• Journal of the Korean Society of School Health
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• v.13 no.1
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• pp.131-145
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• 2000

For the purpose of strengthening Dental Hygiene students' confidence and motivation in the Dental Hygiene Department and helping construct proper professionalism, survey on Dental Hygiene students' consciousness of attitude to and satisfaction of the course, career plan and occupation mind set was carried out. 530 three year students in 8 Dental Hygiene academies in Seoul and Kyonggi province were questioned. The results of the survery are as follows:. 1. Dental Hygiene students' motives consisted primarily of employment and a desire for professionalism 25% of them entered the course after one failure in the entrance examination and 17% had family members engaging in the dentistry field 84%, the largest portion, were from an academic high school. 24% had some knowledge of Dental Hygiene, which they had acquired from seniors, friends, and teachers. 2. Patient care and treatment assistance related matters were not considered important in the course. The weak points of the course turned out to be education in computers and foreign languages, but the weakest was the ability of patient care during clinical training. 53% had experiences had thought of changing their major while in the course because it didn't match their aptitude and interest. 3. As for a career after graduation, 49% worried about it Most students wanted to work at a dental hospital or general hospital, The most favored duty was coordination or reception or oral disease preventive work. They wanted to work untill they had a stable living. 68% answered they would get a job at an oral clinic and 70% said they would continue studying for self-realization. 4. Satisfaction with the major was high in students whose aptitude and interest matched the course, who had background knowledge of the major, and who. didn't think of changing the major but would continue studying resulting in statistically slight difference(p<0.001). As to satisfaction with the faculties, it was high in the students whose aptitude and interest matched the major and who didn't think about a career after graduation showing a slight difference(p<05, p<0l). As for satisfaction with clinical training, students whose aptitude and interest matched the major and who didn't consider changing the major answered positively showing a statistically slight difference(p<.001, p<.01). As to satisfaction with the course, it was high in the students who entered with aptitude and interest, who had preliminary knowledge, who didn't consider changing the major, and who didn't think about a career after graduation showing a statistically slight difference(p<.001, p<.05). 5. Occupation mind-set was positive for students who entered with interest and aptitude, who had preliminary knowledge, and who had not considered changing the major showing a statistically slight difference(P<.001). The higher the satisfaction with the major, faculty and clinical training was, the more positive the occupation mind-set was(p<.001).

• Korean Journal of Microbiology
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• v.41 no.4
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• pp.275-280
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• 2005

Anthrax is an infectious disease caused by the gram-positive bacterium, Bacillus anthracis. Anthrax toxin is a tripartite toxin comprising of protective antigen (PA), lethal factor (LF) and edema factor (EF). PA is the receptor-binding component, which facilitates the entry of LF or EF onto the cytosol. LF is a zinc-dependent metalloprotease, which is a critical virulence factor in cytotoxicity of infected animals. Therefore, it is of interest to develop its potent inhibitors for the neutralization of anthrax toxin. The first step to identify the inhibitors is the development of a rapid, sensitive, and simple assay method with a high-throughput ability. Much efforts have been concentrated on the preparation of powerful assays and on the screening of inhibitors using these system. In the present study, we have tried to construct anthrax lethal factor in yeast expression system to prepare cell-based high-throughput assay system. Here, we have shown the results covering the construction of a new vector system, subcloning of LF gene, and the expression of target gene. Our results are first trial to express LF gene in eukaryote and provide the basic steps in design of cell-based assay system.

