大数据时代网上学习行为研究的挖掘方法模型与应用

发布时间：2018-04-04 12:04

本文选题：大数据　切入点：网上学习　出处：《电化教育研究》2017年01期

【摘要】：文章基于交互层次视角,利用大数据分析技术,对网上学习行为特征与规律研究的方法进行了探讨。所创建的"挖掘方法模型",表达了针对三种挖掘对象(服务器日志、平台课程数据库、论坛帖子集)、沿着三条挖掘主线(操作行为挖掘流程线、活动行为挖掘流程线、言语行为挖掘流程线)、掘出三种行为特征(操作行为模式、活动行为规律、言语行为特征)的总框架。该模型有助于从网络服务器系统、教学平台系统和学习制品库中寻找隐含的网上学习规则与模式,对计算机支持的协作学习、混合式协作学习的深入研究具有重要支持作用。文中详解了模型内容,提炼了模型特征,并简介了模型应用的三个案例("造访站点的路径排行挖掘""BCL中协同认知加工行为的‘转化率’分析""基于共词分析的语义网络特征挖掘")。挖掘实证表明,该模型在研究中具有良好的可行性与有效性。
[Abstract]:Based on the perspective of interaction level, this paper discusses the methods of studying the characteristics and rules of online learning behavior by using big data analysis technology.The "mining method model" is created to express three kinds of mining objects (server log, platform course database, forum post collection), along the three main mining lines (operational behavior mining process line, active behavior mining process line, active behavior mining process line).The general framework of three kinds of behavior characteristics (operational behavior pattern, activity behavior rule, speech act feature) is excavated by mining the flow line of speech act.This model is helpful to search for the implied online learning rules and patterns from web server system, teaching platform system and learning product library. It is important for the research of computer supported collaborative learning and hybrid collaborative learning.In this paper, the contents of the model are explained in detail, and the features of the model are refined.This paper also introduces three cases of model application ("path Mining of visiting sites" "'conversion' Analysis of Cooperative Cognitive processing behavior in BCL" "semantic Network feature Mining based on Co-word Analysis".The results of mining show that the model is feasible and effective in the research.
【作者单位】：湖南师范大学教育科学学院;
【基金】：全国教育科学“十二五”规划教育部重点课题“自动化教学设计的模型体系与建模语言研究”(课题编号:DCA120182)
【分类号】：G434

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