• Educational Technology International
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• v.21 no.2
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• pp.193-216
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• 2020

This study investigated university students' perspectives on good class and instructional practices through social network analysis. The subjects were 321 students in the third and fourth academic years in a Korean university. The subjects completed four open-ended questions, asking about experience of good class, good instructors' teaching practice, and their feelings and attitudes when participating in good class. As social network analysis, KrKwic (Korea Key Words in Context) was used to compute word frequencies and analyze semantic network structures and Ucinet Netdraw to assess centrality in the social network, consisting of degree centrality, closeness centrality, and between centrality. The results are as follows. First, students showed 5 keywords to depict what good class is, including 'understanding', 'example', 'video', 'interest', and 'communication'. Second, the characteristics of teaching methods by professors who practice good class indicate 'assignments', 'questions', 'understanding', 'example', and 'feedback'. Third, the top 5 keywords of students' attitudes as participating in good class are 'active', 'participation', 'focus', 'listening', and 'asking'. Last, keywords depicting desirable class that students most wanted to take next time are 'assignments', 'rewards', 'understanding', 'difficulty', and 'interest'. The findings from this study include the meanings of the semantic network structures of words in the text making up messages. Also this study can provide empirical evidence for educators and educational practitioners in higher education to create effective learning environments.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.39-46
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• 2023

The purpose of this study is to design and develop the test standard to measure AI literacy abilities. First, we selected key areas of AI literacy through the related studies and expert FGI and designed detailed standard. The area of the test standard is divided into three categories: AI concept, practice, and impact. In order to confirm the validity of the test standard, we conducted twice expert validity tests and then modified and supplemented the test index. To confirm the validity of the test standard, we conducted an expert validity test twice and then modified and supplemented the test standard. The final AI literacy test standard consisted of a total of 30 questions. The AI literacy test standard developed in this study can be an important tool for developing self-checklists or AI competency test questions for measuring AI literacy ability.

• YAKHAK HOEJI
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• v.56 no.5
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• pp.333-345
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• 2012

A product labelling is one of key tools in ensuring that a patient uses drugs safely and effectively in self-care without professional support. This study aimed to explore the readability and comprehensibility of the information contained on two package inserts of medicines. Two package inserts were tested with first year college students. Fifty-one potential consumers underlined words they could not understand and answered 10 scenario questions. Any differences among groups with different characteristics were statistically tested. Secondly, the readability of two package inserts was assessed with comparison to the level of the 6th grade Korean textbooks. As results, more than 80% of participants properly replied to straightforward questions concerning indication, dosage, duplication, use in pregnancy and contraindication, and 73% about formulation. Less than half answered correctly in multiple choice questions about pediatric use (41%) and side effects (35%). Little discrepancy was observed in the comprehensibility between participants' characteristics. Drug inserts contained about 20% more professional-level words than 6th grade textbooks. In conclusion, Korean consumers may face challenges to understand drug information due to professional terminology and outdated expressions in the current package inserts. To secure safe and effective use of over-the-counter agents, greater efforts should be made to develop more consumer friendly labels. In the other hand, educational supports are required to prepare consumers in a proper level of knowledge for the safe use of drugs.

• Human Ecology Research
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• v.60 no.4
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• pp.625-643
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• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.173-185
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• 2019

Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.551-560
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• 2020

Due to the nature of fashion design that responds quickly and sensitively to changes, accurate forecasting for upcoming fashion trends is an important factor in the performance of fashion product planning. This study analyzed the major phenomena of fashion trends by introducing text mining and a big data analysis method. The research questions were as follows. What is the key term of the 2010SS~2019FW fashion trend? What are the terms that are highly relevant to the key trend term by year? Which terms relevant to the key trend term has shown high frequency in news articles during the same period? Data were collected through the 2010SS~2019FW Pre-Trend data from the leading trend information company in Korea and 45,038 articles searched by "fashion+material" from the News Big Data System. Frequency, correlation coefficient, coefficient of variation and mapping were performed using R-3.5.1. Results showed that the fashion trend information were reflected in the consumer market. The term with the highest frequency in 2010SS~2019FW fashion trend information was material. In trend information, the terms most relevant to material were comfort, compact, look, casual, blend, functional, cotton, processing, metal and functional by year. In the news article, functional, comfort, sports, leather, casual, eco-friendly, classic, padding, culture, and high-quality showed the high frequency. Functional was the only fashion material term derived every year for 10 years. This study helps expand the scope and methods of fashion design research as well as improves the information analysis and forecasting capabilities of the fashion industry.