• 한국사회정책
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• v.25 no.3
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• pp.3-35
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• 2018

This article critically reviews the arguments that deny the financial feasbility and effectiveness of universal basic income as an alternative to existing social security systems and makes some suggestions to design effective and efficient basic income schemes. Regarding the financial feasibility of universal basic income, I argue that replacement of the existing regressive tax expenditures with universal basic income without raising tax rates can effectively reduce tax burden or provide income support to a majority of people except the rich. Addition of basic income to the tax base and reduction of the number of beneficiaries of public assistance and the amount of cash payment for them can further help save money. Regarding the redistributive effect, I note that the targeting ability of the existing social security systems is not good and that "the paradox of redistribution" that universal-type programs tend to be more redistributive than selective programs applies to universal basic income as well. I demonstrate significant redistributive effect of a hypothetical revenue-neutral basic income scheme and reviews several empirical studies done in Korea and abroad to show that basic income can be more effective in redistribution than social insurances or public assistance programs. Lastly, I emphasize the need to construct a reliable tax-benefit microsimulation model to help researchers to study redistributive effects of basic income schemes and other taxes and social policies.

• Journal of the Korean Society of Earth Science Education
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• v.11 no.3
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• pp.224-236
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• 2018

The purpose of this study was to develop a reasoning-based HTE-STEAM program for the development of the cognitive capacity of middle school students and enhancement of their STEAM literacy, and to investigate the effectiveness of this study in the school setting. The subjects of this study were the students of two middle schools located in the central region of Korea. The students participated in the HTE-STEAM program during their free-learning semesters and 202 of them were selected by random sampling method. Main findings were as follows: First, pre- and post-HTE-STEAM program has shown a significant value in statistical verification (p<.05) and the level of logical thinking ability of the research participants improved after the class compared to before the class. Second, the paired samples t-test comparing the difference between the pre and post scores of the STEAM attitude test has shown a significant value in statistical verification (p<.05), and the HTE-STEAM program has turned out to have a positive effect on the STEAM literacy of the research participants. Third, in the HTE-STEAM satisfaction scale test, the mean value of the sub-construct stood at 3.27~4.12, showing a positive overall response. Therefore, the HTE-STEAM program under the topic of earth science of 'Disaster and Safety' developed at the final stage of this study has proven to have a positive influence on the research participants in terms of the development of cognitive capacity by reasoning and collaborative learning, an important quality of communication and consideration necessary for STEAM literacy.

• (The)Korea Educational Review
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• v.23 no.1
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• pp.231-260
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• 2017

The Fourth Industrial Revolution foreshadows radical changes in our lives. In the era of the fourth industrial revolution called the digital revolution, individualized learning based on ubiquitous learning is emphasized. The contents of learning will be centered on procedural knowledge rather than narrative knowledge, and fusion education in which boundaries between learning domains are broken down will be achieved. First of all, learners in the fourth industrial revolution era should have critical thinking and problem solving abilities. Metacognition based on self-control and cognitive flexibility is important for effective self-directed and active learning. Creativity-based collaborative activities, social vision skills, and social and emotional skills are also important competencies. Therefore, in order to provide individualized learning contents to learners in the fourth industrial revolution era, they should be transformed into learning paradigm based on personal characteristics such as learners' self-efficacy, interest, curiosity and creativity. In addition to this, evaluation forms should be diversified according to changing teaching and learning methods. In order to cultivate teachers to lead such educational innovation, it is necessary to reconsider the teaching capacity. Teachers should be able to construct creative lessons by skillfully exploiting technology in future learning environments. In addition to this, it should also have the ability to collaborate and cognitive flexibility to converge with other academic disciplines. Along with these discussions, we proposed the need for policy intervention along with changes in education.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.469-475
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• 2022

