• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.67-78
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• 2024

Amidst the onset of the Fourth Industrial Revolution and the prominence of artificial intelligence, societal structures are undergoing significant changes. There is a heightened global interest in AI education for nurturing future talents. Consequently, this research aims to develop an AI-integrated Korean language curriculum for first-year high school students, utilizing the ADDIE model for instructional program development. To assess the program's effectiveness, pre-post assessments were conducted on future core competencies (Collaboration, Communication, Critical Thinking, Creativity) and knowledge information processing skills. The curriculum, spanning nine sessions and incorporating four small projects, sought to provide students with a new experience of AI-integrated Korean language education. As a result, students who participated in the program demonstrated improvement in future core competencies across all areas, and positive outcomes were observed in satisfaction levels and qualitative analysis. Through these findings, it is suggested that this program successfully integrates artificial intelligence into high school Korean language education, potentially contributing to the cultivation of future talents among students.

• Journal of Preventive Medicine and Public Health
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• v.57 no.3
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• pp.269-278
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• 2024

Objectives: The Chepang people, an indigenous ethnic group in Nepal, experience substantial marginalization and socioeconomic disadvantages, making their communities among the most vulnerable in the region. This study aimed to determine the prevalence and factors associated with adolescent pregnancy in the Chepang communities of Raksirang Rural Municipality, Makwanpur District, Bagmati Province, Nepal. Methods: A cross-sectional study was conducted from October 2022 to April 2023 among 231 Chepang women selected using simple random sampling from Raksirang Rural Municipality. A semi-structured questionnaire was used for interviewing the mothers. Bivariate and multivariate logistic regression analyses were performed, using odds ratios with 95% confidence intervals (CIs). Variables with a variation inflation factor of more than 2 and a p-value of more than 0.25 were excluded from the final model. Results: The study revealed that the prevalence rate of adolescent pregnancy among Chepang women was 71.4% (95% CI, 65.14 to 77.16). A large percentage of participants (72.7%) were married before the age of 18 years. Poor knowledge of adolescent pregnancy (adjusted odds ratio [aOR], 10.3; 95% CI, 8.42 to 14.87), unplanned pregnancy (aOR, 13.3; 95% CI, 10.76 to 19.2), and lack of sex education (aOR, 6.57; 95% CI, 3.85 to 11.27) were significantly associated with adolescent pregnancy. Conclusions: The prevalence of adolescent pregnancy among the Chepang community was high. These findings highlighted the importance of raising awareness about the potential consequences of adolescent pregnancy and implementing comprehensive sexuality education programs for preventing adolescent pregnancies within this community.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.856-874
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• 2019

The performance of recommender systems (RS) has been measured mainly in terms of accuracy. However, there are other aspects of performance that are difficult to understand in terms of accuracy, such as coverage, serendipity, and satisfaction with recommended results. Moreover, particularly with RSs that suggest multiple items at a time, such as YouTube, user satisfaction with recommended results may vary not only depending on their accuracy, but also on their configuration, content, and design displayed to the user. This is true when classifying an RS as a single RS with one recommended result and as a multiple RS with diverse results. No empirical analysis has been conducted on the influence of the content and distribution of recommendation items on user satisfaction. In this study, we propose a research model representing the content and distribution of recommended items and how they affect user satisfaction with the RS. We focus on RSs that recommend multiple items. We performed an empirical analysis involving 149 YouTube users. The results suggest that user satisfaction with recommended results is significantly affected according to the HHI (Herfindahl-Hirschman Index). In addition, satisfaction significantly increased when the recommended item on the top of the list was the same category in terms of content that users were currently watching. Particularly when the purpose of using RS is hedonic, not utilitarian, the results showed greater satisfaction when the number of views of the recommended items was evenly distributed. However, other characteristics of selected content, such as view count and playback time, had relatively less impact on satisfaction with recommended items. To the best of our knowledge, this study is the first to show that the category concentration of items impacts user satisfaction on websites recommending diverse items in different categories using a content-based filtering system, such as YouTube. In addition, our use of the HHI index, which has been extensively used in economics research, to show the distributional characteristics of recommended items, is also unique. The HHI for categories of recommended items was useful in explaining user satisfaction.

• Journal of Periodontal and Implant Science
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• v.53 no.6
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• pp.444-452
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• 2023

Purpose: The aim of this study was to retrospectively evaluate the survival and failure rates of RESTORE^® implants over a follow-up period of 10-15 years at a university dental hospital and to investigate the factors affecting the survival rate of these dental implants. Methods: A total of 247 RESTORE^® dental implants with a resorbable blast media (RBM) surface inserted in 86 patients between March 2006 and April 2011 at the Department of Periodontology of Seoul National University Dental Hospital were included. Patients with follow-up periods of less than 10 years were excluded, and data analysis was conducted based on dental records and radiographs. Results: Over a 10- to 15-year period, the cumulative survival rate of the implants was 92.5%. Seventeen implants (6.88%) were explanted due to implant fracture (n=10, 4.05%), peri-implantitis (n=6, 2.43%), and screw fracture (n=1, 0.4%). The results of univariate regression analysis using a Cox proportional hazards model demonstrated that implants placed in male patients (hazard ratio [HR], 4.542; 95% confidence interval [CI], 1.305-15.807; P=0.017) and implants that supported removable prostheses (HR, 15.498; 95% CI, 3.105-77.357; P=0.001) showed statistically significant associations with implant failure. Conclusions: Within the limitations of this retrospective study, the RESTORE^® dental implant with an RBM surface has a favorable survival rate with stable clinical outcomes.

