• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.897-907
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• 2023

The purpose of this study is to find out the development direction of adult learners' blended learning class as a study on the class satisfaction of adult college students according to the blended learning class participation type. To this end, a survey was conducted targeting adult learners at two schools that are carrying out the LiFE project (lifelong education system support project). The analyzed research results are as follows. First, in the case of adult learners, the proportion of participating in liberal arts + major classes as a blended learning subject was 77.8%, and home was the highest with 69.8% as a place to participate in online classes. Second, satisfaction with the blended learning teacher/instructor teaching method was generally satisfied with 95.2% of average or higher. Third, 96.8% of the students answered 'yes' or higher regarding the level of satisfaction with the blended learning method. The above research results show that blended learning is one of the important teaching and learning methods in providing adult-tailored education to adult learners who combine work and study. It can be seen that the blended learning teaching method is an effective teaching method that enables interactive communication between the instructor and the learner rather than the one-way teaching-oriented content delivery class of the traditional classroom.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.716-723
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• 2023

Global discussions are actively underway regarding the introduction of urban air mobility (UAM) to revolutionize the paradigm in the innovative mobility industry. While research related to airspace, vertiports, navigation, and communication pertinent to Korean UAM is actively pursued by relevant research institutions, there is a significant dearth in studies focusing on establishing concepts for operational management by UAM operators and formulating control procedures. The commercialization of UAM necessitates the establishment of standardized operational management concepts, pivotal as benchmarks for the individual system development among multiple UAM operators. This paper analyzes UAM exceptional law, operational readiness, existing regulations pertaining to commercial and rotary-wing aircraft, and proposes suitable approaches to formulate domestic low-density operational management and control procedures. By presenting strategies for conceptualizing operational management and control procedures in the initial low-density environment for UAM, this paper aspires to contribute to future trail operations and the wider adoption of UAM.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.97-108
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• 2024

This research delves into the escalating concerns of accidents and fatalities in the construction industry over the recent five-year period, focusing on the development of a Safety Perception Model to augment safety measures. Given the rising percentage of elderly workers and the concurrent drop in productivity within the sector, there is a pronounced need for leveraging Fourth Industrial Revolution technologies to bolster safety protocols. The study comprises an in-depth analysis of statistical data regarding construction-related fatalities, aiming to shed light on prevailing safety challenges. Central to this investigation is the formulation of a Safety Perception Model tailored for small-scale construction projects. This model facilitates the quantification of safety risks by evaluating safety grades across construction sites. Utilizing the DWM1000 module, among an array of wireless communication technologies, the model enables the real-time tracking of worker locations and the assessment of safety levels on-site. Furthermore, the deployment of a safety management system allows for the evaluation of risk levels associated with individual workers. Aggregating these data points, the Safety Climate Index(SCLI) is calculated to depict the daily, weekly, and monthly safety climate of the site, thereby offering insights into the effectiveness of implemented safety measures and identifying areas for continuous improvement. This study is anticipated to significantly contribute to the systematic enhancement of safety and the prevention of accidents on construction sites, fostering an environment of improved productivity and strengthened safety culture through the application of the Safety Perception Model.

• Journal of The Korean Association For Science Education
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• v.44 no.2
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• pp.219-229
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• 2024

The purpose of this study is to analyze the concerns and difficulties encountered by authors involved in the development of integrated science textbooks. Specifically, it focuses on their experiences with understanding and implementing the 2022 revised science curriculum. We collected 89 opinions from textbook authors and categorized them into several key areas: understanding the terminology and descriptors provided in the curriculum, structuring learning content, inquiries and activities, and the depth and scope of learning content. The analysis revealed that the most difficulty encountered by the textbook authors was in defining the level and scope of learning content. Many also expressed concerns and difficulties related to the ambiguity of terms and predicates. In terms of the composition of learning content, difficulties were identified in concerning the repetitive descriptions of achievement standards and the discrepancy between the arrangement of achievement standards and the flow of learning. Regarding inquiries and activities, there were experiments presented that were difficult to experience or actually implement, along with limitations in activity composition due to the need to optimize learning volume. Given the importance of high-quality textbooks for effective science education at the national level, it is crucial to establish effective communication channels between curriculum developers and textbook authors. Additionally, a robust support system for textbook development should be established.

• Journal of The Korean Association For Science Education
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• v.29 no.1
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• pp.90-105
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• 2009

This study analyzed current programs practiced by science-gifted education centers. This study was based on concerns of 18 science teachers on six science-gifted education centers of the Seoul Metropolitan Office of Education that had local representatives. For this study, we collected data using journals, documents, reports, survey reviews and interviews with science teachers. Science teachers were concerned about the selection and identification of gifted students, education periods, curriculum, and student evaluation. More authentic measurement for students' potential ability were needed for the identification and selection process. If the purpose of science-gifted centers was to be met, the number of students selected should be determined by local differences rather than regional equality. The curriculum and educational period could make good use of time allotted for vacation to increase lesson periods. Lessons based on strategies like contests for improving the students' creativity, free inquiry and communication skills had to be encouraged. A consistent system for science-gifted education from primary school to high school was needed.

