• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.417-425
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• 2022

The 'Comprehensive Plan for Nurturing Digital Talents' reported at the Cabinet meeting of the Ministry of Education in August 2022 focuses on qualitative and quantitative expansion of informatics education centered on SW, AI education. With the advent of the era of artificial intelligence, data science education is also drawing attention as a field of informatics education. Data science is originally a field where various studies are fused, and advanced technologies are being used for data analysis, modeling, and machine learning. This study devised a draft of the instructional model of data science education through literature research and analysis of previous studies, and developed a final instructional model through usability test and expert validation.

• Journal of The Korean Association For Science Education
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• v.42 no.2
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• pp.177-184
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• 2022

This article aims to explain the concept and characteristics of the 'Intelligent Science Lab', which is being promoted nationwide in Korea since 2021. The Korean Ministry of Education creates a master plan containing a vision for science education every five years. The most recently announced '4th Master plan for science education (2020-2024)' emphasizes the policy of setting up an 'intelligent science lab' in all elementary and secondary schools as an online and offline space for scientific inquiry using advanced technologies, such as Internet of Things and Augmented and Virtual Reality. The 'Intelligent Science Lab' project is being pursued in two main directions: (1) developing an online platform named 'Intelligent Science Lab-ON' that supports science inquiry classes, and (2) building a science lab space in schools that encourages active student participation while utilizing the online platform. This article presents the key features of the 'Intelligent Science Lab-ON' and the characteristics of intelligent science lab spaces newly built in schools. Furthermore, it introduces inquiry-based science learning programs developed for intelligent science labs. These programs include scientific inquiry activities in which students generate and collect data ('data generation' type), utilize datasets provided by the online platform ('data utilization' type), or utilize open and public data sources ('open data source' type). The Intelligent Science Lab project is expected to not only encourage students to engage in scientific inquiry that solves individual and social problems based on real data, but also contribute to presenting a model of online and offline linked scientific inquiry lessons required in the post-COVID-19 era.

• Journal of Integrative Natural Science
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• v.14 no.4
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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.

• Journal of the Korean earth science society
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• v.39 no.4
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• pp.388-400
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• 2018

The purpose of this study was to explore the nine components of computational thinking (CT) practices and their operational definitions from the view of science education and to develop a CT practice framework that is going to be used as a planning and assessing tool for CT practice, as it is required for students to equip with in order to become creative problem solvers in $21^{st}$ century. We employed this framework into the earlier developed STEAM programs to see how it was valid and reliable. We first reviewed theoretical articles about CT from computer science and technology education field. We then proposed 9 components of CT as defined in technology education but modified operational definitions in each component from the perspective of science education. This preliminary CTPF (computational thinking practice framework) from the viewpoint of science education consisting of 9 components including data collection, data analysis, data representation, decomposing, abstraction, algorithm and procedures, automation, simulation, and parallelization. We discussed each component with operational definition to check if those components were useful in and applicable for science programs. We employed this CTPF into two different topics of STEAM programs to see if those components were observable with operational definitions. The profile of CT components within the selected STEAM programs for this study showed one sequential spectrum covering from data collection to simulation as the grade level went higher. The first three data related CT components were dominating at elementary level, all components of CT except parallelization were found at middle school level, and finally more frequencies in every component of CT except parallelization were also found at high school level than middle school level. On the basis of the result of CT usage in STEAM programs, we included 'generalization' in CTPF of science education instead of 'parallelization' which was not found. The implication about teacher education was made based on the CTPF in terms of science education.

• Journal of The Korean Association For Science Education
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• v.11 no.2
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• pp.133-142
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• 1991

The purpose of this study is to develop a data base system for the information on research and development of science education. As the first step of this study and development, a classification system for the research and development materials was devised after discussing the process of science education and the research and development of science education. The classification system has nine main categories : 1. area, 2. subject, 3. behavior, 4. skill, 5. support, 6. type, 7. materials, 8. language, and 9. the others, each of which has one or two levels of subcategory. This classification system was revised and supplemented through the theoretical analysis by speci.diSts and the practical classification of master's theses and doctoral dissertations from the Department of Science Education, Seoul National University. But it still needs more revision and enlargement through the continuous application and analysis.

