摘要
在分类教育资源时,由于缺乏对资源关联关系的分析,导致分类结果的可靠性较低,为此提出基于密度聚类算法的大学英语教学资源分类方法。首先,充分考虑相邻网格之间资源之间的关联性,为每个资源分区构建加权网格;其次,对应的权重以资源关联性为基础进行设置,计算得到网格单元的密度参数后,采用COMCORE-MR算法判断Key-value参数值范围;最后,当Key-value参数值在网格单元给定的密度阈值参量范围内时,划分对应的教育资源与中心目标网格对象为同类资源。测试结果表明,设计方法的分类结果准确率稳定在79.40%以上,与对照组相比有明显优势。
When classifying educational resources,the reliability of the classification results is low due to the lack of analysis on the relationship between resources.Therefore,a study on the classification method of college English teaching resources based on density clustering algorithm is proposed.Firstly,considering the relationship between resources between adjacent grids,a weighted grid is built for each resource partition.Secondly,the corresponding weight is set on the basis of resource relevance.After the density parameters of grid cells are calculated,the COMCORE-MR algorithm is used to judge the range of key value parameters.Finally,when the Key value parameter value is within the range of the density threshold parameter given by the grid cell,the corresponding educational resources and the central target grid object are divided into similar resources.The test results show that the classification accuracy of the design method is stable at 79.40%,which has obvious advantages compared with the control group.
作者
高雨菲
GAO Yufei(Shaanxi Xueqian Normal University,Xi'an Shaanxi 710061,China)
出处
《信息与电脑》
2022年第22期67-69,共3页
Information & Computer
关键词
密度聚类算法
大学英语
教学资源
分类方法
density clustering algorithm
college English
teaching resources
classification method
作者简介
高雨菲(1987-),女,北京人,硕士研究生,讲师。研究方向:大学英语教学。
