基于K-means算法的数字化教学资源的学生行为数据分析——以“护理管理学”课程为例

Student behavior data analysis of digital teaching resources based on K-means algorithm: Taking Nursing Management as an example

为进一步加强数字化教学资源在课程教学中的应用,提高数字化教学资源质量,实现对数字化教学资源质量持续改进的目的,以“护理管理学”课程为例,采集497名学生一个学期36类数字化教学资源行为数据,经过归一化处理、基于K-means算法完成行为数据的聚类。根据聚类结果,对各类数字化教学资源进行分析并提出针对性的改进建议。该方法可以解决数据量大、无法实现准确分析和避免手动分析结果偏差较大的问题,同时利用资源聚类结果,分析数字化教学资源的质量,为资源质量持续提升奠定基础。

In order to further strengthen the application and improve the quality of digital teaching resources in the teaching, realize the purpose of improving the quality of digital teaching resources continuously, by taking Nursing Management as an example, the paper collected 497 students’ behavioral data of 36 kinds of digital teaching resources in one semester, and made normalization and clustering of the behavioral data based on K-means algorithm. According to the results of clustering, various kinds of digital teaching resources were analyzed and corresponding improvement suggestions were proposed. This method can be used to solve many problems, including problems brought by large amount of data, being unable to accurately analyze data and large errors made by manual analysis. Meanwhile, it also analyzes the quality of digital teaching resources by using the results of clustering and gives suggestions for the continuous improvement of resource quality.