• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.207-212
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• 2024

The importance of hydrogen fuel cells continues to be emphasized, and there is a growing demand for education and training in this field. Among various educational environments, metaverse education is opening a new era of change in the global education industry, especially to adapt to remote learning. The most significant change that the metaverse has brought to education is the shift from one-way, instructor-centered, and static teaching approaches to multi-directional and dynamic ones. It is expected that the metaverse can be effectively utilized in hydrogen fuel cell engineer education, not only enhancing the effectiveness of education by enabling learning and training anytime, anywhere but also reducing costs associated with engineering education.In this research, inspired by these ideas, we are designing a fuel cell education platform. We have created a platform that combines theoretical and practical training using the metaverse. Key aspects of this research include the development of educational training content to increase learner engagement, the configuration of user interfaces for improved usability, the creation of environments for interacting with objects in the virtual world, and support for convergence services in the form of digital twins.

• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.467-480
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• 2018

This study aims to investigate how flipped learning-based socioscientific issue instruction (FL-SSI instruction) affected middle school students' key competencies and character development. Traditional classrooms are constrained in terms of time and resources for exploring the issues and making decision on SSI. To address these concerns, we designed and implemented an SSI instruction adopting flipped learning. Seventy-three 8th graders participated in an SSI program on four topics for over 12 class periods. Two questionnaires were used as a main data source to measure students' key competencies and character development before and after the SSI instruction. In addition, student responses and shared experience from focus group interviews after the instruction were collected and analyzed. The results indicate that the students significantly improved their key competencies and experienced character development after the SSI instruction. The students presented statistically significant improvement in the key competencies (i.e., collaboration, information and technology, critical thinking and problem-solving, and communication skills) and in two out of three factors in character and values as global citizens (social and moral compassion, and socio-scientific accountability). Interview data supports the quantitative results indicating that SSI instruction with a flipped learning strategy provided students in-depth and rich learning opportunities. The students responded that watching web-based videos prior to class enabled them to deeply understand the issue and actively engage in discussion and debate once class began. Furthermore, the resulting gains in available class time deriving from a flipped learning approach allowed the students to examine the issue from diverse perspectives.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.129-130
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• 2018

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Artificial Intelligence" keyword, one month as of May 19, 2018. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Artificial Intelligence" has been found to be technology (4,122). This study suggests theoretical implications based on the results.

• Hwankyungkyoyuk
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• v.14 no.2
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• pp.15-27
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• 2001

The purpose of this study is to understand and compare the characteristics of the content organization system of three Environment textbooks currently used, and to examine its connectivity with the teaching & teaming methods included in the Environment subject part of the 7th Korean National Curriculum. For the analysis three Environment textbooks for middle school and their teacher's guide books by three companies. published(A, B, and C) The result of the taxonomic analysis showed that three Environment books had different steps to get to the lesson class unit in the way that A of six steps, B of five steps and C of seven steps. The amount of main text was different In the domains of'Human and Environment','Environmental Problems and its Counter-plan'and'Environmental Conservation'of three textbooks each. All of three textbooks had the biggest percentage in sub-domains of'Living Environment to Keep'and'Global Environmental Problem'in 'Environmental Problems and its Counter-plan'domain. Considering teaching & loaming methods all of three textbooks contained many activities as 55 in A, 66 in H and 91 in C. Among 9 teaching 8E teaming methods and others listed in the Environment subject part of the 7th Korean National Curriculum, the investigation method is most frequently used in all of three textbooks. The drama, the paly and the case study were used rarely as teaching & teaming methods in activities In the consideration of the content amount regarding academic fields, it was revealed that three textbooks overemphasized the aspect of natural sciences comparing the aspect of human & social sciences aspect as a whole. Generally the appendix section of all three textbooks were well organized to support the teaching and teaming activities in main text.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.146-147
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• 2017

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Internet of things" keyword, one month as of october 8, 2017. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Internet of things" has been found to be technology (995). This study suggests theoretical implications based on the results.

