• Journal of dental hygiene science
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• v.18 no.4
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• pp.227-233
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• 2018

The purpose of this study was to investigate the factors affecting dentists' attitudes and dental hygienists' services on dental anxiety in adults. The subjects were 300 adults older than 20 years of age living in Seoul, Gyeonggi, Daejeon, and Daegu. Data were collected using structured questionnaires. Among the distributed questionnaires, 225 respondents were selected as subjects, excluding 74 people who did not answer and 1 person who was not faithful. Data were analyzed using statistical software with a t-test, one-way ANOVA, and multiple regression. As a result, the gender was slightly higher in women (54.7%) than in men, and the last dental visit was less than one year in 59.6% of respondents. Most of the respondents' educational level was higher than college level (79.1%), and the monthly income was less than 2 million won in 53.8 of respondents. This study showed that distrust of dentists affected dental anxiety and anxiety stimulation. Higher reliability of the dentist was correlated with less dental anxiety in patients. Dental anxiety showed statistically significant results in dentist subcategories of patient slight and dentists' trust (p<0.01). Additionally, the factors affecting dental anxiety and anxiety stimulus were knowledge of dental hygienist and distrust of dentist (p<0.01). According to this study, dentists' and dental hygienists' trust of dental staff show the importance of oral health professionals' role in reducing dental anxiety in patients. It is also suggested that efforts should be made to improve public awareness of oral health experts. It is believed that dentists, and dental hygienists need to promoted to become professionals. In addition, a variety of programs have been developed to reduce dental anxiety, so patients need to be comfortable to receive dental treatment.

• Korean journal of communication and information
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• v.22
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• pp.131-166
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• 2003

The opening of advertising market and introduction of the free competition doctrine make the competition harsher among advertising agencies. Advertising agencies do their best to execute their ad more efficiently and scientifically. But, it is the reality that broadcasting advertising industry in korea did not construct enough infrastructure to execute the systematic activities compared with that of advanced countries. So, we need to grasp the present conditions and draw a time-table to construct primarily necessary infrastructures. In case of hardware infrastructure in advertising industry, digitalization of broadcasting and convergence of broadcasting with telecommunication make it hurry to construct that. But as the ad agencies was in the situation to compete each other, they have a difficulty to construct common hardware infrastructure enthusiastically. Thus, it is necessary to build hardware infrastructure in advertising industry for policy. And the construction of that should be executed systematically not for the short term effects but for the long term objectives. Also, it is the most important to construct reliable Software infrastructure in advertising industry from all of ad agencies. In these days, ad agencies have a tendency not to believe the important information, like the data of ratings and advertising transaction information, in relation to the advertising activities. And they do not share and communicate about the information of the advertising industry trends, research trends, advertisement related information. So, it is also hurry to build the on-line and off-line database system. Finally, for the development of brainware infrastructure in advertising industry, it is the most necessary to activate the cooperation relation between university and advertising agencies. Universities need to invite experts in the advertising to teach the students practical knowledge and ad agencies to recruit students who want to develop their carrier in the advertising industries. In conclusion, advertising industry in korea to solve these tasks for the development of advertising industry infrastructure in the way of cooperation and harmony of each other rationally and efficiently.

• Journal of The Korean Association For Science Education
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• v.37 no.5
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• pp.859-877
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• 2017

The purpose of this study is to probe secondary science teachers' perception on scientific models and modeling. A total of 50 experienced science teachers were surveyed with 10 open-ended questions about several aspects of models and modeling: definition, examples, purpose, multiplicity, changeability, design/construction, evaluation and beliefs in the use of models and modeling as a teaching tool. The analysis of the data shows the following results: 1) understanding of models and modeling held by a majority of experienced secondary science teachers was far from that of experts as they concentrated on a model's superficial, representative, and visual functions, 2) when it comes to their view toward the use of a model, a model does not remain in the stage of 'doing science' but in the stage of being a subsidiary teaching tool for the teacher's explaining and the students' understanding of scientific concepts, 3) the subjects they majored in made meaningful differences in their contextual understanding of models and modeling, 4) though most of the teachers acknowledged the importance of teaching about models and modeling, even a lot of them showed a negative position toward the opinion that they are willing to apply modeling to their classes. Implications of the results were discussed in terms of intervention in order to enhance secondary science teachers' understanding and pedagogical content knowledge of models and modeling.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• Journal of radiological science and technology
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• v.36 no.2
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• pp.99-110
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• 2013