• Journal of Practical Engineering Education
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• v.13 no.3
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• pp.435-442
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• 2021

In this study, a program was developed to educate the students and athletes of the school sports team on the overall knowledge of using sports data. Accordingly, existing research and requirements for using sports data were analyzed, a learning plan was designed, and an education program was developed in a step-by-step manner according to the educational requirements. In addition, as there is no research yet on data science education for school athletics and adult sports officials in existing studies, this study includes the problem definition, data collection, data pre-processing, and data analysis, as well as the additional stages of data visualization and simulation analysis. It is expected that the sports industry's interest in sports data will increase through this study.

• Communications of Mathematical Education
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• v.19 no.4 s.24
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• pp.805-822
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• 2005

In Korea, many local university mathematics faculty knew that the institution faced serious student shortage problems and the restructuring and cut actions for such a mathematics major programs. In general, undergraduate mathematics education in korea is in the crisis. In general, lots of mathematics departments in korea was not prepared for such a severe risk. In this article, university mathematics education and research business are studied in the context of the size of korea-usa population, economy(such as GDP), SCI indices. Korea-usa university mathematics education profile data are presented to compare korea-usa university mathematics education business. Lots of precious data on mathematics education are being helped to prepare for the university mathematics education crisis.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.41-47
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• 2012

Data mining and knowledge discovery techniques have shown to be effective in finding hidden underlying rules inside large database in an automated fashion. On the other hand, analyzing, assessing, and applying students' survey data are very important in science and engineering education because of various reasons such as quality improvement, engineering design process, innovative education, etc. Among those surveys, analyzing the students' views on science-technology-society can be helpful to engineering education. Because, although most researches on the philosophy of science have shown that science is one of the most difficult concepts to define precisely, it is still important to have an eye on science, pseudo-science, and scientific misconducts. In this paper, we report the experimental results of applying decision tree induction algorithms for analyzing the questionnaire results of high school students' views on science-technology-society (HS-VOSTS). Empirical results on various settings of decision tree induction on HS-VOSTS results from one South Korean university students indicate that decision tree induction algorithms can be successfully and effectively applied to automated knowledge discovery from students' survey data.

• Journal of The Korean Association For Science Education
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• v.11 no.2
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• pp.143-159
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• 1991

The purpose of this study is to develop a data base system for the infromation on research and development of science education. As a part of this development, an analysis of master's theses and doctoral dissertations from the Department of Science Education, Seoul National University was done, and using authors' classification system for the research and development materials of science education, those theses were classified. From the analysis and classification, the following conclusion was drawn: 1) The Department of Science Education, Seoul National University had produced 468 masters of education for about 30 years. Among them, only 107 theses were on the science education and the other 361 theses on the pure science. This means that department has not taken root as a department of science education. If it does not carry out its own purpose of establishment, it will not be able to justify its existence any longer 2) As compared to the increased number of students applying for the doctoral program, the number of faculty is very few in the field of science education. Without more supplement of the faculty member majored science education, there will be increased conflict and disorder among faculty and students. 3) The proportion of the theses on science education to those on science vanes greatly by the major of the department. This is the mirror of the faculty attitude to and recognition of science education and the faculty composition. 4) The classification of master theses and doctoral dissertations on science education showed that most of them focused on the secondary school science education and were survey studies. But recently it is noticeable that the theme of the study became diversified and has kept in step with the international research trends.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.81-94
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• 2024

This study explores and conducts an in-depth comparison of the epistemic characteristics in different data types utilized in the science inquiries of preservice teachers regarding noise as a risk in everyday life. Focusing on primary and secondary data in the context of science inquiries about noise, we examined how these data types differ in science inquires in terms of inquiry design, data collection, and analyses. The findings reveal that sensor-based primary data enable direct measurement and observation of key phenomena. Conversely, secondary data rely on predetermined measurement methods within a public data system. These differences require different epistemic considerations during the inquiry process. Based on these findings, we discuss the educational implications concerning teaching approaches for science inquiries, teacher education for inquiry teaching, and the development of risk response competencies in preparation for the VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) era.