| 国内网络学习行为研究的现状与分析 |
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| 作者姓名： | 刘春芝 魏兰兰 张一春 |
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| 作者单位： | 南京师范大学教育科学学院 |
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| 基金项目： | 2019年江苏省高等教育教改研究课题“智慧课堂交互系统模型及教学模式构建”(2019JSJG550)。 |
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| 摘 要： | 根据第46次《中国互联网络发展状况统计报告》统计,我国在线教育用户已达3.81亿,网络学习已成为一种重要的学习方式,国内对于网络学习行为的研究也愈加丰富。分析当前的网络学习行为研究现状,能够为后续的研究提供更加有力的帮助。本研究选取我国2005-2020年发表在国内公开学术平台上的网络学习行为论文,借助Bicomb 2.0工具,从文献时间分布、文献作者分布、高频关键词词频、网络学习行为研究的关注点四个方面入手进行分析,研究结果表明目前国内网络学习行为研究主要聚焦于网络学习行为的内涵、分类、影响因素和模型四个范畴。基于此,笔者提出具体化网络学习行为内涵、深度化网络学习行为分析和实践化网络学习行为模型等建议,以期为研究者进行后续的深入研究提供一定的帮助。
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| 关 键 词： | 网络 学习行为 现状 |
The Current Status and Analysis of National Network Learning Behavior Research |
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| Authors: | LIU Chunzhi WEI Lanlan ZHANG Yichun |
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| Institution: | (School of Education and Science,Nanjing Normal University,Nanjing,Jiangsu,China 210097) |
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| Abstract: | According to"The Statistics of the 46th Statistical Reports on Internet Development in China",the online educational users in China have already been 381 million.Network learning has become an important learning method,and the research on the network learning behavior in China is becoming richer.The analysis of the research status of the current network learning behavior can provide much more powerful assistance for the subsequent research.This research has chosen the national network learning behavior papers published in national public academic platform from 2005 to 2020,and with the assistance of the tool of Bicomb 2.0,has analyzed from four aspects,namely,time distribution of literatures,author distribution of literatures,high-freguency key words freguency,and the research focuses of network learning beharior.The research results indicate that nowadays national research of network learning behavior mainly focuses on four categories of network learning behavior,namely,connotation,classification,influential factors and model.Based on the above,the author has put forward some suggestions like crystallizing the connotation of network learning behavior,deepening the analysis of network learning behavior and practicing network learning behavior model,etc.,hoping to provide certain help for the researchers to do subsequent deep researches. |
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| Keywords: | network learning behavior current status |
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