In the medical field, the necessity of education growth for the professional Radiation Technologists has been emphasized to become experts on radiation and the radiation field is important of the society. Also, in hospitals and companies, important on thesis is getting higher in order to active and cope with rapidly changing internal and external environment and a more in-depth expert training, the necessity of new teaching and learning model that can cope with changes in a more proactive has become. Thesis writing classes brought limits to the in-depth learning as to start a semester and rely on only specific programs besides, inevitable on passive participation. In addition, it does not have a variety opportunity to present, an actual opportunity that can be written and discussed does not provide much caused by instructor-led classes. As well as, it has had a direct impact on the quality of the thesis, furthermore, having the opportunity to participate in various conferences showed the limitations. In order to solve these problems, in this study, writing thesis has organized training operations as a consistent gradual deepening of learning, at the same time, the operational idea was proposed based on the connectivity integrated operating and effective training program & instructional tool for improving the ability to perform the written actual thesis. The development of teaching and learning model consisted of 4 system modeling, scaffolding, articulation, exploration. Depending on the nature of the course, consisting team following the personal interest and the topic allow for connection subject, based on this, promote research capacity through a step-by-step evaluation and feedback and, fundamentally strengthen problem-solving skills through the journal studies, help not only solving the real-time problem by taking wiki-space but also efficient use of time, increase the quality of the thesis by activating cooperation through mentoring, as a result, it was to promote a positive partnership with the academic. Support system in three stages planning subject, progress & writing, writing thesis & presentation and based on cognitive apprenticeship. The ongoing Coaching and Reflection of professor and expert was applied in order to maintain these activities smoothly. The results of this study will introduce actively, voluntarily and substantially join to learners, by doing so, culture the enhancement of creativity, originality and the ability to co-work and by enhance the expertise of based-knowledge, it is considered to be help to improve the comprehensive ability.

• Journal of Nutrition and Health
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• v.47 no.2
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• pp.145-156
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• 2014

Purpose: The purpose of this study was to establish accreditation systems of reliable educational materials for nutrition and dietary life which could be used in schools, workplace, and health promotion. Methods: The study was conducted from April 2011 to October 2011. Literature reviews, institutional visits, and telephone interviews were conducted. Expert meetings and advisory councils were held in order to receive feedback on development of the accreditation systems. A survey was conducted for the accreditation procedures on 143 professionals, including professors, researchers, health and medical experts, teachers, nutrition teachers, dietitians, and clinical nutritionists. Results: The final procedure of the developed accreditation system was finalized as follows: 1) receiving application twice per year 2) complete desk review (written evaluation) by three reviewers within two months, 3) board review (all board members) and decision, and 4) notification of results. The accreditation system is set for printed materials, web-site, and materials for activities. The certificate and accreditation mark is issued to the final certified educational materials. Expiration date is established only for the web-site form. The accreditation length lasts for two years, and can be extended by renewal application. Conclusion: The dietary and nutrition related materials, which are certificated by this accreditation system, could impart reliable information and knowledge to both learners and educators, and help them in effective selection of educational materials. Therefore, this accreditation system might be expected to increase satisfaction for teaching and learning about nutrition and healthy dietary life.

• Journal of Korean Home Economics Education Association
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• v.20 no.3
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• pp.107-129
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• 2008

The purpose of this study was to get insights and data from home economics teachers for the development of an in-service educational program on practical problem focused home economics program, which was planned to be held from January 21st and to 29th, 2008. For this, focus group interview, one of qualitative research methods, was used. One session of pilot and three sessions of main focus group interview in which total of 18 from October 31st, 2007 to November 14, 2007. Home economics teachers that participated were carried out. Participants requested the followings contents of an in-service educational program regarding the practical problem focused home economics curriculum. First, most of participants strongly desired to participate in an in-service educational program when a program provided. The participants wanted to be a professional, who is able to explain logically with philosophical background and knowledge about the practical problem focused home economics curriculum. Second, participants requested the followings regarding practical problem focused home economics curriculum for the contents of an in-service educational program: philosophy of home economics, setting a perspective on each content areas, development of practical problem, watching a sample class unit, developing teaching materials and motivation stimulating questions, designing of instruction and lesson plans, class presentation and peer evaluation, constructing paper and pencil test items, and feedback from expects on the practical problem focused home economics curriculum. Third, participants wanted an in-service educational program to be a combination of theory and practice, and at least 50% of it allotted to practice. Participants thought that both peer participants and experts from university would evaluate them whether they achieved the objectives of the in-service educational program if an in-service program has to evaluate participants. Participants would evaluate an in-service educational program excellent when they become empowered to teach other home economics teachers the theoretical aspects of the practical problem focused home economics curriculum and the practical aspects as well. Based on the results of this study a framework of the 30 hours practical problem focused home economics curriculum was proposed.

• Journal of The Korean Association For Science Education
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• v.38 no.6
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• pp.765-780
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• 2018

The purpose of this study is to synthesize the theoretical perspectives, research methods, and research results of teachers' professional vision by reviewing and analyzing previous research papers and to suggest implications for science teacher education and research. Three databases were used to search peer reviewed journal articles published between 1997-2017, which include 'teachers' and 'professional vision' explicitly in abstracts and empirical studies only. 21 articles in total were analyzed and review results are as follows. First, researchers regarded professional vision as a new concept of teacher professionalism. Previous research viewed professional vision as integrated structure of teachers' knowledge or ability activated at specific moment. Second, the analytical framework of professional vision included two aspects; 'selective attention' and 'reasoning'. Several aspects of lessons or the desirable teaching and learning factors are suggested as the subcategories of selective attention. Hierarchical levels or independent reasoning ability factors are suggested as the subcategories of reasoning process. Third, research on teachers' professional vision focused more on middle school teachers than elementary teachers and on various subject areas. Most studies used video clips and more cases of using videos of non-participants were found. In case of measurement of professional vision, most quantitative scoring methods were whether the responses of experts and teachers on video clips were consistent. Last, most studies examined or assessed teachers' professional vision. It is reported that in-service teachers' professional vision was evaluated higher than novice teachers' and using video clips were effective to examine and improve teachers' professional vision.