• Title/Summary/Keyword: 마인드 모델

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The Study on the Paradigm Change of Policy Making in the field of Korean Content Industry (문화콘텐츠 정책평가와 개선방안에 관한 연구)

  • Shim, Sang-Min
    • Review of Culture and Economy
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    • v.17 no.2
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    • pp.103-135
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    • 2014
  • This research dwells on the new paradigm of policy making of some innovative nations in Korean content industry. Especially this paper did evaluation of governmental policy in recent 5 years (2008~2012) in the field of Korean content industry. According to this research, the program of policy in Korean content industry had been obsessed to passive enrichment focusing on some part of content industry. The whole process of policy, scheme of policy and working flow of policy were very constrained in order to encourage more measurable area like CT(culture technology). Thus, we need new strong policy in this new government launched in 2013. The apparent keyword should be 'management'. New activity of policy need to focus on encourage Korean content industry in the real site of field, not in bureaucratic office in remote site. This change reflecting real filed management system would be productive innovation for policy making and activity in Korea.

Development and Application of Literacy Education program using Coaching methods (코칭기법을 활용한 문해교육프로그램 개발 및 적용)

  • Yang, Bog Yi;Kim, Jin Sook
    • The Journal of the Convergence on Culture Technology
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    • v.7 no.3
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    • pp.261-268
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    • 2021
  • After developing literacy education programs using coaching techniques, applying them to literacy learners, in order to see how they have an impact on improving learning achievement, we selected 13 senior literacy learners in U city and chose qualitative research method based on in-depth interviews, observation journals, and learning materials. Literature education programs using coaching techniques are a process-oriented model consisting of four stages of mind-opening, introducing positivity, strengthening learning competence and assistance, confidence and persistence. You can find the results as following. Firstly, communication between teachers and learners was expanded in the first stage, and secondly, self-directed learning ability was strengthened in the second stage by forming a positive mind. Thirdly, the results of utilizing the three-stage balanced literacy teaching method and interaction teaching method resulted in confidence in reading and writing, leading to an increase in self-efficacy. Fourthly, the fourth stage showed the results of improving learning achievement, which overcame the fear of learning with active praise and continuous encouragement and implied hope for higher courses. As a result of the above-mentioned research, I think literacy education programs using coaching techniques can be useful as an educational method for learners in the field of literacy education.

Personalized Speech Classification Scheme for the Smart Speaker Accessibility Improvement of the Speech-Impaired people (언어장애인의 스마트스피커 접근성 향상을 위한 개인화된 음성 분류 기법)

  • SeungKwon Lee;U-Jin Choe;Gwangil Jeon
    • Smart Media Journal
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    • v.11 no.11
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    • pp.17-24
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    • 2022
  • With the spread of smart speakers based on voice recognition technology and deep learning technology, not only non-disabled people, but also the blind or physically handicapped can easily control home appliances such as lights and TVs through voice by linking home network services. This has greatly improved the quality of life. However, in the case of speech-impaired people, it is impossible to use the useful services of the smart speaker because they have inaccurate pronunciation due to articulation or speech disorders. In this paper, we propose a personalized voice classification technique for the speech-impaired to use for some of the functions provided by the smart speaker. The goal of this paper is to increase the recognition rate and accuracy of sentences spoken by speech-impaired people even with a small amount of data and a short learning time so that the service provided by the smart speaker can be actually used. In this paper, data augmentation and one cycle learning rate optimization technique were applied while fine-tuning ResNet18 model. Through an experiment, after recording 10 times for each 30 smart speaker commands, and learning within 3 minutes, the speech classification recognition rate was about 95.2%.