[1]张国荣.Moodle平台数据挖掘方法设计与实现[J].计算机技术与发展,2014,24(05):231-234.
　ZHANG Guo-rong.Design and Realization of Data Mining Method in Moodle[J].,2014,24(05):231-234.
点击复制

Moodle平台数据挖掘方法设计与实现()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年05期
页码:
231-234
栏目:
应用开发研究
出版日期:
2014-05-31

文章信息/Info

Title:
Design and Realization of Data Mining Method in Moodle
文章编号:
1673-629X（2014）05-0231-04
作者:
张国荣
广州美术学院 艺术与人文学院
Author(s):
ZHANG Guo-rong
关键词:
教育数据挖掘; 聚类; 日志挖掘; Moodle
Keywords:
educational data mining; clustering; log mining; Moodle
分类号:
TP301
文献标志码:
A
摘要:
教育数据挖掘是一个新兴的研究方向。如何把存储在教育软件系统中的数据转变为有意义的信息，并为教育决策、优化教学过程服务，已成为大多数教育工作者所关注的内容。文中总结了当前教育数据挖掘的研究现状，介绍了一种基于Excel的简单数据挖掘方法。该方法利用模糊C均值聚类算法，对Moodle平台积累的日志数据进行分析，找出有相似学习行为的学生，为学习社区的小组划分和研究学习模式服务。实验表明，该方法能够更准确地对学生进行分类，而且操作更为简单、方便。
Abstract:
Educational Data Mining ( EDM) is an emerging research direction. How to use the massive educational data and transfer the data into useful information and knowledge in order to provide the service for educational decision and teaching optimization processing has become an emerging and concerned research domain. It reviews the status on educational data mining,and proposes a simple data mining method based on Excel,which analyzes log data using fuzzy c-means clustering algorithm to identify students who have similar learning behavior and evolve hidden patterns in the Moodle. The experiment demonstrates that the method can more accurately classify the students,and the operation is more simple and convenient.

相似文献/References:

[1]蒋璐璐 王适 王宝成 李慧敏 李鑫慧.一种改进的标记分水岭遥感图像分割方法[J].计算机技术与发展,2010,(01):36.
　JIANG Lu-lu,WANG Shi,WANG Bao-cheng,et al.Segmentation of Remote Sensing Image Based on an Improved Labeling Watershed Algorithm[J].,2010,(05):36.
[2]张甜 罗眉 孟晓红 赵宗涛.一种基于状态特征的航天发射故障诊断技术[J].计算机技术与发展,2010,(01):93.
　ZHANG Tian,LUO Mei,MENG Xiao-hong,et al.A Technology in Fault Diagnosis of Spaceflight Launch Based on State Character[J].,2010,(05):93.
[3]王会颖 章义刚.求解聚类问题的改进人工鱼群算法[J].计算机技术与发展,2010,(03):84.
　WANG Hui-ying,ZHANG Yi-gang.An Improved Artificial Fish- Swarm Algorithm of Solving Clustering Analysis Problem[J].,2010,(05):84.
[4]赵敏 倪志伟 刘斌.K—means与朴素贝叶斯在商务智能中的应用[J].计算机技术与发展,2010,(04):179.
　ZHAO Min,NI Zhi-wei,LIU Bin.Application Research of K - Means Clustering and Naive Bayesian Algorithm in Business Intelligence[J].,2010,(05):179.
[5]吴楠 胡学钢.基于聚类分区的序列模式挖掘算法研究[J].计算机技术与发展,2010,(06):109.
　WU Nan,HU Xue-gang.Research on Clustering Partition-Based Approach of Sequential Pattern Mining[J].,2010,(05):109.
[6]耿波 仲红 徐杰 闫娜娜.用关联分析法对负荷预测结果进行二次处理[J].计算机技术与发展,2008,(04):171.
　GENG Bo,ZHONG Hong,XU Jie,et al.Using Correlation Analysis to Treat Load Forecasting Results[J].,2008,(05):171.
[7]游芳 姜建国 张坤.基于二维属性的高维数据聚类算法研究[J].计算机技术与发展,2009,(05):111.
　YOU Fang,JIANG Jian-guo,ZHANG Kun.Cluster- Algorithm Studies Based on Two- Dimensional Attribute Higher - Dimension Data[J].,2009,(05):111.
[8]刘淑英 程国建 彭方.人工神经生长细胞结构网络在医疗诊断的应用[J].计算机技术与发展,2009,(05):231.
　LIU Shu-ying,CHENG Guo-jian,PENG Fang.Applications of Growing Cell Structures of Artificial Neural Network for Medical Diagnosis[J].,2009,(05):231.
[9]范新 沈闻 丁泉勋 沈洁.基于正例和未标文档的半监督分类研究[J].计算机技术与发展,2009,(06):58.
　FAN Xin,SHEN Wen,DING Quan-xun,et al.Research on Semi- Supervised Classification Based on Positive and Unlabeled Text Document[J].,2009,(05):58.
[10]王园园 倪志伟 赵裕啸 伍章俊.基于决策树的模糊聚类评价算法及其应用[J].计算机技术与发展,2009,(09):232.
　WANG Yuan-yuan,NI Zhi-wei,ZHAO Yu-xiao,et al.Fuzzy Clustering Evaluation Algorithm Based on Decision Tree and Application[J].,2009,(05):232.

更新日期/Last Update: 1900-01-01