• Knowledge Management Research
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• v.14 no.2
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• pp.137-154
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• 2013

In an ever-tougher competitive environment caused by globalization, domestic companies are increasingly adopting business strategies aimed at continuously securing competitive advantage by taking advantage of globally-competitive foreign professionals. Despite a persistent rise of such trend, domestic researches regard migrant workers as the socially underprivileged, and delve into the issue of migrant workers from the policy, welfare, and ethical perspectives. With a growing need to deal with migrant professionals from the strategic viewpoint - to acquire professional talent in an era of global competition, it becomes essential to verify the real effectiveness of migrant professionals. Yet, there has been relatively little discussion of it. This study assumes that based on th137e integration-learning perspective on diversity, the greater the number of foreign professionals, the greater the effect on organizational innovative performance. Also could be effective in managing diversity is diversity-friendly HRM which involves eliminating discrimination against migrant professional workers and treating them fairly. Based on the data collected from 72 domestic companies, this study conducted an empirical analysis of the impact of the percentage of foreign professionals in the total workforce on organizational innovative performance and of the moderating role of diversity-friendly HRM. The results show that the proportion of foreign professionals in the entire workforce has had no significant impact on organizational innovative performance, and that the proportion of foreign professionals in the total workforce and diversity-friendly HRM have had a interaction effect on organizational innovative performance. Based on these research results, the study attempted to interpret the significance of the proportion that migrant professionals make up of the total workforce and of diversity-friendly HRM in relation to organizational innovative performance, and their implications for diversity management.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.177-185
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• 2019

The purpose of this study is to investigate the causes of career problems, which are the biggest problems of Korean university students, and to solve them by using case studies of domestic and global universities, I would like to suggest a career advisor system model for college students. It is most important to collect advice and learning data to solve the career problems of college students by utilizing information technology such as data analysis and AI. Research has not been actively pursued because the university has very limited internal data to advise on career problems. In this paper, we study the data types and methods of college students' career advice, and propose a career advisor counseling system for college students.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.1
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• pp.16-28
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• 2021

Purpose: The purpose of this study was to develop nurses' core competencies and sub-competencies and to verify the validity and importance-performance of core competencies. Methods: The core competencies of nurses were derived through an analysis of strengths, weaknesses, opportunities, and threats, as well as a literature analysis of domestic and foreign accreditation institutions. Validity and importance-performance analyses were conducted on the core competencies derived from nursing colleges nationwide. Results: Six core competencies of nurses were revealed: integration of knowledge and nursing skills, critical thinking, communication, leadership, safety management, and global competency. Further, eighteen sub-competencies were derived. The content validity ratio values for the core competencies were higher than 0.74. Communication skills among multidisciplinary teams and communication skills among nursing teams were shown to be the most important competencies to be improved. Conclusion: The results of this study are meaningful in terms of how the core competencies of nurses were derived and evaluated for the fourth cycle of nursing education accreditation according to the changes of time and culture.

• Journal of Environmental Health Sciences
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• v.48 no.2
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• pp.96-105
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• 2022

Background: Pyrethroid insecticides account for more than 30% of the global insecticide market and are frequently used in agricultural settings and residential and public pest control among the general population. While several animal studies have suggested that exposure to pyrethroids can alter glucose homeostasis, there is only limited evidence of the association between environmental pyrethroid exposure and diabetes in humans. Objectives: This study aimed to report environmental 3-phenoxybenzoic acid (3-PBA) concentrations in urine and evaluate its association with the risk of diabetes in Korean adults. Methods: We analyzed data from the Korean National Environmental Health Survey (KoNEHS) Cycle 2 (2012~2014) and Cycle 3 (2015~2017). A total of 10,123 participants aged ≥19 years were included. Multiple logistic regressions were used to calculate the odds ratios (ORs) for diabetes according to log-transformed urinary 3-PBA levels. We also evaluated age, sex, education, monthly income, marital status, alcohol drinking, physical activity, urinary cotinine, body mass index, and sampling season as potential effect modifiers of these associations. Results: After adjusting for all the covariates, we found significant dose-response relationships between urinary 3-PBA as quartile and the prevalence of diabetes in pooled data of KoNEHS Cycles 2 and 3. In subgroup analyses, the adverse effects of pyrethroid exposure on diabetes were significantly stronger among those aged 19~39 years (p-interaction<0.001) and those who consumed high levels of cotinine (p-interaction=0.020). Conclusions: Our findings highlight the potential diabetes risk of environmental exposure to pyrethroids and should be confirmed in large prospective studies in different populations in the future.