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基于MOOCs的多元同心学习分析模型构建
引用本文:花燕锋,张龙革.基于MOOCs的多元同心学习分析模型构建[J].电大教学,2014(5):104-112.
作者姓名:花燕锋  张龙革
作者单位:吉林大学高等教育研究所,吉林长春130012
基金项目:本文系吉林省教育科学“十二五”规划项目“小学英语生态课堂建构的个案研究”(项目编号:GHl3003)阶段性研究成果.
摘    要:随着MOOCs在国内外迅猛发展,其高退学率低通过率导致教学效果不尽人意的问题,逐渐成为研究者关注的焦点。学习分析为解决该难题提供了一系列的研究方法和技术支持。为此,以学习分析促进MOOCs的个性化教育为核心,从学习过程、学习环境、教育环境、数据挖掘、应用支持服务、受益者等多元化视角出发,构建基于MOOCs的多元同心学习分析模型。并在已有研究的基础上归纳出支持该学习分析模型的关键技术。最终,以学习者特征识别、学习者在线学习行为分析、学习者交互分析三类典型应用来演示多元同心学习分析模型在MOOCs中的具体应用.以期为学习分析在M00Cs中的具体实施提供指导。

关 键 词:MOOCs  学习分析  数据挖掘  模型构建与应用  个性化教育

Research on Construction and Application of Multiplex and Concentric Learning Analytics Model Based onMOOCs
Authors:Hua Yanfeng & Zhang Longge
Institution:Hua Yanfeng & Zhang Longge (Institute for Higher Education, Jilin University, Changchun, Jilin 130012)
Abstract:With the overwhelming development of MOOCs all over the world, the most important problem is a high dropout rate and low through-rate of MOOCs. How to solve this problem has become the focus of this study. Learning analytics provides a series of research methods and technology for solving the problem, which is regarded to promote the personalized education of MOOCs. From the diverse perspectives of learning process, learning environment, education environment, data mining, support services and the beneficiaries, multiplex and concentric learning analyties model based on MOOCs is constructed. According to the existing researches, the key technology and tools to support this model is summed up. Finally, three types of typical applications including the recognition of students' feature,analysis of students'online learning behavior and analysis of students' interaction are proposed to demonstrate the application of the model, in order to provide enlightenment for the application of learning analytics in MOOCs.
Keywords:MOOCs  Learning analytics  Data mining  Construction and application of the model  Personalized education
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