• Journal of Digital Convergence
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• v.14 no.10
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• pp.1-8
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• 2016

Coding education is an effective convergence type educational tool. While solving problems and designing programs, students can enhance problem solving ability, logical reasoning ability and creative thinking. Researches on coding education are done primarily for elementary school and middle school students. However, researches on college students are lacking. Today, educating college students about coding is in dire need. Although there are efforts to promote the importance of coding education and make it requirements. People find it difficult to find ways to provide training. There is a need for researches on coding as universal education. Therefore, this research proposed educational training using app inventor based on flipped running in order to effectively promote coding education. This study conducted the survey and the personal interview to measure the effectiveness of coding education. It is hoped that, through coding education, students who do not major in coding could combined their knowledge of their major with coding to improve their problem solving ability to solve various problems based on computing knowledge and approach.

• Journal of The Korean Association of Information Education
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• v.24 no.4
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• pp.401-412
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• 2020

Team-based programming methods are widely applied to solve various difficulties that pre-service teachers experience in the programming lessons. To prepare effective collaboration strategies necessary for them, it is necessary to analyze various collaborative factors that affect learners' programming competencies. Therefore, in this article, a questionnaire survey was conducted by dividing learners' collaboration factors into individual and team competencies, and the relationship between learners' programming competencies was analyzed. As a result of the verification, the program design competency showed significant results in all elements of the learner's personal competency, team techniques such as data sharing skills necessary for collaboration, and team collaboration. It was analyzed that an individual's understanding of learning and team collaboration influenced the program implementation competency. In addition, the group with relatively high team technique showed significant differences in programming competence, interest, and satisfaction. Accordingly, by linking meaningful factors related to individual and team competencies according to the programming process, a collaborative strategy practically necessary for pre-service teachers was suggested.

• Journal of The Korean Association of Information Education
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• v.8 no.2
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• pp.191-201
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• 2004

In accordance with wide spread of personal computer and the expansion of network access, the use of internet has been popular and communication by text message is much more normal than that of voice and image. Accordingly, the side effect of communication language brings about gap between diverse social class, the isolation of communication between generations, abusive expressions, obstacles of juvenile mental development and so on. It appears by the form of slang and vulgar word and has a negative effect on education of mother tongue and usage of children's real language. To deal with these problems, we developed new web-based education system through the analysis of learners' requirement; "Barun Mal, Goeun Mal class". So we verified its efficiency to apply to real class. We also found that this system increased the learners' interest and educational effectiveness. Also, this system contributed to the proper use of language.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.99-106
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• 2015

This research is focused on the development of the proprietary database embedded in the OTT device, which is used for searching and indexing video contents, and also the development of the search algorithm in the form of the critical components of the interface application with the OTT's database to provide video query searching, such as remote control smartphone application. As the number of available channels has increased to anywhere from dozens to hundreds of channels, it has become increasingly difficult for the viewer to find programs they want to watch. To address this issue, content providers are now in need of methods to recommend programs catering to each viewer's preference. the present study aims provide of the algorithm which recommends contents of OTT program by analyzing personal watching pattern based on one's history.

• International Journal of Human Ecology
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• v.12 no.2
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• pp.13-24
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• 2011

This study compared actual weight with perceived weight, described the prevalence of unhealthy weight control behavior, determined the differences in psychological and personal variables between participants that reported unhealthy weight control behavior and those who did not, and examined the relationship of eating habits to unhealthy weight control behavior for Korean adolescents. The study population consisted of a nationally representative sample of middle and high school students who completed the Fifth Korea Youth Risk Behavior Web-based Survey (KYRBWS): Fifth in 2009. Among the 75,066 participants of KYRBWS, 35,473 (n = 18,851 girls and 16,622 boys) were eligible for a research focused on unhealthy weight control behavior. The results of this research were as follows: First, there were considerable discrepancies (45.1% of girls and 32.8% of boys) between the perceived weight and the actual weight. Second, overall, unhealthy weight control behavior was more prevalent in girls and fasting was the most commonly reported behavior. Third, participants that reported unhealthy weight control behavior scored significantly lower on scaled measures of happiness, health, academic achievement, and economic status; in addition, they scored higher on stress measures. Fourth, girls and boys shared common protective factors of having breakfast and vegetables more often, perceiving their weight as underweight rather than overweight, and having a correct weight conception. Protective factors unique to girls were having lunch and dinner more often. Girls and boys shared common risk factors of the consumption of soda, fast food, instant noodles, and snacks more often, while consumption of fruit more often was a risk factor only for girls. The improvement of protective factors and minimization of risk factors through Home Economics classes (and other classes relevant to health) may mitigate unhealthy weight control behavior of adolescents.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.33-39
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• 2011

