• Journal of Advanced Technology Convergence
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• v.3 no.3
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• pp.17-27
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• 2024

This study focuses on the development and evaluation of a chatbot system based on the International Classification of Functioning, Disability, and Health (ICF) framework to enhance community participation among elderly individuals with mild dementia in South Korea. The study involved 12 elderly participants who were living alone and had been diagnosed with mild dementia, along with 15 caregivers who were actively involved in their daily care. The development process included a comprehensive needs assessment, system design, content creation, natural language processing using Transformer Attention Algorithm, and usability testing. The chatbot is designed to offer personalized activity recommendations, reminders, and information that support physical, social, and cognitive engagement. Usability testing revealed high levels of user satisfaction and perceived usefulness, with significant improvements in community activities and social interactions. Quantitative analysis showed a 92% increase in weekly community activities and an 84% increase in social interactions. Qualitative feedback highlighted the chatbot's user-friendly interface, relevance of suggested activities, and its role in reducing caregiver burden. The study demonstrates that an ICF-based chatbot system can effectively promote community participation and improve the quality of life for elderly individuals with mild dementia. Future research should focus on refining the system and evaluating its long-term impact.

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.135-149
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• 2023

The purpose of this study is to develop and implement an integrated course model that centers around the subject of Home Economics Education Curriculum and Teaching Methods and its pedagogical approaches, as well as the subject of Chinese Language and Literature Curriculum and Teaching Methods and its pedagogical methods. This study aims to provide a framework to prepare pre-service teachers to effectively address a variety of educational issues in future educational settings. To achieve these objectives, the study utilizes Fogarty's connected model as a guiding framework to explore the impact of the integrated curriculum on fostering collaborative and divergent thinking among students. The findings of this research confirm that this model not only cultivates interdisciplinary competencies among course participants but also goes beyond the mere transmission of knowledge to build the capacities needed for forming an educational community, thereby increasing course satisfaction. Additionally, the study substantiates the importance of learner-centered strategies, cooperative learning, and diverse evaluation mechanisms. Such an integrated course model has the potential to revolutionize not only pre-service teacher education but also to be applicable in in-service teacher training, thus contributing to solving a broader range of educational issues.

• Therapeutic Science for Rehabilitation
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• v.13 no.3
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• pp.37-50
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• 2024

Korea has the highest suicide rate among Organization for Economic Cooperation and Development countries, with increasing rates observed among children and adolescents with low levels of life satisfaction. Patients in these age groups exhibit particularly turbulent and impulsive behaviors, which make suicide highly contagious and fatal. The loss of meaningful work or activities increases the risk of suicide, especially in young people. Following revisions to the Mental Health Welfare Act (2016), which has included occupational therapists among mental health professionals, a multidisciplinary approach including occupational therapy has been emphasized upon in mental health services. Screening tools for preventing suicide in children and adolescents include the Suicidal Ideation Scale, Beck's Suicidal Ideation Scale, Columbia-Suicide Severity Rating Scale, Reasons for Living Scale for Adolescents, and the Student Emotional and Behavioral Characteristics Test. The Canadian Occupational Performance Measure, which is an occupational therapy evaluation tool, is also used. Various suicide prevention programs have been proposed across academic disciplines; however, due to the urgent nature of high-risk groups, there is a need for timely services. Currently, most existing programs focus primarily on the cognitive-behavioral aspects. In this study, we aimed to introduce diverse suicide prevention programs for mental health professionals working with high-risk children and adolescents in order to equip them with the relevant information and help apply their learnings effectively in different situations.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.3
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• pp.23-44
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• 2024

In recent years, there has been a significant increase in the importance of curation services that analyze user information requests to provide tailored information within extensive information resources. This study aims to identify user information needs by analyzing search term logs from the Presidential Archives Portal to enhance the utilization value of presidential records, which possess high historical significance. In addition, by evaluating the portal's search performance, this study seeks to determine whether the Presidential Archives Portal is providing archival information services that meet users' information needs and to suggest areas for improvement through digital record curation services. To achieve these objectives, topic analysis and word network analysis were conducted based on search term logs spanning the past eight years. The search quality of the Presidential Archives Portal was evaluated from an accuracy perspective, focusing on areas with high user demand, and recommendations were drawn based on the results of the analysis. As a preliminary study for digital record curation of presidential records, this study is significant because it identifies specific user information needs and quantifies the search quality of archival portal sites to improve user satisfaction.

