A computational tool for lifelong learning: Experiencing breakdowns and understanding situations

  • Kumiyo Nakakoji
  • Masao Ohira
  • Akio Takashima
  • Yasuhiro Yamamoto
Part of the Computer-Supported Collaborative Learning book series (CULS, volume 9)

Abstract: Our approach to supporting lifelong learning involves regarding a breakdown as an opportunity for learning. In this approach, systems for lifelong learning need to support a learner in: (1) experiencing a breakdown, and (2) asking for information relevant to the breakdown situation. Knowledge-based critiquing systems, which have been studied to support these processes, monitor human performance within its computational environment, identify a potentially problematic situation, alert the user about the situation to make the user aware of the potential problem, and provide explanation for the criticism and information relevant to the problem. Although found to be effective, such critiquing systems depend on knowledge-bases that need to be built by knowledge engineers prior to the use of the system. Thus, such systems cannot afford synchronous collaborative learning among practitioners. To complement this aspect, this paper presents a new system, EVIDII (Environment for Visualizing Individual Differences of Impressions), which visualizes differences among associations made by individual group members between two sets, for instance, pictorial images and words. By interacting with EVIDII, a user is encouraged to experience a breakdown by encountering an unexpected association made by other group members, and to ask for more information about the association from the other members. This embraces further communication and knowledge construction among group members. Scenarios are provided to illustrate how learning takes place when using EVIDII.


Computational Environment Tacit Knowledge Computational Tool Lifelong Learning Problematic Situation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC. 2007

Authors and Affiliations

  • Kumiyo Nakakoji
    • 1
  • Masao Ohira
    • 2
  • Akio Takashima
  • Yasuhiro Yamamoto
    • 3
  1. 1.Research Center for Advanced Science and TechnologyUniversity of TokyoTokyoJapan
  2. 2.Graduate School of Information ScienceNara Institute of Science and TechnologyIkomaJapan
  3. 3.SRA Key Technology Laboratory Inc.ShinjukuJapan

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