• Journal of Elementary Mathematics Education in Korea
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• v.21 no.2
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• pp.263-289
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• 2017

New mathematics textbooks for elementary school students are under development according to the 2015 national revised curriculum. Not only contents but also framework of textbooks may be interesting to the mathematics educators and researchers. Considering the high dependency on textbooks in elementary classrooms, the influence of the framework of textbooks in mathematics learning cannot be overlooked. The unit and lesson frameworks of the textbook are important because they are directly related to the quality of mathematic lessons, especially when teachers make a lesson plan based on the unit and lesson frameworks of the textbook. This study is to analyse the unit and lesson frameworks of elementary school mathematics textbooks and to find out elementary school teachers' preference about its analysed key points. For longitudinal analysis, we selected 3rd-grade mathematics textbooks of 5th, 6th, 7th, the 2007, and the 2009 national revised curriculums. For horizontal analysis, we selected 3rd-grade mathematics textbooks of Korea, Japan, United States and Finland. We compared unit and lesson frameworks of various textbooks, and abstracted key elements of the textbook frameworks, and constructed survey questions. Looking at results from survey questions based on analysed key points, we were able to grasp the teachers' preference for unit and lesson frameworks for mathematics textbook. Based on the results of this study, some implications for the development of framework for new mathematics textbooks are suggested.

• Knowledge Management Research
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• v.18 no.4
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• pp.181-211
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• 2017

Despite a growing literature focusing on technological development as a key driving force behind the economic performance of a firm or a nation's industy, we still fall short of a comprehensive understanding of how each of the elements required of technological developent and innovation fits togther and leads to economic progress and industry change. This paper seeks to fill this gap by bringing together some of key insights from the theory and research on the coevolutionary process of technology, organizations, and industry, and on the role of institutions in this process. By combining a diverse array of research streams, we provide a broad suvey of foundational work on the following two questions: (1) how the creation and diffusion of innovation occurs and gives rise to structural reconfigurations of the industry, (2) how organisations and technology coevolve, and (3) what is the role of institutions in this coevolutionary process? Based on this literature survey, we also offer a synthesis that can serve as a ground that allows a more nuanced understanding of the sources, dynamics and impacts of technological development and innovation, and interrelationships among technology, organizations and industry change.

• Molecules and Cells
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• v.44 no.2
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• pp.63-67
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• 2021

The bed nucleus of the stria terminalis (BNST)-a key part of the extended amygdala-has been implicated in the regulation of diverse behavioral states, ranging from anxiety and reward processing to feeding behavior. Among the host of distinct types of neurons within the BNST, recent investigations employing cell type- and projection-specific circuit dissection techniques (such as optogenetics, chemogenetics, deep-brain calcium imaging, and the genetic and viral methods for targeting specific types of cells) have highlighted the key roles of glutamatergic and GABAergic neurons and their axonal projections. As anticipated from their primary roles in excitatory and inhibitory neurotransmission, these studies established that the glutamatergic and GABAergic subpopulations of the BNST oppositely regulate diverse behavioral states. At the same time, these studies have also revealed unexpected functional specificity and heterogeneity within each subpopulation. In this Minireview, we introduce the body of studies that investigated the function of glutamatergic and GABAergic BNST neurons and their circuits. We also discuss unresolved questions and future directions for a more complete understanding of the cellular diversity and functional heterogeneity within the BNST.

• Educational Technology International
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• v.16 no.2
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• pp.183-200
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• 2015

This study investigated the effect of learning achievements and cognitive load according to different types of presenting learning materials and epistemological beliefs (EB). Learning achievements in this study were composed by retention and transfer of ill-structured problem. A total of 80 college students participated in the study. Prior to the learning, students were guided to fill out a questionnaire regarding epistemological beliefs and a prior knowledge test. The students of each group studied with a different type of reading material: full text (FT), full text including key questions (KeyFT) and full text including a concept map (CmFT). After a session of study was finished, they were asked to complete the posttest: retention and transfer. The results showed that there was a significant difference in transfer achievements. CmFT outperformed higher scores than the other types. There was no significant difference in retention among the groups. It is strongly believed that the types of presenting learning materials may have affected the understanding of ill-structured problem solving skills. Students with sophisticated EB showed higher achievements on retention and transfer than naive-EB and mixed-EB. Even though the data showed decrease of the cognitive load on the type of materials and EB, there were no significant differences on the cognitive load. We should consider a positive effect of types of presenting learning materials and EB enhancing capabilities of solving ill-structured problems in real life.