• Journal of Korean Home Economics Education Association
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• v.27 no.4
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• pp.19-35
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• 2015

The purpose of this study was to develop and evaluate a teaching learning process plan for the practice of caring and sharing to improve character of highschool students through Home Economics subject. The teaching learning process plan consisting of 13-session lessons has been developed and implemented according to the ADDIE model for the unit of 'Happy Family Life and Culture led by Family'. The unit was divided into two themes: Theme I caring through sharing and Theme II caring through practice. Six practice elements of caring and sharing such as communication, gratitude, courage, love, empathy, and environment drawn from Theme I are applied to Theme II. Various activities and teaching materials as well as questionnaire were developed. The plan was applied to 8 classes, 287 freshmen of S highschool in Jeonju-si from March to May, 2014. Three factors were drawn from 35 character-related items: self-perception, perception of caring and sharing, and practice of caring and sharing. These factors were related to respondents' satisfaction with family relationships and school life. Two factors except self-perception improved through 13 lessons. Students evaluated that the whole caring and sharing practice lessons of Theme I and II gave them the chance to realize a actual practice in everyday life was important even with small efforts such as cooking for special family. Also students commented that the praising workbook was impressive. All 23 items of evaluation gained from over 3.5 to 4.2 on 5-point scale. It can be concluded that the teaching learning process plan for the practice of caring and sharing for the unit of 'Happy Family Life and Culture led by Family' would improve character of highschool students through the Home Economics subject.

• Journal of Korean Home Economics Education Association
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• v.26 no.1
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• pp.1-16
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• 2014

The purpose of this research is to develop a theme-based integrated teaching/learning plan in clothing, food, and housing in home economics and to apply the developed process in classes for evaluation in order to identify the suitability in schools. The theme-based integrated teaching/learning plan developed on the basis of textbooks consist four sub-themes; choosing($1^{st}$ and $2^{nd)$ lessons), using($3^{rd}$ lesson), processing ($4^{th}$ lesson), and alternatives($5^{th}$ and $6^{th}$ lessons) under the main theme of 'green family life'. The results from 20 individual and group activities showed that the students actively solved the problems when the presented cases were related to their own lives or experiences. The opportunity to implement green life through activities motivated students' willingness to proceed in real life. However, it is vital to assist integrated thinking through various examples before beginning due to students with difficulties connecting the issue from one area to the other during the problem-focused activity. The students' ability to solve the activity workbook had been improved as the lessons continued. From the survey questions on the theme-based integrated lessons, all items associated with integration of clothing, foods, and housing were positively responded. Also, questions regarding general understanding, suitability and satisfaction on the teaching/learning process were marked positive. The conclusion could be that the integrated theme related to clothing, food, and housing in our life would be appropriate for green family life. The theme-based integrated teaching/learning plan is effective in understanding the occurrence of green family life in relation with clothing, food, and housing, identifying the practical ideas implementing green life in those areas, and improving the integrated ability to solve the green life related problems. However, this research has its weakness in generalizing the results due to its limited survey respondents and post-evaluation being the only assessment conducted.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11B
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• pp.980-990
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• 2004

