhance the machine learning for teaching effectiveness judgments positive sensitivity:the introduction of HR indicators,the overall teaching correct judgment rate rose 11.9%,the introduction of heart rate and blood oxygen index,determined the correct rate rose to 85.7%,Increased heart rate and blood oxygen saturation interaction indicators sentenced positive rate increased to 92.9%,the introduction of maximum oxygen uptake indicators did not affect the sentence positive efficiency.Conclusion:physiological parameters monitored during the intervention has a significant impact on the quality of teaching college sports.All kinds of human physiological indicators of statistical results collinearity test is necessary.
出处
《浙江体育科学》
2016年第6期74-79,共6页
Zhejiang Sport Science
基金
南京工程学院高等教育研究课题(2015ZC10)基于生理指标的运动训练效果辅助支持研究
关键词
生理指标
教学质量评价
机器学习
physiological indicators
teaching quality assessment
machine learning