• 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 Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.609-617
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• 2020

After introducing the industry 4.0 policy, Korean government announced 'Digital New Deal' and 'Green New Deal' as 'Korean New Deal' in 2020. We analyzed Korea Institute of Geoscience and Mineral Resources (KIGAM)'s research projects related to that policy and conducted markets analysis focused on Digital Twin and environmental monitoring technologies. Regarding 'Data Dam' policy, we suggested the digital geo-contents with Augmented Reality (AR) & Virtual Reality (VR) and the public geo-data collection & sharing system. It is necessary to expand and support the smart mining and digital oil fields research for '5th generation mobile communication (5G) and artificial intelligence (AI) convergence into all industries' policy. Korean government is suggesting downtown 3D maps for 'Digital Twin' policy. KIGAM can provide 3D geological maps and Internet of Things (IoT) systems for social overhead capital (SOC) management. 'Green New Deal' proposed developing technologies for green industries including resource circulation, Carbon Capture Utilization and Storage (CCUS), and electric & hydrogen vehicles. KIGAM has carried out related research projects and currently conducts research on domestic energy storage minerals. Oil and gas industries are presented as representative applications of digital twin. Many progress is made in mining automation and digital mapping and Digital Twin Earth (DTE) is a emerging research subject. The emerging research subjects are deeply related to data analysis, simulation, AI, and the IoT, therefore KIGAM should collaborate with sensors and computing software & system companies.

• The Korean Nurse
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• v.32 no.5
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• pp.66-77
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• 1994

The purpose of this study was to develop a new nursing unit which can meet changing health care needs, enhance patients' satisfaction and nurses' job satisfaction, and finally guarantee quality nursing care with present manpower. For this, one medical unit was selected as a unit for quality care. And one medical unit which is similar in staffing and patients' characteristics was selected as a control unit. To assess present problems and identify the remedies to the problems a hospital-wide survey and a workshop were performed. According to the survey results, educational programs and improvement of the facilities and equipment supply system, managereal support for interdepartmental cooperation and intensification of bed-side nursing care were adopted as main principles for operating model unit, This model unit was operated for 3 months from Sep. 1, 1992 to Nov. 30, 1992. To evaluate the effectiveness of the model unit, derect/indirect nursing care hours, patients' satisfaction to nursing care, nurses' job satisfaction, and quality care index were measured. Direct/indirect nursing care hours were compared with that of the control unit, and patients' and nurses' satisfaction and quality care index were measured before and after operating model unit and compared with each other. The results of the study were as follows; 1. In the model unit mean direct nursing care hours per cach shift was 146.88 minutes and indirect nursing care hours was 354.72 minutes. The ratio of the direct nursing care hour to indirect nursing hour was 29.6 ; 70.4 and that of the control unit was 26.9 : 73.1. Direct nursing care hour in model unit was longer than that of the control unit. But, the difference was not significant. In subcategories of direct nursing care, the time spent in mobility and exercise, conservation of body temperature, hygiene, and communication and health education were longer than that of the con" trol unit. 2. Indirect nursing care hour in model unit was shorter than that of the control unit. But, the difference was not significant. In subcategories of indirect nursing care, the time spent in drug management and ward arrangement was shorter than that of the control unit. 3. Patients' satisfaction to nursing care was increased significantly after operating the model unit (T=-3.48, P=-0.002) and satisfaction to subcategories of physical comfort measure, psychological cate, and unit management components were significantly higher than before. 4. In the model unit, nurses' total job satisfaction was increased significantly after operating the model unit(Z=2.1004, P=.0357) and satisfaction to subcategory of satisfaction to administration was significantly higher than before (Z=-2.0732, P=.0382). 5. After operating the model unit, quality care index was increased from 89 to 93. With this results, it can be summarized that all the measures tried for quality care, such as educational programs, managereal support for interdepartmental cooperation, and improvement of the equipment and facility provision resulted in partial increase in direct nursing care hours, nurses satisfaction to their job and patients' satisfaction to nursing care. In can be postulated that managereal support and motivation without proper staff supplementation is not enough for increasing direct nursing care hours. And for the enhancement of the level in clinical nursing, and staff supplement must be considered sincerely and the measures for reducing indirect nursing care hours, such as computerization of nursing care activities, improvement of facilities and equipment and facilities supply system, must be instituted in addition.