In modern society, in business decisions of our customers are continually increasing in importance, and owing to the development of information and communication technology effectively on a computer to measure the preferences of key customer techniques are being studied. However, this preference reflects significantly on personal ideas, and therefore, it is difficult to commercialize a measure calculated according to the ambiguous results. In this paper, by using biometric information that has been measure; we have configured the multi-sensitivity models based on customer preferences to evaluate the proposed system. This system consists of multiple biometric information of multi-dimensional vector model for learning through the use of structured emotional to apply the same criteria to evaluate customer preferences. In addition, by studying the specific subject-specific emotion model, it is shown to improve accuracy with further experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.251-256
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• 2021

Unlike general cameras, a high-speed camera capable of capturing a large number of frames per second can enable the advancement of some image processing technologies that have been limited so far. This paper proposes a method of removing undesirable noise from an high-speed input color image, and then detecting a human face from the noise-free image. In this paper, noise pixels included in the ultrafast input image are first removed by applying a bidirectional filter. Then, using RetinaFace, a region representing the person's personal information is robustly detected from the image where noise was removed. The experimental results show that the described algorithm removes noise from the input image and then robustly detects a human face using the generated model. The model-based face-detection method presented in this paper is expected to be used as basic technology for many practical application fields related to image processing and pattern recognition, such as indoor and outdoor building monitoring, door opening and closing management, and mobile biometric authentication.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.85-110
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• 2018

A chatbot is an interactive assistant that utilizes many communication modes: voice, images, video, or text. It is an artificial intelligence-based application that responds to users' needs or solves problems during user-friendly conversation. However, the current version of the chatbot is focused on understanding and performing tasks requested by the user; its ability to generate personalized conversation suitable for relationship-building is limited. Recognizing the need to build a relationship and making suitable conversation is more important for social chatbots who require social skills similar to those of problem-solving chatbots like the intelligent personal assistant. The purpose of this study is to propose a text analysis method that evaluates relationships between chatbots and users based on content input by the user and adapted to the communication situation, enabling the chatbot to conduct suitable conversations. To evaluate the performance of this method, we examined learning and verified the results using actual SNS conversation records. The results of the analysis will aid in implementation of the social chatbot, as this method yields excellent results even when the private profile information of the user is excluded for privacy reasons.

• Research in Community and Public Health Nursing
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• v.30 no.3
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• pp.295-306
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• 2019

Purpose: There has been a growing recognition that person-centered care enhances the quality of life of nursing home residents with dementia. This study was conducted to develop a person-centered dementia care online education program for direct care staff in long-term care facilities. Methods: Delphi method with expert group was used to validate contents. We developed 61 draft items based on literature review. Twenty experts participated in consecutive three round surveys including 5-point Likert scale questions and open-ended questions. Based on experts' opinions, the content validity ratio for content validity and the coefficient of variation for stability were calculated. Results: Three-round Delphi surveys and additional feedback from the expert panel established a consensus of core contents: 1) dementia (7 categories), 2) person-centered care (6 categories), 3) communication (8 categories), and 4) behavioral and psychological symptoms of dementia (6 categories). Specific sub-categories in each category were differentiated according to the job qualifications (65 sub-categories for registered nurses, 64 sub-categories for nursing aids, and 41 sub-categories for personal care workers). Conclusion: This delphi study identified person-centered dementia education curricula, in which the person-centered approach should be a key policy priority in Korean long-term care system. Now it is urgently needed to develop education programs utilizing online platforms that enable efficient and continuous learning for long-term care staff, which can contribute to behavior changes in the person-centered dementia care approach and improvement of care quality in long-term care facilities.

• The korean journal of orthodontics
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• v.51 no.2
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• pp.77-85
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• 2021

Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 PA-cephalograms synthesized from cone-beam computed tomography scans (CBCT-PA) were selected as samples. Twenty-three landmarks used for Tweemac analysis were manually identified on all CBCT-PA images by a single examiner. Intra-examiner reproducibility was confirmed by repeating the identification on 85 randomly selected images, which were subsequently set as test data, with a two-week interval before training. For initial learning stage of the multi-stage CNN model, the data from 345 of 430 CBCT-PA images were used, after which the multi-stage CNN model was tested with previous 85 images. The first manual identification on these 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors in manual identification and artificial intelligence (AI) prediction. Results: The AI showed an average MRE of 2.23 ± 2.02 mm with an SDR of 60.88% for errors of 2 mm or lower. However, in a comparison of the repetitive task, the AI predicted landmarks at the same position, while the MRE for the repeated manual identification was 1.31 ± 0.94 mm. Conclusions: Automated identification for CBCT-synthesized PA cephalometric landmarks did not sufficiently achieve the clinically favorable error range of less than 2 mm. However, AI landmark identification on PA cephalograms showed better consistency than manual identification.