[1]郭涵阳,高曼如,沈良忠. Moodle平台师生访问行为日志统计与挖掘研究[J].计算机技术与发展,2016,26(11):168-171.
　GUO Han-yang,GAO Man-ru,SHEN Liang-zhong. Research on Statistics and Mining of Log Data about Visiting Behavior for Both Teachers and Students from Moodle[J].,2016,26(11):168-171.
点击复制

 Moodle平台师生访问行为日志统计与挖掘研究()
分享到：

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

卷:
26
期数:
2016年11期
页码:
168-171
栏目:
应用开发研究
出版日期:
2016-11-10

文章信息/Info

Title:
 Research on Statistics and Mining of Log Data about Visiting Behavior for Both Teachers and Students from Moodle
文章编号:
1673-629X（2016)11-0158-04
作者:
 郭涵阳; 高曼如; 沈良忠
 温州商学院
Author(s):
 GUO Han-yang; GAO Man-ru; SHEN Liang-zhong
关键词:
 ; Moodle; 统计分析; 数据挖掘; 聚类
Keywords:
 ; Moodle; statistical analysis; data mining; clustering
分类号:
TP311
文献标志码:
A
摘要:
 信息技术的快速发展催生了网络教学平台的不断涌现，其中Moodle平台因其开源特征被认为是当前构建MOOC (大规模开放在线课程)的有力平台之一。网络课程教学的有效开展使得Moodle系统日志表中存储了大量的师生访问行为数据，有效的日志统计挖掘有助于发现大量数据背后潜在的访问规律。通过统计分析、数据挖掘等技术实现了对日志数据的深入研究，并针对分析结果提出了Moodle课程使用中存在的问题及相应的改进意见，不仅有助于理解师生在Moo-dle平台的课程访问学习规律，而且能为Moodle课程的教学评价提供有价值的参考建议，有助于后期Moodle课程自动评价系统的研究。
Abstract:
 The rapid development of information technology has led to the emergence of the online teaching platforms,among which Moo-dle system is considered to be one of the powerful platform to build MOOC ( Massive Open Online Course) because of its characteristic of open-source. The effective development of online course teaching makes the Moodle system accumulate a lot of visit behavior data of both teachers and students. An effective analysis on the log data would surely help discover the underlying pattern. It makes a deep study on these log data by means of statistical analysis and data mining technologies. Based on the result,the existing problems and correspond-ing improving suggestions are presented which not only help to understand behaviors of both teachers and students,but provide valuable suggestions for Moodle course’ s teaching evaluation which is also helpful for the later research on the automatic evaluation system of Moodle course.

相似文献/References:

[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.
　ZHANG Guo-rong.Design and Realization of Data Mining Method in Moodle[J].,2014,24(11):231.
[2]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(11):1.
[3]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(11):5.
[4]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(11):13.
[5]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(11):21.
[6]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(11):25.
[7]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(11):29.
[8]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(11):34.
[9]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(11):38.
[10]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(11):43.

更新日期/Last Update: 2016-12-16