As the recent structure construction is constructed as a large-scale and deep underground excavation in close proximity to the building, the installation of retaining wall and supporters (Struts) has become complicated, and the number of supporters to avoid interference of the structural slab has increased. This construction process becomes a factor that causes an increase in construction joints of a structure, leakage and an increase in wall cracks. In addition, this reduced the durability and workability of the structure and led to an increase in the construction period. This study planned to dismantle the two struts simultaneously as a plan to reduce the construction joints, and corrected the earth pressure by assuming the reaction force value by the initial earth pressure and the measured data as the response ratio. After recalculating the corrected earth pressure through the iterative trial method, it was verified by numerical analysis that simultaneous disassembly of the two struts was possible. As a result of numerical analysis applying the final corrected earth pressure, the measured value for the design reaction force was found to be up to 197%. It was analyzed that this was due to the effect of grouting on the ground and some underestimation of the ground characteristics during design. Based on the result of calculating the corrected earth pressure in consideration of the response ratio performed in this study, it was proved analytically that the improvement of the brace dismantling process is possible. In addition, it was considered that the overall construction period could be shortened by reducing cracks due to leakage and improving workability by reducing construction joints. However, to apply the proposed method of this study, it is judged that sufficient estimations are necessary as there are differences in ground conditions, temporary facilities, and reinforcement methods for each site.

• Journal of Christian Education in Korea
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• v.72
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• pp.183-203
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• 2022

The purpose of this study is to construct factors of AI education utilization competency. AI education utilization competency is used as basic data for education to enhance the AI education competency of pre-service early childhood teachers. To this end, 7 studies related to competency factors and models were selected by searching for previous studies. Seven preceding studies were analyzed. As a result, 18 competency factors were extracted, including understanding of artificial intelligence. The extracted competency elements were divided into six areas, which are divided into understanding subject knowledge through coding, class preparation, class management, class result feedback, class guidance, and self-development. And 15 factors were constructed. The draft formed through coding was improved through review by three early childhood education experts. Factors improved through expert review were structured by classifying them into knowledge, skills, and attitudes to organize the curriculum. The validity of the structured competency factor was verified through expert Delphi. As a result of the Delphi verification, all factors were converged in the first survey. Through this, 6 competency areas, 11 competency factors, and 19 competency factors were composed of knowledge, 10 skills, and 5 attitudes. The implication is that the competency factors presented as a result of this study can be used as basic data for organizing a curriculum to improve the ability of pre-service early childhood teachers to use artificial intelligence education.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.89-95
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• 2024

Nowadays, the competitiveness of a company depends on the ability of all organizational members to share and utilize the organizational knowledge accumulated by the organization. As if to prove this, the world is now focusing on ChetGPT service using generative AI technology based on LLM (Large Language Model). However, it is still difficult to apply the ChetGPT service to work because there are many hallucinogenic problems. To solve this problem, sLLM (Lightweight Large Language Model) technology is being proposed as an alternative. In order to construct sLLM, corporate data is essential. Corporate data is the organization's ERP data and the company's office document knowledge data preserved by the organization. ERP Data can be used by directly connecting to sLLM, but office documents are stored in file format and must be converted to data format to be used by connecting to sLLM. In addition, there are too many technical limitations to utilize office documents stored in file format as organizational knowledge information. This study proposes a method of storing office documents in DB format rather than file format, allowing companies to utilize already accumulated office documents as an organizational knowledge system, and providing office documents in data form to the company's SLLM. We aim to contribute to improving corporate competitiveness by combining AI technology.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.323-338
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• 2024

This study analyzes the Korean professional volleyball league and predict match outcomes using popular machine learning classification methods. Match data from the 2012/2013 to 2022/2023 seasons for both male and female leagues were collected, including match details. Two different data structures were applied to the models: Separating matches results into two teams and performance differentials between the home and away teams. These two data structures were applied to construct a total of four predictive models, encompassing both male and female leagues. As specific variable values used in the models are unavailable before the end of matches, the results of the most recent 3 to 4 matches, up until just before today's match, were preprocessed and utilized as variables. Logistc Regrssion, Decision Tree, Bagging, Random Forest, Xgboost, Adaboost, and Light GBM, were employed for classification, and the model employing Random Forest showed the highest predictive performance. The results indicated that while significant variables varied by gender and data structure, set success rate, blocking points scored, and the number of faults were consistently crucial. Notably, our win-loss prediction model's distinctiveness lies in its ability to provide pre-match forecasts rather than post-event predictions.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.