• Journal of The Korean Association of Information Education
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• v.26 no.6
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• pp.481-490
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• 2022

With the development of computing technology, the big data era has arrived, and we live with a lot of data around us. Elementary school students are no exception. Therefore, it is very important to learn to process data from elementary school. Since elementary school students have intuitive thinking, data visualization, which expresses data directly in pictures, is an important learning element. In this study, we study how effective elementary school students can visualize data in their daily lives to improve their information processing capabilities. Adata visualization program was developed by organizing and visualizing data using data visualization tools for the 8th class, which can be done by third graders in elementary school, and then experiencing the process of interaction. As a result of applying the developed program to 186 students in 7 classes, knowledge information processing competency factors were evaluated before and after class. As a result of the pre- and post-test, there was a significant difference in knowledge information processing capabilities. Therefore, the data visualization program developed in this study is effective.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.31-39
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• 2020

Big data analysis is analyzed and used in a variety of management and industrial sites, and plays an important role in management decision making. The job competency of big data analysis personnel engaged in management analysis work does not necessarily require the acquisition of microscopic IT skills, but requires a variety of experiences and humanities knowledge and analytical skills as a Data Scientist. However, big data education by state-run and state-run educational institutions and job education institutions based on the National Competency Standards (NCS) is proceeding in terms of software engineering, and this teaching methodology can have difficult and inefficient consequences for non-technical majors. Therefore, we analyzed the current Big Data platform and its related technologies and defined which of them are the requisite job competency requirements for field personnel. Based on this, the education courses for big data analysis and visualization techniques were organized for non-technical-based majors. This specialized curriculum was conducted by working-level officials of financial institutions engaged in management analysis at the management site and was able to achieve better educational effects The education methods presented in this study will effectively carry out big data tasks across industries and encourage visualization of big data analysis for non-technical professionals.

• Journal of the Korean Society of Earth Science Education
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• v.17 no.2
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• pp.123-136
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• 2024

This study examines the performance expectations (PEs) and clarification statements of each PE in the 2022 revised national science and mathematics education standards from a data visualization competency perspective. First, the authors intensively reviewed data visualization literature to define key competencies and developed a framework comprising four main categories: collection and pre-processing skills, technical skills, thinking skills, and interaction skills. Based on the framework, the authors extracted a total of 191 mathematics and 230 science PEs from the 2022 revised science and mathematics education standards (Ministry of Education Ordinance No. 2022-33, Volumes 8 and 9) as the main data set. The analysis process consisted of three steps: first, the authors organized the data (421 PEs) by the four categories of the framework and four grade levels (3-4th, 5-6th, 7-9th, and 10th grade); second, the numbers of PEs in each grade level were standardized by the accomplishing period (1-3 years) of each PE depending on the grade level; lastly, the data set was represented by heatmaps to visualize the relationship between the four categories of visualization competency and four grade levels, and the differences between the competency categories and grade levels were quantitatively analyzed using the Mann-Whitney U test and independent sample Kruskal-Wallis tests. The analysis results revealed that in mathematics, there was no significant difference between the number of PEs by grade. However, on average, the number of PEs categorized in 'thinking skills' was significantly lower than those in the technical skills (p = .002) and interaction skills categories (p = .001). In science, it was observed that as grade level increased, PEs also increased (pairwise comparison: Grades 5-6 vs. 7-9, p = .001; Grades 5-6 vs. Grade 10, p = .029; Grades 3-4 vs. 7-9, p = .022). Particularly, the frequency of PEs in 'thinking skills' was significantly lower than in the other skills (pairwise comparison: technical skills p = .024; collection and pre-processing skills p = .012; interaction skills p = .010). Based on the results, two implications for revising national science and mathematics standards and teacher education were suggested.

• The Journal of Industrial Distribution & Business
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• v.12 no.8
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• pp.21-32
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• 2021

Purpose: With the advent of the era of the 4th industrial revolution, called a new technological revolution, the necessity of fostering future talents equipped with AI utilization capabilities is emerging. However, there is a lack of research on AI education design and competency-based education curriculum as education for business major. The purpose of this study is to design AI education to cultivate competency-oriented AI literacy for business major in universities. Research design, data and methodology: For the design of AI basic education in business major, three expert Delphi surveys were conducted, and a demand analysis and specialization strategy were established, and the reliability of the derived design contents was verified by reflecting the results. Results: As a result, the main competencies for cultivating AI literacy were data literacy, AI understanding and utilization, and the main detailed areas derived from this were data structure understanding and processing, visualization, web scraping, web crawling, public data utilization, and concept of machine learning and application. Conclusions: The educational design content derived through this study is expected to help establish the direction of competency-centered AI education in the future and increase the necessity and value of AI education by utilizing it based on the major field.

• Journal of Industrial Convergence
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• v.18 no.2
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• pp.45-50
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• 2020

The purpose of this study is to analyze relationships among essential keywords in curriculum. The number of 1,935 keyword was collected from 644 manuscripts published between 2002 and 2019. For data analysis, this study selected softwares of KrKwic and KrTitle to compose a 1-mode network matrix and UCINET 6 and NetDraw to implement network analysis and visualization. Results are as follows. First, the frequency of keyword was curriculum, curriculum development, national curriculum, competency-based curriculum, 2015 revised national curriculum, curriculum implementation, understanding by design, competency, teacher education, school curriculum, and IBDP from highest to lowest. Second, degree centrality was curriculum development, curriculum, competency-based curriculum, national curriculum, 2015 revised national curriculum, understanding by design, competency, key competency, high school curriculum, textbook, curriculum implementation, teacher education, and IBDP from highest to lowest.

