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引用次数: 73
摘要
Web 2.0的巨大成功主要得益于允许Web用户轻松创建、共享、标记和连接内容和知识的基础设施。以这种方式开发结构化知识的工具也开始出现。然而,很少有(如果有的话)用户研究旨在了解用户对这些工具的期望,哪些有效,哪些无效。我们组织了协作知识构建(CKC)挑战,以评估支持创建各种形式的结构化知识的协作过程的工具的技术状态。挑战赛的目标是让用户尝试不同的工具,并了解用户对这些工具的期望——用户需要的功能,他们喜欢或不喜欢的功能。Challenge的任务是为提供研究信息的门户构建结构化知识。挑战赛的设计包含了一些鼓励用户参与的激励措施。49名用户注册参加挑战赛;其中33人通过使用工具积极参与。我们从用户那里收集了大量的反馈,他们讨论了他们对所尝试的所有工具的想法。在本文中,我们展示了挑战的结果,讨论了用户期望从协作知识构建工具中获得的功能,挑战参与者不同意的功能,以及我们学到的教训。
The CKC Challenge: Exploring Tools for Collaborative Knowledge Construction.
The great success of Web 2.0 is mainly fuelled by an infrastructure that allows web users to create, share, tag, and connect content and knowledge easily. The tools for developing structured knowledge in this manner have started to appear as well. However, there are few, if any, user studies that are aimed at understanding what users expect from such tools, what works and what doesn't. We organized the Collaborative Knowledge Construction (CKC) Challenge to assess the state of the art for the tools that support collaborative processes for creation of various forms of structured knowledge. The goal of the Challenge was to get users to try out different tools and to learn what users expect from such tools-features that users need, features that they like or dislike. The Challenge task was to construct structured knowledge for a portal that would provide information about research. The Challenge design contained several incentives for users to participate. Forty-nine users registered for the Challenge; thirty-three of them participated actively by using the tools. We collected extensive feedback from the users where they discussed their thoughts on all the tools that they tried. In this paper, we present the results of the Challenge, discuss the features that users expect from tools for collaborative knowledge constructions, the features on which Challenge participants disagreed, and the lessons that we learned.
期刊介绍:
IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications. Typically they are degreed professionals, with backgrounds in engineering, hard science, or business. The publication emphasizes current practice and experience, together with promising new ideas that are likely to be used in the near future. Sample topic areas for feature articles include knowledge-based systems, intelligent software agents, natural-language processing, technologies for knowledge management, machine learning, data mining, adaptive and intelligent robotics, knowledge-intensive processing on the Web, and social issues relevant to intelligent systems. Also encouraged are application features, covering practice at one or more companies or laboratories; full-length product stories (which require refereeing by at least three reviewers); tutorials; surveys; and case studies. Often issues are theme-based and collect articles around a contemporary topic under the auspices of a Guest Editor working with the EIC.