• Knowledge Management Research
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• v.24 no.1
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• pp.1-29
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• 2023

The COVID-19 pandemic has accelerated digital transformation across all industries and daily life. Edutech is spreading in the education field, also bringing changes in university education. Non-face-to-face online-only classes at universities have spread after the COVID-19 pandemic physical distancing started. Online-only or real-time online classes showed diverse educational imitations. 'Metaverse' started to attract attention as a learning space and community activity support platform that may solve the limitations of online education and communication. It is time to prepare an introduction strategy for the actual application of education using metaverse. This study, first, by examining previous studies and cases of metaverse application, and second, establishing a metaverse introduction framework based on the technology lifecycle model and the innovation diffusion theory. Finally, we provide an introduction strategy in steps, a specialized introduction plan according to the main users is established and presented as a scenario. We expect that this study will provide the theoretical background of the new technology introduction and the spread of metaverse research. Also, we present an efficient introduction strategy, the basis for a service model, and a practical basis for the university's value-added strategy.

• Journal of the Korean GEO-environmental Society
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• v.23 no.4
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• pp.5-10
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• 2022

Recently, ground subsidence has been continuously occurring in downtown areas, threatening the safety of citizens. Various underground facilities such as water and sewage pipelines and communication pipelines are buried under the road. It is reported that the cause of ground subsidence is the deterioration of various facilities and the reckless development of the underground. In particular, it is known that the biggest cause of ground subsidence is the aging of sewage pipelines. As an existing study related to this, several representative factors of sewage pipelines were selected and a study to predict the risk of ground subsidence through statistical analysis has been conducted. In this study, a data SET was constructed using the characteristics of OO city's sewage pipe characteristics and ground subsidence data, The data set constructed from the characteristics of the sewage pipe of OO city and the location of the ground subsidence was used. The goal of this study was to present a classification model for the occurrence of ground subsidence according to the characteristics of sewage pipes through machine learning. In addition, the importance of each sewage pipe characteristic affecting the ground subsidence was calculated.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.35-56
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• 2023

With the development of information and communication technology, numerous reviews are continuously posted on websites, which causes information overload problems. Therefore, users face difficulty in exploring reviews for their decision-making. To solve such a problem, many studies on review helpfulness prediction have been actively conducted to provide users with helpful and reliable reviews. Existing studies predict review helpfulness mainly based on the features included in the review. However, such studies disable providing the reason why predicted reviews are helpful. Therefore, this study aims to propose a methodology for applying eXplainable Artificial Intelligence (XAI) techniques in review helpfulness prediction to address such a limitation. This study uses restaurant reviews collected from Yelp.com to compare the prediction performance of six models widely used in previous studies. Next, we propose an explainable review helpfulness prediction model by applying the XAI technique to the model with the best prediction performance. Therefore, the methodology proposed in this study can recommend helpful reviews in the user's purchasing decision-making process and provide the interpretation of why such predicted reviews are helpful.

• Korean Medical Education Review
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• v.25 no.2
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• pp.174-183
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• 2023

Educational evaluation involves data collection and the analysis of various education-related factors to make decisions that improve educational quality. Systematic educational evaluation is essential for enhancing the quality of education. This study reports a case of student-conducted process evaluation of a medical school's student support system and the procedure for devising improvement plans. Sixteen Inje University College of Medicine students participated in the Education Evaluation Committee (IUCM-EEC) to understand the educational improvement process as learners and actively achieve improvement. The Quality Improvement Committee of the Inje University College of Medicine (IUCM-QIC) decided to reform its student support system based on a previous educational evaluation in 2019. The evaluation of the student support system was conducted for 10 months in 2021 by the student subcommittee, under the guidance of the IUCM-EEC. The CIPP (context-input-process-product) evaluation model was used for a systematic evaluation. Accordingly, the subcommittee developed evaluation criteria and indicators, and analyzed relevant data collected from surveys and the previous literature. For further recommendations and revision ideas, the student subcommittee members interviewed faculty members from six other medical schools and also conducted a focus group interview with the dean and vice deans of IUCM. Finally, the student subcommittee submitted a report to the IUCM-QIC. Communication with various stakeholders is essential for a successful evaluation process. In this case, students, as key stakeholders in education, evaluated the student support system. Their active participation helped improve their understanding of the evaluation process.

• Korean Journal of Culture and Social Issue
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• v.29 no.4
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• pp.429-453
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• 2023

Due to recent demographic changes, employees from diverse generations now work together in organizations. Thus, there is a need for research on intergenerational cooperation. However, the lack of valid and reliable measures to capture intergenerational climate in the workplace is an obstacle to research. Therefore, we translated the Workplace Intergenerational Climate Scale(WICS) into Korean and validated it with a sample of 1,052 Korean full-time employees. Firstly, we conducted an exploratory factor analysis by using sample 1(N = 460) and revealed a five-factor solution. Secondly, the confirmatory factor analysis(sample 2; N = 592) showed a good model fit of the correlated five-factor model. Thirdly, the scale's discriminant and convergent validity was supported by negative correlations with four types of existing ageism scales and by positive correlations with trust, organizational commitment, work engagement, psychological safety, intention to remain, job satisfaction, and communication satisfaction. Moreover, it further demonstrated significant incremental validity in predicting positive outcome variables even when controlling for pre-existing agism scales. Lastly, we confirmed strict measurement invariance of the scale between the age groups(below 40 versus above 40). The findings support the reliability and validity of the Korean version of WICS among Korean employees. The scale will be broadly applied to measure intergenerational climate of organizations and provide practical implications for HR management.

