• Korean Journal of Child Studies
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• v.29 no.6
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• pp.15-33
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• 2008

The purpose of this study was to investigate which variables predicted preschoolers' pro-social behavior. Subjects consisted of 250 preschooler-mother pairs; children were 3 to 6 years of age Children's emotional intelligence and pro-social behavior were assessed by classroom teachers. Data on the mothers' socialization beliefs and strategies as well as children's temperament were gathered through maternal self-reported questionnaires. Data were analyzed by descriptive statistics, t-tests, correlation and regression analyses. Results showed that children's temperament (withdrawal predisposition) and emotional intelligence (self emotional expression) predicted children's pro-social behavior. Mothers' perceptions of the importance of pro-social behavior correlated negatively with children's pro-social behavior.

• Journal of the Korean Home Economics Association
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• v.44 no.10
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• pp.1-8
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• 2006

This study examined effects of emotional intelligence and satisfaction of school life on the social competence of institutional children. Data were collected from institutional children of 300 10-12 year old and questionnaire measures were employed. Data were analyzed by t-test, pearson's correlation and multiple regression analyses. Relationships were found between emotional intelligence and social competence as well as satisfaction of school life and social competence. Components of emotional intelligence and satisfaction of school life that predicted social competence were emotional facilitation of thinking and personal relationship between friends. Degree of personal relationship between friends was the most powerful predictor of institutional children's social competence.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.73-86
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• 2012

This paper theoretically derives an algorithm to discover a new type of social networks from workflow models, which is termed workflow-based social network intelligence. In general, workflow intelligence (or business process intelligence) technology consists of four types of techniques that discover, analyze, monitor and control, and predict from workflow models and their execution histories. So, this paper proposes an algorithm, which is termed ICN-based workflow-based social network intelligence discovery algorithm, to be classified into the type of discovery techniques, which are able to discover workflow-based social network intelligence that are formed among workflow performers through a series of workflow models and their executions, In order particularly to prove the correctness and feasibility of the proposed algorithm, this paper tries to apply the algorithm to a specific workflow model and to show that it is able to generate its corresponding workflow-based social network intelligence.

• Korean Journal of Child Studies
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• v.27 no.3
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• pp.225-240
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• 2006

This study examined the effects of mother's facilitative communication and child's emotional intelligence on social competence. Subjects were 474 students in the $5^{th}$ and $6^{th}$ grades of two elementary schools located in Seoul. Data were analyzed by the SAS statistics program. Findings were that mother's facilitative communication has an effect on child's social competence and on emotional intelligence. Higher perception of child's own emotional ability was associated with higher sociability, social participation and leadership. Increased perception of the emotional ability of others was associated with higher sociability, interpersonal adjustment, social participation, leadership, and popularity.

• Journal of the Korean Society of Food Culture
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• v.29 no.1
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• pp.18-25
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• 2014

The purposes of this study were to understand the influence of social intelligence and positive psychology capital in foodservice employees on job satisfaction and to analyze whether employees' positive psychology capital plays a moderating role between social intelligence and job satisfaction. Based on total 316 samples obtained from empirical research, this study reviewed the reliability and verified a total of 2 hypotheses and 1 proposition using the SPSS program. The results showed that social awareness (${\beta}$=0.546) and social skill (${\beta}$=0.152) in foodservice employees, were indicated to have positive (+) influence upon job satisfaction. And, employees' positive psychology capital (${\beta}$=0.307) had a significant positive (+) effect on the job satisfaction. However, the effect of the employees' social intelligence upon job satisfaction was not found to be moderated by positive psychology capital. Limitations and future research directions are also discussed.

• Korean Journal of Child Studies
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• v.26 no.6
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• pp.335-349
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• 2005

The subjects in this study of children's playfulness in relation to emotional intelligence and social competence were 247 children in Gyeonggi Province. Data were analyzed by regression analysis using the SPSS computer program. The major results of this study were that children's playfulness was significantly related to and explained by "Peer relations skill" in the sub areas of social competence and "relationship with peers," utilization of emotion" in sub areas of emotional intelligence. Emotional intelligence and socially competent interaction were significantly related to and explained by manifest joy in the sub areas of children's playfulness.

