• Journal of Families and Better Life
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• v.30 no.4
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• pp.59-75
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• 2012

The purpose of the study shall be to achieve an understanding of learning and emotional support services for adolescents being raised by their grandparents. In-depth interview and qualitative methodology were used to find changes in the service experiences of 10 adolescents being raised by their grandparents by analyzing their experiences at a health family support center. 1 agency in the Chungnam area was selected as a model for its program for adolescents being raised by their grandparents. Ultimately, 78 items as sub-concepts, 44 items as sub-categories, and 4 items as subjects were identified. Specifically, these included school achievement, peer group relationship, family relationship and significant others. In conclusion, they were found to experience slower physical and emotional development and tend to withdraw in social situations. They were also found to have experienced difficulties in communicating with other people and with school achievement. However, it was shown that these adolescents have made positive changes after participating in a program involving a family coach who supports and provides services for them. Also, they were found to have experienced psychologically changes, and improved in their school achievement and personal relationships. Consequently, we will require more effort to provide emotional support, adult role models, counseling intervention, and social support for them.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.139-152
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• 2012

In the medical web forum, people share medical experience and information as patients and patents' families. Some people search medical information written in non-expert language and some people offer words of comport to who are suffering from diseases. Medical web forums play a role of the informative support and the emotional support. We propose the automatic classification model of articles in the medical web forum into the information support and emotional support. We extract text features of articles in web forum using text mining techniques from the perspective of linguistics and then perform supervised learning to classify texts into the information support and the emotional support types. We adopt the Support Vector Machine (SVM), Naive-Bayesian, decision tree for automatic classification. We apply the proposed model to the HealthBoards forum, which is also one of the largest and most dynamic medical web forum.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.3
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• pp.38-53
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• 2022

Recently, the acquisition and production of information using digital tools and the creation of new knowledge are emphasized as important educational elements. Therefore, in this study, the effect of learning achievement according to the digital literacy level of college students was analyzed. For the analysis, a questionnaire is conducted with college students majoring in aviation services attending universities in Seoul Capital Area and Chungcheong area. To verify the hypothesis of the study, demographic characteristics are identified based on the questionnaire, reliability and validity of measurement items are verified, and structural equation model analysis is performed to verify the hypothesis. The analysis results are as follows. First, among the sub-factors of digital literacy competency of college students majoring in aviation service, 'technology use' is found to have a positive effect on 'cognitive flow' and 'emotional flow' of learning flow except 'behavioral flow'. Second, among the sub-factors of digital literacy competency, 'self-learning' is found to have a positive effect on 'cognitive flow', 'emotional flow', and 'behavioral flow' in learning flow. Third, the sub-factors of learning flow, 'cognitive flow', 'emotional flow', and 'behavioral flow' have a positive effect on 'learning satisfaction'. Fourth, 'learning satisfaction' is found to have a positive effect on 'learning outcomes'. Based on the research results, practical support measures and strategies for educational success are presented.

• Research in Community and Public Health Nursing
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• v.25 no.4
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• pp.270-281
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• 2014

Purpose: The purpose of the study was to understand low-income elders' experiences of community-based u-Health services. Methods: Qualitative data were collected from 11 participants. All interviews were recorded and transcribed verbatim. The transcribed data were analyzed using qualitative content analysis. Results: Three themes and eight sub-themes emerged as a result of analysis. The three main themes were 'recovered confidence and health condition,' 'trial and error in change,' and 'hope.'The eight sub-themes were 'the burden and efforts to overcome it in using bio-signal device,' 'ambivalence due to changing lifestyle,' 'increase of care time, decrease of pressure', 'conflict under environmental constraints,' 'difficulty in prioritizing health management,' 'discouragement in handling new devices,' 'desire not to be a burden to their children-gradual fulfillment of learning needs,' and 'long for broadening coverage range of services.' Conclusion: The findings of this study demonstrate that low-income elders among the participants have different needs in using u-Health services. Therefore, health professionals need to give personalized education to deal with their conflicts and requirements, especially emotional and environmental support in order for them to successfully accept the u-Health services for self-care.

• Journal of Integrative Natural Science
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• v.13 no.3
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• pp.97-104
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• 2020

This study suggests the emotion space modeling and emotion inference methods suitable for personalized services based on psychological and emotional models. For personalized emotion space modeling taking into account the subjective disposition based on the empirical assessment of the personal emotions felt by the personalization process of emotion space was used as a decision support tool, the Analytic Hierarchy Process. This confirmed that the special learning to perform personalized emotion space modeling without considering the subjective tendencies. In particular to check the possible reasoning based on fuzzy emotion space modeling and sensitivity for the quantification and vague human emotion to it based on the inherent human sensitivity.

