• 인터넷정보학회논문지
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• 제21권6호
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• pp.133-139
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• 2020

Big data, artificial intelligence (AI), and machine learning are keywords that represent the Fourth industrial Revolution. In addition, as the development of science and technology, the Korean government, public institutions and industries want professionals who can collect, analyze, utilize and predict data. This means that data analysis and utilization education become more important. Education on data analysis and utilization is increasing with trends in other academy. However, it is true that not many academy run long-term and systematic education. Korea Institute of Science and Technology Information (KISTI) is a data ecosystem hub and one of its performance missions has been providing data utilization and analysis education to meet the needs of industries, institutions and governments since 1966. In this study, KISTI's data education was analyzed using the number of curriculum trainees per year from 2001 to 2019. With this data, the change of interest in education in information and data field was analyzed by reflecting social and historical situations. And we identified the characteristics of KISTI and trainees. It means that the identity, characteristics, infrastructure, and resources of the institution have a greater impact on the trainees' interest of data-use education.In particular, KISTI, as a research institute, conducts research in various fields, including bio, weather, traffic, disaster and so on. And it has various research data in science and technology field. The purpose of this study can provide direction forthe establishment of new curriculum using data that can represent KISTI's strengths and identity. One of the conclusions of this paper would be KISTI's greatest advantages if it could be used in education to analyze and visualize many research data. Finally, through this study, it can expect that KISTI will be able to present a new direction for designing data curricula with quality education that can fulfill its role and responsibilities and highlight its strengths.

• International Journal of Internet, Broadcasting and Communication
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• 제12권1호
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• pp.137-143
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• 2020

With the development of information and communication technology, various data have appeared and are being distributed. The use of various data has contributed to the enrichment and convenience of our lives. Data in the public areas is also growing in volume and being actively used. Public data in the field of education are also used in various ways. As the distribution and use of public data has increased, advantages and disadvantages have started to emerge. Among the various disadvantages, the privacy problem is a representative one. In this study, we deal with the privacy issues of public data in education. First, we introduce the privacy issues of public data in the education field and suggest various solutions. The various solutions include the expansion of privacy education opportunities, the need for a new privacy protection model, the provision of a training opportunity for privacy protection for teachers and administrators, and the development of a real-time privacy infringement diagnosis tool.

• 한국과학교육학회지
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• 제12권3호
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• pp.35-47
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• 1992

The purpose of this study was to develop a data base system for the information on science education research and development. As a part of this study, a data base program was developed on the Macintosh SE using the 4th Dimension from ACI. The data base consisted of two files, dissertation and journal. The information on the 107 theses including the master's theses and the doctoral dissertations from the Department of Scince Education, Seoul National University and on the 640 papers on science education from the first issues to the 1991 issues of five selected science education journals was input into the data base. The selected five Journals were Journal of the Korean Association for Research in Science Education(published by the Korean Association for Research in Science Education, 148 papers), Teaching Physics(published by Korean Physical Society, 164 papers),Chemical Education(published by The Korean Chemical Society, 98 papers), The Korean Journal of Biological Education(published by The Korean Society of Biological Education, 148 papers), and Journal of Science Education(published by Science Education Center, College of Education, Seoul National University,82 papers).

• 공학교육연구
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• 제22권5호
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• pp.37-48
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• 2019

The government has tried to develop a platform for systematically collecting and managing engineering education data for policy proposals. However, there have been few cases of big data analysis platform for policy proposals in engineering education, and it is difficult to determine the major function of the platform, the purpose of using big data, and the method of data collection. This study aims to collect the cases of big data analysis systems for the development of a big data system for educational policy proposals, and to conduct a study to analyze cases using the analysis frame of key elements to consider in developing a big data analysis platform. In order to analyze the case of big data system for engineering education policy proposals, 24 systems collecting and managing big data were selected. The analysis framework was developed based on literature reviews and the results of the case analysis were presented. The results of this study are expected to provide from macro-level such as what functions the platform should perform in developing a big data system and how to collect data, what analysis techniques should be adopted, and how to visualize the data analysis results.

• 실천공학교육논문지
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• 제13권3호
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• pp.435-442
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• 2021

본 연구에서는 학교 운동부 학생 선수들에게 스포츠 데이터 활용에 대한 전반적인 소양을 교육하는 프로그램을 개발하였다. 이에 스포츠 데이터에 대하여 기존 연구와 요구사항에 대하여 분석하고 학습 계획을 설계하였으며, 단계별 교육 프로그램에 따라 교육 프로그램을 개발하였다. 또한 기존 연구에서 학교 운동부 및 성인 스포츠 관계자들을 위한 데이터 과학 교육에 대한 연구가 전무하므로, 기존의 학교 현장에서 연구되었던 데이터 과학 교육에 대한 연구를 참고하여 문제 정의, 데이터 수집, 데이터 전처리, 데이터 분석, 데이터 시각화, 모의 분석의 단계로 연구를 진행하였다. 본 연구를 통하여 스포츠 데이터에 대한 스포츠 산업의 관심이 높아질 것으로 기대한다.

