基于多种机器学习算法的高校公共体育教师教学能力评价模型研究  

Research on Teaching Ability Evaluation Model of University Public Physical Education Teachers based on Multiple Machine Learning Algorithms

作　　者：王翔宇 杨铃春 王晓昀 WANG Xiangyu;YANG Lingchun;WANG Xiaoyun(School of Physical Education,Sichuan Normal University,Chengdu 610101,China;School of Physical Education,Changsha University,Changsha 410000,China;School of Economics and Management,Chengdu Sport University,Chengdu 610051,China)

机构地区：[1]四川师范大学体育学院,成都610101 [2]长沙大学体育学院,长沙410000 [3]成都体育学院经济管理学院,成都610051

出　　处：《成都工业学院学报》2025年第2期88-92,116,共6页Journal of Chengdu Technological University

基　　金：教育部产学合作协同育人项目(220802495041824);四川师范大学人才培养质量与教学改革项目(20230001XJG)。

摘　　要：为进一步提升高校公共体育教师教学能力评价的科学性和准确性,构建了包含“教学态度、教学实施、教学学术、教学发展”4个维度的高校公共体育教师教学能力评价指标体系,并基于多层BP神经网络模型和最小二乘支持向量机(LSSVM)算法构建了高校公共体育教师教学能力评价模型,同时采用所构建模型对样本数据进行了测试和验证。结果表明,基于多层BP神经网络模型和LSSVM算法所构建的高校公共体育教师教学能力评价模型的学生评价准确率为90.70%和93.88%;同行评价准确率为90.17%和93.24%,均高于90.00%,适合对高校公共体育教师的教学能力进行评价。To further enhance the scientificity and accuracy of evaluating the teaching abilities of public physical education teachers in colleges and universities,an evaluation index system was constructed,which includes four dimensions:teaching attitude,teaching implementation,teaching scholarship,and teaching development.Additionally,an evaluation model for public physical education teachers'teaching abilities in colleges and universities was developed based on multi-layer BP neural networks and the least squares support vector machine(LSSVM)algorithm.The constructed model was tested and validated using sample data.The results indicate that the accuracy rates of student evaluation of the model based on multi-layer BP neural networks and LSSVM are 90.70%and 93.88%,respectively,while for peer evaluations,they are 90.17%and 93.24%,both exceeding 90.00%,which proves that the model is suitable for evaluating the teaching abilities of public physical education teachers in colleges and universities.

关 键 词：公共体育教师 高校 教学能力 机器学习 评价模型 

分 类 号：G80[文化科学—运动人体科学] G64[文化科学—体育学]