关联规则挖掘在精品课程网站中的应用研究
发布时间：2019-05-23 06:51
【摘要】：由于国家对精品课程建设的大力推广,作为精品课程核心内容之一的精品课程网络教学平台也已经得到普及。然而现在的精品课程网络教学平台普遍存在个性化学习推荐功能较弱、互动性较差等情况,所以用户利用网络教学平台进行学习的体验并不优秀,这也成为了导致网络教学平台利用率不高的因素之一。本文基于关联规则挖掘技术和AJAX技术,以软件工程技术为指导,设计并实现了具有个性化学习内容推荐功能和较强互动功能的精品课程网络教学平台。个性化学习内容推荐功能包含两个核心子模块：一是实现关联规则挖掘子模块,二是利用关联规则实现学习内容推荐子模块。在关联规则挖掘模块中采用了Apriori算法对用户的访问日志进行关联规则挖掘,实现用户访问系统时的学习内容推荐；同时,基于用户访问的内容数据具有层次性的特点,本文也研究了利用ML-SH挖掘算法对同层数据进行关联规则挖掘,从而实现了板块之间的访问推荐效果。在平台实现的基础上,本文对平台的关联规则模块进行了测试,并对测试过程中关联规则模块可能存在的问题进行了分析。同时,为了获知用户对平台推荐的学习内容的满意程度,即系统推荐的效果,本文提出了利用统计用户访问系统推荐的内容数量占其当次访问的内容数量的比值,作为评判用户满意度的方式,并以该方式对系统的推荐效果进行了实验测试,实验结果证明用户对推荐的内容是感到满意的。
[Abstract]:Due to the national promotion of the construction of high-quality courses, as one of the core contents of high-quality courses, the network teaching platform of high-quality courses has also been popularized. However, the current excellent course network teaching platform generally has the situation that the personalized learning recommendation function is weak, the interaction is poor and so on, so the user's experience of using the network teaching platform to carry on the study is not excellent. This has also become one of the factors that lead to the low utilization rate of network teaching platform. Based on association rule mining technology and AJAX technology, this paper designs and implements a network teaching platform for excellent courses with personalized learning content recommendation function and strong interaction function under the guidance of software engineering technology. The personalized learning content recommendation function consists of two core sub-modules: one is to realize the association rule mining sub-module, the other is to use the association rules to realize the learning content recommendation sub-module. In the association rule mining module, Apriori algorithm is used to mine the user's access log, and the learning content recommendation is realized when the user accesses the system. At the same time, based on the hierarchical characteristics of the content data accessed by users, this paper also studies the use of ML-SH mining algorithm to mine association rules for the same layer of data, so as to achieve the effect of access recommendation between plates. On the basis of the implementation of the platform, this paper tests the association rules module of the platform, and analyzes the possible problems of the association rules module in the testing process. At the same time, in order to know the satisfaction of users with the learning content recommended by the platform, that is, the effect of system recommendation, this paper proposes to use the ratio of the number of recommended content to the number of content that users visit the system. As a way to judge user satisfaction, the recommendation effect of the system is tested in this way. The experimental results show that the user is satisfied with the recommended content.
【学位授予单位】：广西大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP311.13;TP393.092
本文编号：2483689
[Abstract]:Due to the national promotion of the construction of high-quality courses, as one of the core contents of high-quality courses, the network teaching platform of high-quality courses has also been popularized. However, the current excellent course network teaching platform generally has the situation that the personalized learning recommendation function is weak, the interaction is poor and so on, so the user's experience of using the network teaching platform to carry on the study is not excellent. This has also become one of the factors that lead to the low utilization rate of network teaching platform. Based on association rule mining technology and AJAX technology, this paper designs and implements a network teaching platform for excellent courses with personalized learning content recommendation function and strong interaction function under the guidance of software engineering technology. The personalized learning content recommendation function consists of two core sub-modules: one is to realize the association rule mining sub-module, the other is to use the association rules to realize the learning content recommendation sub-module. In the association rule mining module, Apriori algorithm is used to mine the user's access log, and the learning content recommendation is realized when the user accesses the system. At the same time, based on the hierarchical characteristics of the content data accessed by users, this paper also studies the use of ML-SH mining algorithm to mine association rules for the same layer of data, so as to achieve the effect of access recommendation between plates. On the basis of the implementation of the platform, this paper tests the association rules module of the platform, and analyzes the possible problems of the association rules module in the testing process. At the same time, in order to know the satisfaction of users with the learning content recommended by the platform, that is, the effect of system recommendation, this paper proposes to use the ratio of the number of recommended content to the number of content that users visit the system. As a way to judge user satisfaction, the recommendation effect of the system is tested in this way. The experimental results show that the user is satisfied with the recommended content.
【学位授予单位】：广西大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP311.13;TP393.092
【参考文献】
相关期刊论文 前1条
1 陈以海;;高校精品课程网站建设探索[J];中国教育信息化;2008年01期
，本文编号：2483689
本文链接：https://www.wllwen.com/guanlilunwen/ydhl/2483689.html