• 의학교육논단
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• 제25권2호
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• pp.126-131
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• 2023

The aim of this study was to systematically collect data for evaluating short- and long-term outcomes using Kirkpatrick's four-level evaluation model, Chonnam National Medical School has established plans for developing and managing a database of student and graduate cohorts. The Education Evaluation Committee, with assistance from the Medical Education Office, manages the development and maintenance of cohort data. Data collection began in the 2022 academic year with first- through fourth-year medical students and graduates of the year 2022. The collected data include sociodemographic characteristics, admission information, psychological test results, academic performance data, extracurricular activity data, scholarship records, national medical licensing exam results, and post-graduation career paths. The Education Evaluation Committee and the Medical Education Office analyze the annually updated student and graduate cohort data and report the results to the dean and relevant committees. These results are used for admissions processes, curriculum improvement, and the development of educational programs. Applicants interested in using the student and graduate cohort data to evaluate the curriculum or conduct academic research must undergo review by the Educational Evaluation Committee before being granted access to the data. It is expected that the collected data from student and graduate cohorts will provide a sound and scientific basis for evaluating short- and long-term achievements based on student, school, and other characteristics, thereby supporting medical education policies, innovation, and implementation.

• 정보교육학회논문지
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• 제20권1호
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• pp.13-20
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• 2016

실세계에서 데이터는 숫자와 문자, 이미지, 소리 등 다양한 형태로 존재한다. 하지만 컴퓨터에서는 실세계의 데이터를 디지털 형태인 1과 0으로 표현하고 있다. 초등학생들은 전통적인 강의식 방법으로는 컴퓨터에서 사용하고 있는 데이터 표현에 대한 개념을 이해하기에는 매우 어렵다. 따라서 본 논문에서는 초등학생들이 데이터 표현 개념을 쉽게 이해할 수 있도록 기존의 연구를 분석하고 언플러그드 교수-학습 방법에 대해 연구하였다. 이를 위해 정보교육학회 소프트웨어교육 내용 체계를 분석하고, 초등학교 중학년을 위한 데이터 표현 교육 내용을 선정하였다. 또한 데이터 표현 교육을 위해 언플러그드 방식을 이용한 교수-학습 지도안을 개발하고 수업자료를 개발하였다. 본 연구에서 제시한 언플러그드를 통한 교수-학습 방법이 데이터 표현 교육에 도움이 되기를 기대한다.

• 실천공학교육논문지
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• 제14권3호
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• pp.471-477
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• 2022

본 연구에서는 고교학점제에서 데이터과학 과목을 신청한 학생들을 위하여 교육 프로그램을 개발하였다. 이에 데이터과학교육에 대하여 기존 연구와 요구사항을 분석하고 학습 계획을 설계하였으며, 단계별 교육 프로그램에 따라 교육 프로그램을 개발하였다. 또한 기존 연구에서 고교학점제를 위한 데이터과학교육에 대한 연구가 활발하지 않았으므로 기존의 학교 현장에서 연구되었던 데이터 과학 교육에 대한 연구를 참고하여 문제 정의, 데이터 수집, 데이터 전처리, 데이터 분석, 데이터 시각화, 모의 분석의 단계로 연구를 진행하였다. 본 연구를 통하여 고교학점제에서 데이터과학교육에 대한 연구가 더욱 활발해질 것으로 기대한다.

• 통합자연과학논문집
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• 제14권4호
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• pp.176-182
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• 2021

We live in the age of artificial intelligence, and big data and artificial intelligence education are no longer just for majors, but are required to be able to handle non-majors as well. Software and artificial intelligence education for non-majors is not just a general education, it creates talents who can understand and utilize them, and the quality of education is increasingly important. Through such education, we can nurture creative talents who can create and use new values by fusion with various fields of computing technology. Since 2015, many universities have been implementing software-oriented colleges and AI-oriented colleges to foster software-oriented human resources. However, it is not easy to provide AI basic statistics education of big data analysis deception to non-majors. Therefore, we would like to present a big data education model for non-majors in big data analysis so that big data analysis can be directly applied.

• 정보교육학회논문지
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• 제24권5호
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• pp.473-481
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• 2020

데이터과학은 스몰데이터 분석에서 출발하여, 빅데이터 분석을 위한 머신러닝, 딥러닝까지 포함하고 있다. 데이터과학은 인공지능 기술의 핵심 영역이고, 학교 교육과정에 체계적으로 반영해야 할 내용이다. 데이터과학 교육을 위해, 엔트리에서도 초등교육용 데이터 분석 도구를 제공하고 있다. 빅데이터 분석에서는 데이터 표본을 추출하여, 통계학적인 추측과 판단을 통해 분석결과를 해석한다. 본 논문에서는 통계학적인 지식을 필요로 하는 빅데이터 분석 영역을 초등영역에서 제외하기로 하고, 초등영역에 초점을 맞춘 데이터과학 교육 사례를 제안하였다. 이를 위해서, 일반적인 데이터과학 교육 단계를 먼저 설명하고, 초등 데이터과학 교육 단계를 새롭게 제안하였다. 그리고 엔트리에서 제공하는 공공 스몰 데이터를 사용한 데이터 변수 값 비교 사례와 데이터 변수 간 상관관계 분석 사례를 초등 데이터과학 교육 단계에 따라 제안하였다. 본 논문에서 제안된 엔트리 데이터분석 사례들을 활용하면, 여러 교과에서 발생하는 데이터를 사용한 초등 데이터과학 융합 교육이 가능하다. 또한, 엔트리를 사용하여 텍스트, 음성 및 영상인식 AI 도구와 결합한 데이터과학 교육 자료도 개발 가능하다.