[1]马志强,管 秀.面向多维关联的社会认知网络分析——协作学习交互研究的新进展[J].远程教育杂志,2020,(06):096-103.[doi:10.15881/j.cnki.cn33-1304/g4.2020.06.010]
 Ma Zhiqiang,Guan Xiu.Social Epistemic Network Analysis of Multidimensional Association:New Development of Collaborative Learning Interaction Research[J].Distance Education Journal,2020,(06):096-103.[doi:10.15881/j.cnki.cn33-1304/g4.2020.06.010]
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面向多维关联的社会认知网络分析——协作学习交互研究的新进展()
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《远程教育杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年06期
页码:
096-103
栏目:
学习新论
出版日期:
2020-11-22

文章信息/Info

Title:
Social Epistemic Network Analysis of Multidimensional Association:New Development of Collaborative Learning Interaction Research
作者:
马志强; 管 秀
1.江南大学 “互联网+教育”研究基地,江苏无锡 214122;2.华东师范大学 教育信息技术学系,上海 200241
Author(s):
Ma Zhiqiang; Guan Xiu
1.Jiangnan University, Research Center of“Internet Plus Education”, Jiangsu Wuxi 214122; 2.East China Normal University, Department of Education Information Technology, Shanghai 200241
关键词:
社会认知网络分析社会网络分析认知网络分析学习分析CSCL
Keywords:
Social Epistemic Network Analysis Social Network Analysis Epistemic Network Analysis Learning Analysis CSCL
分类号:
G420
DOI:
10.15881/j.cnki.cn33-1304/g4.2020.06.010
文献标志码:
A
摘要:
协作学习交互研究的核心是从“横向的关系构成”与“纵向的关系演化”两个维度,对小组成员的“生产性”相互作用关系进行分析。将社会认知网络分析引入协作交互研究中,并重点探讨社会认知网络的内涵、分析框架与要素及分析思路,得到如下结论:首先,社会认知网络分析作为协作学习交互研究的新视角,能够从社会认知网络结构与演化的角度对协作交互过程进行多维、关联、动态化的表征和分析;其次,社会认知网络分析框架融入了社会网络和认知网络的关键指标,包含节点层、关系层和网络层三个层级;最后,社会认知网络分析基本过程包含目标与编码框架确定、数据整理与编码、网络模型构建以及网络模型分析。进一步进行案例分析发现,社会认知网络分析方法能够揭示协作学习小组的社会认知网络结构特征与关键指标,并描述小组网络结构演化的基本过程。基于此,未来研究发展方向在于:扩展社会认知网络分析框架与指标,实现对社会认知网络节点与关系的精准描述;采用智能语音识别与深度神经网络结合的方式分析与编码,减轻数据处理和分析的负担;探索将社会认知网络分析整合到论坛、WIKI等协作学习平台中,实现社会认知网络的动态可视化呈现。
Abstract:
The core of the interactive analysis of collaborative learning is to analyze the "productive" interaction among group members from the horizontal relationship structure and vertical relationship changes. In order to use the social epistemic analysis network in collaboration research, this study focuses on introducing the basic connotation of social epistemic network, analysis framework and elements, and basic analysis paths. The basic conclusions are: Firstly, social epistemic network as a new perspective of collaborative learning interaction research, can do multi-dimensional, related, dynamic representation and analysis from two perspectives of network structure and evolution process. Secondly, the social epistemic network analysis framework combines the social network and epistemic network key indicators, and includes node layer, relation layer and network layer to analysis the collaborative interaction process. Finally, the basic process of social epistemic network analysis includes the determination of goals and coding framework, data sorting and coding, network model integration and network model analysis. The case analysis results show that the social epistemic network can reveal the structural characteristics and key indicators of the social epistemic network of collaborative learning groups, and describe the basic process of the evolution of the network structure. The directions of future research development are expanding the social epistemic network analysis framework and indicators to achieve more accurate description of the social epistemic network. Using the combination of intelligent speech recognition and deep neural network to analyze and code, which can reduce the burden of data processing and analysis. Exploring the integration of social cognitive network analysis into forums, WIKI and other collaborative learning platforms to realize the dynamic visual presentation of social cognitive network.

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备注/Memo

备注/Memo:
基金项目:本文系2018年度国家社科基金教育学青年项目“在线协作学习投入分析与评价研究”(项目编号:CCA180257)的研究成果。
更新日期/Last Update: 1900-01-01