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      An Intelligent Learning Agent using Learner’s Information in Mobile Environment

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      https://www.riss.kr/link?id=A100237462

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      다국어 초록 (Multilingual Abstract)

      Recently, Since people always carry mobile devices and can learn whenever and wherever by using not only phone, text, SNS, and TV, but also using learning device, recorder, e-book, e-dictionary, and internet surfing due to rapid distribution of smartp...

      Recently, Since people always carry mobile devices and can learn whenever and wherever by using not only phone, text, SNS, and TV, but also using learning device, recorder, e-book, e-dictionary, and internet surfing due to rapid distribution of smartphone, the interest in intelligent learning agent related to situated learning is increasing more. The method of practicing the situated learning theories developed so far provides learning contents to learners by analyzing their environment information along with their basic information. However, if learning contents are recommended by considering only basic and partial information of a learner, in many cases, they do not match with the demand from the learner, so it could become factors that decrease learner’s learning desire and obstruct inducing learning motivation. Therefore, this study propose an intelligent learning agent that automatically recommends learning contents that learners want in timely manner in their real life by analyzing not only learners’ location information, but also their available time for learning and Internet activity pattern.

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      목차 (Table of Contents)

      • Abstract
      • 1. Introduction
      • 2. Related Study
      • 2.1. Situated Earning
      • 2.2. Intelligent Agent
      • Abstract
      • 1. Introduction
      • 2. Related Study
      • 2.1. Situated Earning
      • 2.2. Intelligent Agent
      • 3. The Proposed Intelligent Learning Agent
      • 3.1. Context Awareness Module
      • 3.2. Learning Recommendation Module
      • 3.3. System Mapping Module
      • 4. System Implementation and Evaluation
      • 4.1. Overview of the Experiment
      • 4.2. Operation Test
      • 5. Conclusion
      • Acknowledgments
      • References
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