In this paper, we propose the dynamic bandwidth allocation algorithm for the IEEE802.3ah Ethernet Passive Optical Network(EPON) system to provide the fairness among terminals, and evaluate the delay-throughput performance by simulation. For the conventional EPON systems, an Optical Line Termination (OLT) schedules the upstream bandwidth for each Optical Network Unit (ONU), based on its buffer state. This scheme can provide a fair bandwidth allocation for each ONU. However, it has a critical problem that it does not guarantee the fair bandwidth among terminals which are connected to ONUs. For an example, we assume that the traffic from a greedy terminal increases at a time. Then, the buffer state of its ONU is instantly reported to the OLT, and finally the OW can get more bandwidth. As a result, the less bandwidth is allocated to the other ONUs, and thus the transfer delay of terminals connected to the ONUs gets inevitably increased. Noting that this unfairness problem exists in the conventional EPON systems, we propose a fair bandwidth allocation scheme by OLT with considering the buffer state of ONU as welt as the number of terminals connected it. For the performance evaluation, we develop the EPON simulation model with SIMULA simulation language. From the result of the throughput-delay performance and the dynamics of buffer state along time for each terminal and ONU, respectively, one can see that the proposed scheme can provide the fairness among not ONUs but terminals. Finally, it is worthwhile to note that the proposed scheme for the public EPON systems might be an attractive solution for providing the fairness among subscriber terminals.

• Korean Journal of Health Education and Promotion
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• v.2 no.1
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• pp.56-80
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• 1984

The main objectives of the present study is to evaluate Physician's Health Education Activities by means of physician's direct response to the prepared questionnaire and patient's perception to the physician in the course of medical care. For the data collection, the present study was conducted from Aug. 16 to Oct. 7, 1983 for 739 patients and 91 physicians who were attended outpatient clinics of 5 general hospitals in Seoul. The major findings are summarized as follows: 1. Self-evaluation on Physician's Health Education Activities (1) In consideration of health education services for the patient, the data revealed that 9.9% of the sampled physician wanted to strength public health and preventive medicine lecture in the curricula at medical education. On the other hand, only 1.1% expressed that they wanted to make it short. (2) In consideration of the necessity of health education service, it was shown that 95.6% of physicians agreed to take it into consideration. Self expression for the practice of health education was placed on the 3.15 score when 5 point scale used. (3) To evaluate the degree of an explanation about medical care for the patient, Index score with 4 point scale was employed. The Index score for the first time was shown that scale was placed on 3.23 for 'diagnosis', 3.12 for 'progress of the disease', 3.11 for 'discription of procedure' and 3.02 for 'cause of the disease' respectively. In comparison of the physician's explanation about the status of disease for the first and the second visitors to clinic, they evaluated themselves as giving more detailed explanation for the second visitors rather than the first visitors. 2. Physician's Health Education Services evaluated by patients (1) To evaluate physician-patient communication at beginning time for taking history about disease, the Index score with 5 point scale was employed. The data on taking history have shown that the score placed on 3.07 for those patients who visited the first time and 2.53 for second visitors. And the score about listening from the patients was placed on 3.52 and 3.42 respectively. (2) The Index score with 5 point scale, as used before, was also employed to evaluate medical care services for the patient. The data evaluated by the patients was shown that the score placed on 4.21 for patient treatment in general, 4.58 for physician's credibility, and 3.6 for physician's kindness. However, approximately 80% of those who failed to understand physician's explanation was caused by highly sophisticated medical terminology. (3) According to the Index score with 4 point scale, to evaluate physician's explanation, the data was shown that the patient who visited the first time gave 2.51 for 'diagnosis', 2.35 for 'progress', 2.11 for 'cause of the disease' and so on. It is acknowledged on the whole that the patients who visited the second time have more satisfaction in physician's explanation about their disease, than those who visited the first time. 3. Comparison of self-evaluation of Physician's Health Education Activities and patient's perception. (1) There was communication barriers between physicians and patients in expressing some medical terminology. For example physician understood that they explained more than 50% of medical terminology into common words for the patient, but 30% of patient complained medical terminology used by physician. (2) Comparing the index score of health education practice recognized by patients and physicians for both first visit and revisit groups, it was shown that the Index score of health education activities evaluated by physicians themselves were slightly higher than the score evaluated by patients.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.