• Communications of Mathematical Education
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• v.34 no.4
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• pp.545-561
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• 2020

The purpose of this study is to investigate the applications and possibilities of major programs that provide services using artificial intelligence in mathematics education. For this study, related papers, reports, and materials were collected and analyzed, focusing on materials mostly published within the last five years. The researcher searched the keywords of "artificial intelligence", "artificial intelligence", "AI" and "mathematics education" independently or in combination. As a result of the study, artificial intelligence for mathematics education was mostly supporting learners' personalized mathematics learning, defining it as an auxiliary role to support human mathematics teachers, and upgrading the technology of not only cognitive aspects but also affective aspects. As suggestions, the researcher argued that followings are necessary: Research for the establishment of an elaborate artificial intelligence mathematical system, discovery of artificial intelligence technology for appropriate use to support mathematics education, development of high quality of mathematics contents for artificial intelligence, and the establishment and operation of a cloud-based comprehensive system for mathematics education. The researcher proposed that continuous research to effectively help students study mathematics using artificial intelligence including students' emotional or empathetic abilities, and collaborative learning, which is only possible in offline environments. Also, the researcher suggested that more sophisticated materials should be developed for designing mathematics teaching and learning by using artificial intelligence.

• 한국노년학
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• v.41 no.4
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• pp.587-609
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• 2021

The purpose of this study was to investigate parenting experiences among grandmothers raising their grandchildren from grandmothers' perspective, and a variety of their physical health, psychological and social challenges they were facing in everyday life. In addition, this study explored new issues, changes, and difficulties grandparents and their grandchildren were going through during the COVID-19 pandemic. Seven grandmothers raising their grandchildren without their cohabiting parents participated in an in-depth interview, and the qualitative date were obtained using semi-structured questionnaires. Analyses identified 5 main categories: 1) my emotion, worries, and coping with parenting grandchildren, 2) difficulties and obstacles facing in real life of the parenting, 3) conflicts and coping with growing grandchildren who showed new characters, 4) relationships and emotions among grandparents, parents, and grandchildren, and 5) needs and desires toward social services and support. Sixteen themes and 60 sub-themes were also derived. The majority of grandmothers expressed diverse difficulties in their dail y lives including ambivalent emotions regarding grandchild-rearing(rewards and burden), economic hardships, physical health limitations, and a lack of communications with their grandchildren. Further, findings indicated profound generation conflicts which had been even deepened during school close period in COVID-19 pandemic and had been associated with increased hours of using internet and playing computer games. The top priority of the social service needs among interviewed grandmothers was learning support for their grandchildren. Emotional support and social support to cover their lack of family interactions, and financial support were the next of their desired social services. Implications to improve social services for grandparent-headed families are discussed.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.63-72
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• 2018

The human voice is one of the easiest methods for the information transmission between human beings. The characteristics of voice can vary from person to person and include the speed of speech, the form and function of the vocal organ, the pitch tone, speech habits, and gender. The human voice is a key element of human communication. In the days of the Fourth Industrial Revolution, voices are also a major means of communication between humans and humans, between humans and machines, machines and machines. And for that reason, people are trying to communicate their intentions to others clearly. And in the process, it contains various additional information along with the linguistic information. The Information such as emotional status, health status, part of trust, presence of a lie, change due to drinking, etc. These linguistic and non-linguistic information can be used as a device for evaluating the individual's credit worthiness by appearing in various parameters through voice analysis. Especially, it can be obtained by analyzing the relationship between the characteristics of the fundamental frequency(basic tonality) of the vocal cords, and the characteristics of the resonance frequency of the vocal track.In the previous research, the necessity of various methods of credit evaluation and the characteristic change of the voice according to the change of credit status were studied. In this study, we propose a personal credit discriminator by machine learning through parameters extracted through voice.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.382-395
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• 2017

This study attempted to examine how sexually abused women with intellectual disability living in residential care facilities interact with their environment, such as personal life, family and communities, and to identify contextual characteristics and needs of these women. Qualitative case study method was adopted, and data was collected through individual and intake interviews, participant observation and a survey with 11 participants residing in a residential care facility for sexually abused women with intellectual disability. The results showed that participants struggled with stresses and emotional instability affected by traumatic experiences of sexual abuse. Family was a system that sexual abused took place, while the systems of residential facilities protected them from potential dangers and violence. Work and school systems also provided them opportunities of learning and having dreams in the future. However, the community system which participants would live after discharging from the facility, had risk factors vulnerable to sexual violence against participants. In conclusion, this study suggested diverse services and programs, such as professional psychotherapy programs, integrated support programs for victim and their families and provision of professional care facilities.