• Title/Summary/Keyword: 지식 기반 한국어 교육

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A Study on the Application of Google Classroom for Problem-Based Learning (문제중심학습을 위한 구글크레스룸 활용 방안 연구)

  • Bayarmaa, Natsagdorj;Lee, Keunsoo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.7
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    • pp.81-87
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    • 2018
  • Problem-based learning (PBL) appears to be a superior and effective strategy to train competent and skilled practitioners and to promote long-term retention of knowledge and skills acquired during the learning experience. This study concerns the implementation of PBL in the online environment and face-to-face PBL. An online environment allows participants to communicate with one another, view presentations or videos, interact with other participants, and engage with resources in work groups. Nowadays, education is accessible everywhere with the use of digital devices. Educational institutions subscribe to GSuite for Education, and Google introduced its Google Classroom as an e-learning platform. This study aims to analyze Google Classroom and to design PBL for Mongolian students taking Korean courses. The main objective of this paper is to identify the usability and evaluation of Google Classroom. The result of this study will be a proposed e-learning platform for Dornod University, Mongolia, which is initially needed in the Natural Science and Business Department.

A Method of Mining Visualization Rules from Open Online Text for Situation Aware Business Chart Recommendation (상황인식형 비즈니스 차트 추천기 개발을 위한 개방형 온라인 텍스트로부터의 시각화 규칙 추출 방법 연구)

  • Zhang, Qingxuan;Kwon, Ohbyung
    • The Journal of Society for e-Business Studies
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    • v.25 no.1
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    • pp.83-107
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    • 2020
  • Selecting business charts based on the nature of the data and the purpose of the visualization is useful in business analysis. However, current visualization tools lack the ability to help choose the right business chart for the context. Also, soliciting expert help about visualization methods for every analysis is inefficient. Therefore, the purpose of this study is to propose an accessible method to improve business chart productivity by creating rules for selecting business charts from online published documents. To this end, Korean, English, and Chinese unstructured data describing business charts were collected from the Internet, and the relationships between the contexts and the business charts were calculated using TF-IDF. We also used a Galois lattice to create rules for business chart selection. In order to evaluate the adequacy of the rules generated by the proposed method, experiments were conducted on experimental and control groups. The results confirmed that meaningful rules were extracted by the proposed method. To the best of our knowledge, this is the first study to recommend customizing business charts through open unstructured data analysis and to propose a method that enables efficient selection of business charts for office workers without expert assistance. This method should be useful for staff training by recommending business charts based on the document that he/she is working on.