基于聚类分析的课堂教学质量评价标准研究

Research on the Evaluation Standard of Teaching Quality Based on Cluster Analysis

为了进一步优化课堂教学质量评价指标,并协助培养高校教师教学能力,使用优化的K-means聚类算法和SPSS分析软件分析某高校教师的课堂教学质量评价数据集,发现不同指标之间的隐藏关系、不同聚类结果之间质量评价指标的差异,得到对课堂教学质量起关键作用的指标。实验结果显示,高校教师在教学态度方面具有较小差异,在教学内容、教学方法这两项指标上存在较大差异,从而导致教学效果差异较大。因此,在优化教学质量评价指标时,应增加教学内容和教学方法权值或采用细分二级指标的方法。同时,这些差异与教师职称具有密切关系,助教和讲师在课堂设计和教学方法上需进一步加强,教授在信息化教学手段上需加强。

In order to further optimize the evaluation index of classroom teaching quality and assist university management to cultivate teachers’teaching ability,this paper uses the optimized K-means clustering algorithm and SPSS analysis software to analyze a university teacher’s classroom teaching quality evaluation data set,and finds out the hidden relationship between different indicators and the difference of quality evaluation indicators among different clustering results,so as to get the conclusion that the quality of classroom teaching can be improved by Key role indicators.Through the experimental analysis,the results show that there are small differences in the teaching attitude of university teachers,and there are large differences in the two indicators of teaching content and teaching methods,which leads to great differences in teaching effect.In the optimization of teaching quality evaluation index,we should strengthen the weight of teaching content and teaching methods or subdivide the secondary index.At the same time,these differences are closely related to teachers’professional titles.Teaching assistants and lecturers need to be further strengthened in classroom design and teaching methods,and professors need to strengthen information-based teaching methods.