• Journal of Food Hygiene and Safety
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• v.18 no.3
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• pp.139-145
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• 2003

The purpose of this study was to investigate the awareness and practice of Korean consumer on food safety. A telephone survey was conducted from 1,040 adults randomly selected from each province and large city of Korea. Therefore, 12.4% of the subjects experienced foodborne illness at least once a year and 0.3% was hospitalized due to the illness. General restaurant (37.2%) and home (21.2%) were the main causative place of foodborne illness, and the most frequently associated foods were meat and meat products (41.7%) and fish and fish products (18.7%). Regarding the causative agent of foodborne illness, the respsondents were aware of Cholera (75.5%), Vibrio gastroenteritis (73%), Shigellosis (65.5%), Bacillary dysentery (65.5%) and Salmonellosis (47.5%) very well; however very few were aware of Listeriosis (9.9%) and brucellosis (8.3%) and ever believed they were not food-related illness. When the survey data were analyzed based on 3 models (Model 1: Knowledge about the pathogens associated food and water, Model 2: The awareness of food safety, Model 3: Attitudes and behavior about foodborne disease prevention and measure) by Multiple regression analysis. The results showed that the awareness of the causative agent of foodborne illness was significantly related with the previous experience of foodborne illness (OR: 1.714) followed by education level (OR: 0.536) and married status (OR: 0.527). The awareness of food safety was significatly related with education level (OR: 0.702). Education (OR: 0.816) and gender (OR:0.650) were the main factors affecting the awareness of the practice to prevent foodborne illness. However, the previous experience of foodborne illness and food safety education, and the awareness of food safety did not show any correlation, suggesting that the experience and awareness of foodborne illness do not affect the real practice of food safety.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Journal of The Korean Association For Science Education
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• v.35 no.1
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• pp.37-52
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• 2015

In this study, assessment items to examine geocognition on plate tectonics were developed and applied to middle and high school students and college students. Conceptual constructs on plate tectonics are Earth interior structure, specific geomorphology, and geologic phenomena at each plate boundary. Construct for geocognition included temporal reasoning, spatial reasoning, retrospective reasoning, and system thinking. Pictorial data in each item were all obtained from GeoMapApp. Students' responses to the items were analyzed and measured cross-sectionally by Rasch model, which distinguishes persons' ability levels based on their scores for all items and compared them with item difficulty. By Rasch model analysis, Wright maps for middle and high school students and college students were obtained and compared with each other. Differential Item Functioning analysis was also implemented to compare students' item responses across school grades. The results showed: 1) Geocognition on plate tectonics was an assessable construct for middle and high school students in current science curriculum, 2) The most distinguished geocognition factor was spatial reasoning based on cross sectional analysis across school grades, 3) Geocognition on plate tectonics could be developed towards more sophisticated level through scaffolding of relevant instruction and earth science content knowledge, and 4) Geocognition was not a general reasoning separated from a task content but a content-specific reasoning related to the content of an assessment item. We proposed several suggestions for learning progressions for plate tectonics and national curriculum development based on the results of the study.

• The Mathematical Education
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• v.63 no.2
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• pp.123-138
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• 2024

This study described the utilization of ChatGPT in teaching and students' learning processes for the course "Introductory Mathematics for Artificial Intelligence (Math4AI)" at 'S' University. We developed a customized ChatGPT and presented a learning model in which students supplement their knowledge of the topic at hand by utilizing this model. More specifically, first, students learn the concepts and questions of the course textbook by themselves. Then, for any question they are unsure of, students may submit any questions (keywords or open problem numbers from the textbook) to our own ChatGPT at https://math4ai.solgitmath.com/ to get help. Notably, we optimized ChatGPT and minimized inaccurate information by fully utilizing various types of data related to the subject, such as textbooks, labs, discussion records, and codes at http://matrix.skku.ac.kr/Math4AI-ChatGPT/. In this model, when students have questions while studying the textbook by themselves, they can ask mathematical concepts, keywords, theorems, examples, and problems in natural language through the ChatGPT interface. Our customized ChatGPT then provides the relevant terms, concepts, and sample answers based on previous students' discussions and/or samples of Python or R code that have been used in the discussion. Furthermore, by providing students with real-time, optimized advice based on their level, we can provide personalized education not only for the Math4AI course, but also for any other courses in college math education. The present study, which incorporates our ChatGPT model into the teaching and learning process in the course, shows promising applicability of AI technology to other college math courses (for instance, calculus, linear algebra, discrete mathematics, engineering mathematics, and basic statistics) and in K-12 math education as well as the Lifespan Learning and Continuing Education.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.225-232
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• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.