• Journal of Engineering Education Research
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• v.25 no.3
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• pp.35-44
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• 2022

Artificial intelligence is a core technology in the era of digital transformation, and as the technology level is advanced and used in various industries, its influence is growing in various fields, including social, ethical and legal issues. Therefore, it is time to raise social awareness on ethics of artificial intelligence as a prevention measure as well as improvement of laws and institutional systems related to artificial intelligence development. In this study, we analyzed unstructured data, typically text, such as online news articles and comments to confirm the degree of social awareness on ethics of artificial intelligence development. The analysis showed that the public intended to concentrate on specific issues such as "Human," "Robot," and "President" in 2018 to 2019, while the public has been interested in the use of personal information and gender conflics in 2020 to 2021.

• Journal of Information Technology Applications and Management
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• v.30 no.1
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• pp.53-70
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• 2023

Artificial intelligence has become familiar with modern society, not the distant future. As artificial intelligence and machine learning developed more highly and became more complicated, it became difficult for people to grasp its structure and the basis for decision-making. It is because machine learning only shows results, not the whole processes. As artificial intelligence developed and became more common, people wanted the explanation which could provide them the trust on artificial intelligence. This study recognized the necessity and importance of explainable artificial intelligence, XAI, and examined the trends of XAI research by analyzing social networks and analyzing topics with IEEE published from 2004, when the concept of artificial intelligence was defined, to 2022. Through social network analysis, the overall pattern of nodes can be found in a large number of documents and the connection between keywords shows the meaning of the relationship structure, and topic modeling can identify more objective topics by extracting keywords from unstructured data and setting topics. Both analysis methods are suitable for trend analysis. As a result of the analysis, it was found that XAI's application is gradually expanding in various fields as well as machine learning and deep learning.

• Journal of the Korean Society of Earth Science Education
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• v.7 no.1
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• pp.24-33
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• 2014

The purpose of this study was to determine the discrete intelligences from multiple intelligence affecting the social adaptive behavior, help to understand their relation and draw educational implications to be used in supporting gifted students who have social and emotional difficulties by comparing and analyzing the relation between multiple intelligence and social adaptive behavior of gifted and general elementary students. The conclusions of this study are as follows. First, the levels of both multiple intelligence and social adaptive behavior were significantly higher in gifted elementary students compared to general ones on all sub-factores, indicating that the gifted elementary students are more adaptive in such constructs as self-efficacy, self-esteem, communicative skill, school life and interpersonal skill compared to general ones. Second, the association between multiple intelligence and social adaptive behavior was statistically significant both in gifted and general elementary students, indicating that the two constructs have close relation with each other. Third, for the gifted elementary students, the logical-mathematical and interpersonal intelligences had explanatory powers for self-efficacy, self-esteem, communicative skill, adaptation in school life, interpersonal skill while, for the general ones, intra- and inter-personal intelligences had explanatory powers for most domains of social adaptive behaviors, indicating that development of intelligences affecting the social adaptive behavior many have positive effects on social and emotional development of both gifted and general elementary students.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2966-2986
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• 2023

Climate change is a constant threat to human life, and it is important to understand the public perception of this issue. Previous studies examining climate change have been based on limited survey data. In this study, the authors used big data such as news articles and social media data, within which the authors selected specific keywords related to climate change. Using these natural language data, topic modeling was performed for discourse analysis regarding climate change based on various topics. In addition, before applying topic modeling, sentiment analysis was adjusted to discover the differences between discourses on climate change. Through this approach, discourses of positive and negative tendencies were classified. As a result, it was possible to identify the tendency of each document by extracting key words for the classified discourse. This study aims to prove that topic modeling is a useful methodology for exploring discourse on platforms with big data. Moreover, the reliability of the study was increased by performing topic modeling in consideration of objective indicators (i.e., coherence score, perplexity). Theoretically, based on the social amplification of risk framework (SARF), this study demonstrates that the diffusion of the agenda of climate change in public news media leads to personal anxiety and fear on social media.