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首页> 外文期刊>Computers in Human Behavior >The Network Awareness Tool: A web 2.0 tool to visualize informal networked learning in organizations
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The Network Awareness Tool: A web 2.0 tool to visualize informal networked learning in organizations

机译：网络意识工具：一种Web 2.0工具，用于可视化组织中的非正式网络学习

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摘要

Professionals often initiate informal learning in an effort to solve work-related problems. This paper focuses mainly on research methods for visualizing informal networked learning for teachers. Drawing upon Social Network Theory and Analysis and such notions as Social Capital, Networked Learning, and Communities of Practice we create a theoretical model to underpin the design of our web 2.0, Network Awareness Tool NAT. NAT address several problems connected with informal networked learning research in organizations, such as the informal learning paradox. Advanced Search and Signalling Features can overcome the problem of under-representation of the data. Authorship minimises the problem of unrealistic representation of data. Making use of user profiles and allowing them to (re)enter and improve data on both online and off-line (and combinations thereof) networked activities offers an alternative approach to the name/ resource generating (Van der Gaag & Snijders, 2005) and position generating strategies (Lin, Fu, & Hsung, 2001) often used for collecting social network data. The implementation of a profile page, tags and ratings provides the opportunity to triangulate data on the needed multidimensional level, representing informal learning in the dimensions covered by the theoretical model. NAT could impact on our current understanding of informal networked learning.
机译：专业人士通常会发起非正式学习，以解决与工作有关的问题。本文主要关注用于可视化教师非正式网络学习的研究方法。利用社会网络理论和分析以及诸如社会资本，网络学习和实践社区等概念，我们创建了一个理论模型来支撑我们的Web 2.0网络意识工具NAT的设计。 NAT解决了与组织中的非正式网络学习研究有关的若干问题，例如非正式学习悖论。高级搜索和信令功能可以克服数据表示不足的问题。作者身份可最大程度地减少数据不真实表示的问题。利用用户个人资料并允许他们（重新）输入和改进在线和离线（及其组合）网络活动中的数据，为名称/资源生成提供了另一种方法（Van der Gaag＆Snijders，2005）和位置生成策略（Lin，Fu和Hsung，2001）通常用于收集社交网络数据。配置文件页面，标签和等级的实现提供了在所需的多维层次上对数据进行三角剖分的机会，代表了理论模型所涵盖维度中的非正式学习。 NAT可能会影响我们对非正式网络学习的当前理解。

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