基于KNN算法的教学质量评价模型建立  

Establishment of teaching quality evaluation model based on KNN algorithm

作　　者：张晓东 张晓晓[2] ZHANG Xiao-dong;ZHANG Xiao-xiao(College of Mechanical&Electrical Engineering,Ningde Normal University,Ningde,Fujian 352100,China;School of Preschool Education,Wuhan City Vocational College,Wuhan,Hubei 430000,China)

机构地区：[1]宁德师范学院机电工程学院,福建宁德352100 [2]武汉城市职业学院学前教育学院,湖北武汉430000

出　　处：《宁德师范学院学报（自然科学版）》2024年第3期324-329,共6页Journal of Ningde Normal University(Natural Science)

基　　金：宁德师范学院教改项目(JG2022037);宁德师范学院师范教育亮色工程专项(2022ZX202)。

摘　　要：针对当前教学质量评价存在主观性较强的不足,基于K-最近邻(K-nearest neighbor,KNN)算法,提出教学质量评价模型.确立教学质量评价体系;以教学督导的评价数据为样本数据,通过交叉验证求解最近邻算法参数K的最佳值,从而建立教学质量评价模型.模型以专家数据为样本,评价精度高,评价结果具有较高的可靠性,能根据相关指标快速产生评价等级,提高了教学质量评价效率,使教学质量评价更加客观全面.Aiming at the shortcomings of subjectivity in current teaching quality evaluation,this paper proposes a teaching quality evaluation model based on the principle of nearest neighbor algorithm classification.Firstly,the evaluation system of teaching quality is established.Then,taking the evaluation data of teaching supervision as sample data,the best value of parameter K of K-nearest neighbor(KNN)algorithm is solved through cross verification,so as to establish the teaching quality evaluation model.Since the model takes expert data as samples,it has high evaluation accuracy.The evaluation results of the model are highly reliable and it can quickly generate evaluation grades according to relevant indicators,which not only improves the efficiency of teaching quality evaluation,but also makes teaching quality evaluation more objective and comprehensive.

关 键 词：教学质量评价 K-最近邻(KNN)算法 交叉验证 

分 类 号：G642[文化科学—高等教育学]