K-均值算法支持的优质网络学习资源筛选方法研究

Using the K-means Algorithm-Based Method to Screen High-quality Online Resources

网络学习资源建设是教育信息化的重要组成部分。面对海量的网络学习资源,如何筛选出高质量的学习资源就变得尤为重要。本文以自主研发的精品资源共享课程公共服务平台为支撑,提出一种优质网络学习资源的筛选方法。该方法首先使用K-均值聚类算法对大量网络学习资源进行自动分类,然后通过支配关系对聚类后的资源进行评价,通过引入优胜劣汰机制筛选优质网络学习资源,以消除学习者在资源选择时的盲目性,有效提高网络学习的效率与效果。

The development of online learning resources is an important part of the use of IT in education.With unlimited availability of learning resources in the Internet, how to select high-quality learning resources becomes a key factor affecting the efficiency and effectiveness of online learning. This paper proposes a method to generate high-quality online resources, based on an in-house public platform for sharing courses. The method uses K-means algorithm to automatically classify a large number of online resources. Then it evaluates resources which have been clustered by domination relationship. After that,it generates high-quality learning resources via the survival of the fittest mechanism, hence avoiding random selection and significantly improving the efficiency and effectiveness of online learning.