• Journal of The Korean Association For Science Education
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• v.12 no.3
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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).

• Journal of Internet Computing and Services
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• v.21 no.6
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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.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.471-477
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• 2022

In this study, an educational program was developed that allows students who take data science courses in the high school credit system to explore related fields after learning data science education. Accordingly, the existing research and requirements for data science education were analyzed, a learning plan was designed, and an educational program was developed in accordance with a step-by-step educational program. In addition, since there is no research on data science education for the high school credit system in existing studies, the research was conducted in the stages of problem definition, data collection, data preprocessing, data analysis, data visualization, and simulation, and referred to studies on data science education that have been conducted in existing schools. Through this study, it is expected that research on data science education in the high school credit system will become more active.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.473-481
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• 2020

Data science starts with small data analysis and includes machine learning and deep learning for big data analysis. Data science is a core area of artificial intelligence technology and should be systematically reflected in the school curriculum. For data science education, The Entry also provides a data analysis tool for elementary education. In a big data analysis, data samples are extracted and analysis results are interpreted through statistical guesses and judgments. In this paper, the big data analysis area that requires statistical knowledge is excluded from the elementary area, and data science education examples focusing on the elementary area are proposed. To this end, the general data science education stage was explained first, and the elementary data science education stage was newly proposed. After that, an example of comparing values of data variables and an example of analyzing correlations between data variables were proposed with public small data provided by Entry, according to the elementary data science education stage. By using these Entry data-analysis examples proposed in this paper, it is possible to provide data science convergence education in elementary school, with given data generated from various subjects. In addition, data science educational materials combined with text, audio and video recognition AI tools can be developed by using the Entry.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.393-400
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• 2021

Data science is a scientific discipline that defines problems while finding meaningful information from collected data to solve problems. Along with artificial intelligence technology, the field of data utilization is gradually expanding, and awareness of the importance of data science education is also increasing. Despite the rapid growth of the domestic data industry market, it has recently been predicted that the shortfall of data experts will reach 31.4% within the next 5 years according to an analysis of the current status of the data industry by the Korea Data Agency. In the field of elementary education, various studies have been conducted to introduce data science in order to improve students' computational thinking and creativity. This paper proposed the contents of data science lectures developed for the purpose of educating elementary school teachers, who are mostly non-majors in the computer field. The developed contents were applied to a group of elementary school teachers attending graduate school for artificial intelligence convergence education. Points for improvement were derived by identifying the contents that were difficult for learners to understand and analyzing the causes of difficulty.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.337-346
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• 2021

Despite the increasing rate of use of data science in various fields of society, research on data science education programs is relatively inadequate. In this study, a data science education program for elementary school students was developed and its effectiveness was verified. We created a program that collects data using microbit, one of the physical computing tools, and developed an education program that performs the data science stage of analyzing the collected data to derive results. A study was conducted on 10 students enrolled in the Information Gifted Program at 00 University, and pre- and post-tests of computing thinking skills were conducted to verify the effectiveness. As a result, it was found that the data science education program developed through this study has a significant effect on improving the computational thinking of elementary school students.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.899-906
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• 2021

Data science is a discipline comprised of the academic fields of statistics, computer science, information technology, and domain knowledge. It analyzes data and derives meaningful results using complex technologies. Data science, along with artificial intelligence, is a core technology of the 4th industrial revolution; consequently, universities and companies worldwide are actively developing programs to develop data scientists who require high levels of expertise. In line with this undertaking, the field of elementary education has recognized the importance of data science education and so various studies have been conducted to develop curricula designed to help students understand how to use data. This paper proposes a curriculum for the purpose of educating elementary school teachers who are mostly non-majors in the computer field about data science. Satisfaction analysis was conducted based on questionnaires collected from students to analyze the effectiveness of the data science education proposed in this paper.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.633-641
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• 2020

The development of intelligent information technology based on intelligence and data and network technology implemented by artificial intelligence has instigated innovation in society as a whole and has shown wide social and economic impact. Therefore, not only overseas but also in Korea, AI education is in a hurry to cultivate talents who will lead the upcoming society. Data is an important part of artificial intelligence, and data literacy, which can collect, process, and analyze data, to make data-based decisions, can be seen as an important competency to be developed along with AI literacy. Therefore, in this study, an AI data science education program that can increase data literacy of elementary school students was developed and applied to the experimental group, and its effectiveness was verified through a pre- and post response sample t-test. As a result, all of the four detailed competencies of data literacy, data understanding, collection, analysis, and expression, showed statistically significant improvement, indicating that the AI data science education program was effective in improving students' data literacy.

• Journal of The Korean Association For Science Education
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• v.12 no.3
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• pp.17-33
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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, an analysis of papers on science education included in five selected jounals related science education was done by the use of the authors classification system for the research and development materials of science education. A total of 640 papers from the first issues to the 1991 issues of the journals were classified and analyzed. 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, 164papers), Chemical Education (published by The Korean Chemical Socity, 98 papers), The Korean Journal of Biological Education(published by The Korean Society of Biological Education, 148papers), and Journal of Science Education (published by Science Education Center.College of Education, Seoul National University, 82 papers).

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.459-469
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• 2021

In this paper, a case study of basic data science practice education for field teachers and pre-service teachers was studied. In this paper, for basic data science education, spreadsheet software was used as a data collection and analysis tool. After that, we trained on statistics for data processing, predictive hypothesis, and predictive model verification. In addition, an educational case for collecting and processing thousands of public big data and verifying the population prediction hypothesis and prediction model was proposed. A 34-hour, 17-week curriculum using a spreadsheet tool was presented with the contents of such basic education in data science. As a tool for data collection, processing, and analysis, unlike Python, spreadsheets do not have the burden of learning program- ming languages and data structures, and have the advantage of visually learning theories of processing and anal- ysis of qualitative and quantitative data. As a result of this educational case study, three predictive hypothesis test cases were presented and analyzed. First, quantitative public data were collected to verify the hypothesis of predicting the difference in the mean value for each group of the population. Second, by collecting qualitative public data, the hypothesis of predicting the association within the qualitative data of the population was verified. Third, by collecting quantitative public data, the regression prediction model was verified according to the hypothesis of correlation prediction within the quantitative data of the population. And through the satisfaction analysis of pre-service and field teachers, the effectiveness of this education case in data science education was analyzed.