|本期目录/Table of Contents|

[1]王传安,葛 华.MOOC学习中“伪学习者”行为特征分析与识别的研究[J].武汉工程大学学报,2018,40(01):109-113.[doi:10. 3969/j. issn. 1674?2869. 2018. 01. 020]
 WANG Chuanan,GE Hua.Analysis and Detection of Pseudo-Learners Behavior in Massive Open Online Courses[J].Journal of Wuhan Institute of Technology,2018,40(01):109-113.[doi:10. 3969/j. issn. 1674?2869. 2018. 01. 020]
点击复制

MOOC学习中“伪学习者”行为特征分析与识别的研究(/HTML)
分享到:

《武汉工程大学学报》[ISSN:1674-2869/CN:42-1779/TQ]

卷:
40
期数:
2018年01期
页码:
109-113
栏目:
机电与信息工程
出版日期:
2018-02-25

文章信息/Info

Title:
Analysis and Detection of Pseudo-Learners Behavior in Massive Open Online Courses
文章编号:
20180120
作者:
王传安12葛 华1
1. 安徽科技学院信息与网络工程学院,安徽 滁州 233100;2. 北京邮电大学网络技术研究院,北京 100876
Author(s):
WANG Chuan’an12 GE Hua1
1. Anhui Science and Technology University, Chuzhou 233100, China; 2. Beijing University of Posts and Telecommunications, Beijing 100876, China
关键词:
慕课伪学习者检测学习行为协同训练
Keywords:
MOOC pseudo-learners detection learning behavior Tri-training
分类号:
G434
DOI:
10. 3969/j. issn. 1674?2869. 2018. 01. 020
文献标志码:
A
摘要:
为深入了解学习者在慕课(Massive Open Online Courses,MOOC)平台下的学习行为特性,识别可能存在的伪学习行为,提出了基于协同训练的伪学习者识别模型。首先在学习者概要特征基础上,提出自主行为特征和交互信息特征,并将三者联合优化分析,以建立学习者动态行为模式;然后采用多分类器协同学习的方法对学习者行为数据进行标记,根据标记结果以判定学习者是否为伪学习者。最后的数据表明,基于协同训练的伪学习者识别模型能有效地判别一个学习者是否是伪学习者,为今后检测MOOC教学效果提供了一种依据。
Abstract:
To detect the pseudo-learners in Massive Open Online Courses (MOOC), a pseudo-learner detection model was proposed based on Tri-training. First, new behavior characteristics such as autonomic behavior and interactive information were proposed based on the analysis of the learners’ fundamental characteristics, and the learner’s dynamic behavior pattern was established by the joint analysis of the above characteristics. Second,a multi-classifier was used to identify the learner’s behavior data to detect pseudo-learners. Finally, experiment results indicate the proposed method can be used to detect whether a learner is a pseudo-learner, which has the potential to partially evaluate the MOOC teaching effect in practice.

参考文献/References:

[1] 蒋卓轩,张岩,李晓明. 基于MOOC数据的学习行为分析与预测[J]. 计算机研究与发展,2015,52(3):614-628. [2] 梁林梅. MOOCs学习者分类特征与坚持性[J]. 比较教育研究, 2015, 37(1):28-34. [3] CHANG J W. Exploring engaging gamification mechanics in massive online open courses [J]. Journal of Educational Technology & Society,2016 ,19 (2) :177-203. [4] 李帅,张岩峰,于戈,等. MOOC平台学习行为数据的采集与分析[J]. 中国科技论文, 2015,10(20):2373-2376. [5] RODRIGUEZ C. MOOCs and the AI-stanford like courses: two successful and distinct course formats for massive open online courses [J]. European Journal of Open, Distance and E-Learning , 2012, 1(2):1-13. [6] BRESLOW L, PRITCHARD D , DEBOER J, et al. Studying learning in the worldwide classroom research into edX’s first MOOC [J]. Research & Practice in Assessment, 2013 , 8 (1):13-25[7] MILLIGAN C, LITTLEJOHN A , MARGARYAN A. Patterns of engagement in connectivist MOOCs [J]. Journal of Online Learning & Teaching, 2017, 9(2): 149-159. [8] SHEN C W, KUO C J. Learning in massive open online courses: Evidence from social media mining [J]. Computers in Human Behavior , 2015 , 51(3):568-577.[9] GLYN H, CHELSEA D. The utilization of data analysis techniques in predicting student performance in massive open online courses (moocs) [J]. Research and Practice in Technology Enhanced Learning, 2015,10(1):1-18. [10] HEATHER B,SHAPIROC C, NOELLE E, et al. Understanding the massive open online course (MOOC) student experience: an examination of attitudes, motivations, and barriers [J]. Computers & Education , 2017 , 110(3) :35-50. [11] WANG M M,ZUO W L,WANG Y. A multidimensional nonnegative matrix factorization model for retweeting behavior prediction [J]. Mathematical Problems in Engineering Volume, 2015,5(1): 1-10. [12] 陆悠,李伟,罗军舟,等. 一种基于选择性协同学习的网络用户异常行为检测方法[J]. 计算机学报,2014,37(37):28-40. [13] 陈文,张恩阳,赵勇. 基于多分类器协同学习的卷积神经网络训练算法[J]. 计算机科学,2016,43(9):223-227. [14] ANGLUIN D,LAIRD P. Learning from noisy examples [J]. Machine Learning, 1988, 2(4): 343-370. [15] 李赫元,俞晓明,刘悦,等. 中文微博客的垃圾用户检测[J]. 2014,28(3):62-68. Society of America B, 2017,34(3): A49-A62.

相似文献/References:

备注/Memo

备注/Memo:
基金项目:安徽省质量工程教研项目(2014Mvoco38)作者简介:王传安, 博士研究生,副教授。 E-mail:[email protected]引文格式:王传安, 葛华. MOOC学习中“伪学习者”行为特征分析与识别的研究 [J]. 武汉工程大学学报,2018,40(1):109-113.
更新日期/Last Update: 2018-02-01