• Information Systems Review
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• v.24 no.1
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• pp.1-19
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• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• Journal of The Korean Association For Science Education
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• v.27 no.9
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• pp.907-918
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• 2007

This study was conducted to explore key factors of expertise development of talented scientists who achieved outstanding research performance according to the stages of expertise development and dimensions of individual-domain-field. To fulfill the research purpose, 31 domestic scientists who were awarded major prizes in the field of science were interviewed in-depth from March to September, 2007. Stages of expertise development were analyzed in light of Csikszentmihalyi's IDFI (individual-domain-field interaction) model. Self-directed learning, multiple interests and finding strength, academic and liberal home environment, and meaningful encounter were major factors affecting expertise development in the exploration stage. In the beginner stage, independence, basic knowledge on major, and thirst for knowledge at university affected expertise development. Task commitment, finding flow, finding their field of interest and lifelong research topic, and mentor in formal education were the affecting factors in the competent stage. Finally, placing priority, communication skills, pioneering new domain, expansion of the domain, and evaluation and support system affected talented scientists' expertise development in the leading stage. The meaning of major patterns of expertise development were analyzed and described. Based on these analyses, educational implications for nurturing scientists were suggested.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.91-98
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• 2024

With the advancement of ICT (Information and Communication Technology), searching for judgments through the internet has become increasingly convenient. However, predicting sentencing based on judgments remains a challenging task for individuals. This is because sentencing involves a complex process of applying aggravating and mitigating factors within the framework of legal provisions, and it often depends on the subjective judgment of the judge. Therefore, this research aimed to develop a model for predicting sentencing using artificial intelligence by focusing on structuring the data from judgments, making it suitable for AI applications. Through theoretical and statistical analysis of previous studies, we identified variables with high explanatory power for predicting sentencing. Additionally, by analyzing 50 legal judgments related to serious crimes that are publicly available, we presented a framework for extracting essential information from judgments. This framework encompasses basic case information, sentencing details, reasons for sentencing, the reasons for the determination of the sentence, as well as information about offenders, victims, and accomplices evident within the specific content of the judgments. This research is expected to contribute to the development of artificial intelligence technologies in the field of law in the future.

• The Journal of KAIRB
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• v.6 no.1
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• pp.17-31
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• 2024

Purpose: This study aims to develop preliminary items for measuring the perceived service quality of clinical trials among participants and to verify content validity. Methods: This study was designed as a methodological study. A conceptual framework was established based on Brady and Cronin's hierarchical model, and preliminary items were prepared through translation-back-translation, a review of existing instruments, and in-depth interviews with clinical trial participants and clinical research coordinators. The final items were completed through content validity testing by experts and a review of items by clinical trial participants for the prepared preliminary items. Results: Through this study, a set of 58 items across four domains (quality of interaction with researchers, the physical environment, performance procedures, and performance results) and 9 components (information·education·communication, trust, respect for participant preferences, securing facilities and space, accessibility, comfortability, informed consent, coordination of care, subjective understanding of clinical trials) on the service quality of clinical trials were completed. The scale content validity index of all preliminary items was 0.96, meeting the recommended standards. The individual-item content validity index also meets the recommended criteria for most items, excluding four items. Conclusion: This study holds significance in developing items to measure the quality of clinical trial execution from the perspective of participants. By verifying the reliability and validity of these items through subsequent research, it is expected that they can be utilized as a valuable instrument to devise strategies for improving the quality of clinical trials.

• The Korean Journal of Coaching Psychology
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• v.7 no.3
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• pp.1-26
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• 2023

This study aims to explore how professors at community colleges are experiencing career guidance for their students, uncovering key components related to causes, phenomena, contexts, coping strategies, etc., and deriving a paradigm model to structurally. To achieve this objective, focus group interviews were conducted with a total of 23 community college professors, and the results were analyzed using the grounded theory method (Strauss & Corbin, 1998). The analysis yielded 17 categories and 31 subcategories. The central phenomenon in the career guidance of community college professors was categorized into two groups: the distress experience in career guidance and attitude changes toward interactions with students..Three categories of causal conditions contributing to the occurrence of the central phenomenon were identified: professors' lack of expertise in career guidance, students' scope and needs for career guidance, and students' low interest and expectations. Additionally, four contextual conditions corresponding to special situations or circumstances that contributed to the central phenomenon were identified: increasing demand for career guidance, excessive burden and time constraints for career guidance, lack of communication about career guidance at the institutional level, and limitations within the career guidance support system. Furthermore, the study uncovered patterns of action-interaction, mediating conditions, and outcomes. This study is expected to contribute to a better understanding of the actual experiences of college professors in the career guidance process, and to stimulate consideration of the types of support required for effective career guidance for college professors in the future.