基于兴趣度关联规则的在线学习行为分析方法

针对如何使用数据挖掘技术分析指导用户改善学习行为的问题,提出了一种基于兴趣度关联规则的学习行为分析方法.首先,采用K-means聚类方法快速归纳出用户的学习状态; 其次,通过含兴趣度的关联规则算法获得学习行为与学习效果之间的强规则; 最后,以edX平台提供的用户学习数据为例对算法进行了验证.结果表明:含兴趣度指标的算法所获得的强规则数目比传统关联规则算法缩减了40.9%,同时该方法能够得出学习行为因素与学习效果之间的具体关系,有利于指导用户改善学习行为.

Research on online learning behavior analysis method based on the association rule of degree of interest

Aiming at the problem of how to use data mining technology to analyze and guide users to improve their learning behavior, this paper proposes a learning behavior analysis method based on association rules of degree of interest. Firstly, the K-means clustering method is adopted to quickly summarize the learning state of users. Secondly, strong rules between learning behavior and learning effect are obtained by association rule algorithm with degree of interest. Taking the user learning data provided by edX platform as an example, the verification results show that the number of strong rules obtained by the algorithm with degree of interest is reduced by 40.9% compared with the traditional association rule algorithm. At the same time, the method can obtain the specific relationship between learning behavior factors and learning effects, which is helpful to guide users to improve learning behavior.