• Journal of the Korean Society for information Management
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• v.35 no.4
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• pp.7-36
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• 2018

This study aimed to analyze the content of Records Data Management (RDM) training programs provided by 51 out of 121 university libraries in North America that implemented RDM services, and to provide implications from the results. For the content analysis, 317 titles of classroom training programs and 42 headings at the highest level from the tables of content of online tutorials were collected and coded based on 12 data literacy competencies identified from previous studies. Among classroom training programs, those regarding data processing and analysis competency were offered the most. The highest number of the libraries provided classroom training programs in relation to data management and organization competency. The third most classroom training programs dealt with data visualization and representation competency. However, each of the remaining 9 competencies was covered by only a few classroom training programs, and this implied that classroom training programs focused on the particular data literacy competencies. There were five university libraries that developed and provided their own online tutorials. The analysis of the headings showed that the competencies of data preservation, ethics and data citation, and data management and organization were mainly covered and the difference existed in the competencies stressed by the classroom training programs. For effective RDM training program, it is necessary to understand and support the education of data literacy competencies that researchers need to draw research results, in addition to competencies that university librarians traditionally have taught and emphasized. It is also needed to develop educational resources that support continuing education for the librarians involved in RDM services.

• Smart Media Journal
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• v.8 no.1
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• pp.67-73
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• 2019

This study investigates Active Learning and Facilitation Convergence Education Program which can improve core competency to cope with vocational education in the fourth industrial revolution era. I applied the integrated advantages of Active Learning which enhances 'problem solving skill' and those of Facilitation for creative thinking idea to application design process coursework and verified the effectiveness of such education method through student satisfaction survey. I also designed application contents for the students who are familiar with the mobile environments and UI contents for data visualization which can help those students to improve their skills in software. Every coursework was conducted as a team project. As a result, Active Learning and Facilitation Convergence Education Program is found to be helpful in improving the basic skills and competencies required in college education. I hope this work helps to reduce the educational gap between industry and professional colleges.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.107-116
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• 2018

One of the key mission is to develop and prompte the use National Competency Standards, which is defined to be the systemization of competencies(knowledge, skills and attitudes) required to perform duties at the workplace by the nation for each industrial sector and level. This provides the basis for the design of training and detailed specifications for workplace assessment. To promote the data-driven service improvement, the commercial product Splunk was introduced, and has grown to become an extremely useful platform because it enables the users to search, collect, and organize data in a far more comprehensive, far less labor-intensive way than traditional databases. Leveraging Splunk's built-in data visualization and analytical features, HRD Korea have built custom tools to gain new insight and operational intelligence that organizations have never had before. This paper analyzes the NCS homepage. Concretely, applying Splunk in creating visualizations, dashboards and performing various functional and statistical analysis and structure without Web development skills. We presented practical use and implications through case studies.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.201-207
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• 2021

As artificial intelligence gradually expands into jobs, iIt is necessary to nurture talents with AI literacy capabilities required for non-majors. Therefore, in this study, based on the necessity and current status of AI education, AI literacy competency improvement education was conducted for non-majors so that AI learning could be sustainable in relation to future majors. For non-majors at University D, problem-solving solutions through project-based data analysis and visualization were applied over 15 weeks, and the AI ability improvement and effectiveness of learners before and after education were analyzed and verified. As a result, it was possible to confirm a statistically significant level of positive change in the learners' data analysis and utilization ability, AI literacy ability, and AI self-efficacy. In particular, it not only improved the learners' ability to directly utilize public data to analyze and visualize it, but also improved their self-efficacy to solve problems by linking this with the use of AI.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.173-191
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• 2024

Typhoons are significant meteorological phenomena that cause interactions among the ocean, atmosphere, and land within Earth's system. In particular, wind speed, a key characteristic of typhoons, is influenced by various factors such as central pressure, trajectory, and sea surface temperature. Therefore, a comprehensive understanding based on actual observational data is essential. In the 2015 revised secondary school textbooks, typhoon wind speed is presented through text and illustrations; hence, exploratory activities that promote a deeper understanding of wind speed are necessary. In this study, we developed a data visualization program with a graphical user interface (GUI) to facilitate the understanding of typhoon wind speeds with simple operations during the teaching-learning process. The program utilizes red-green-blue (RGB) image data of Typhoons Mawar, Guchol, and Bolaven -which occurred in 2023- from the Korean geostationary satellite GEO-KOMPSAT-2A (GK-2A) as the input data. The program is designed to calculate typhoon wind speeds by inputting cloud movement coordinates around the typhoon and visualizes the wind speed distribution by inputting parameters such as central pressure, storm radius, and maximum wind speed. The GUI-based program developed in this study can be applied to typhoons observed by GK-2A without errors and enables scientific exploration based on actual observations beyond the limitations of textbooks. This allows students and teachers to collect, process, analyze, and visualize real observational data without needing a paid program or professional coding knowledge. This approach is expected to foster digital literacy, an essential competency for the future.