• The Journal of Korean Philosophical History
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• no.29
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• pp.375-396
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• 2010

The aim of this paper is to suggest some ways of moral education for the building of children's social awareness and relationship skills as social and emotional competencies. Based on the social and emotional learning(SEL), this paper is tried to provide the effective ways to develop children's social awareness and relationship skill. According to SEL, social and emotional competence is the ability to understand, manage, and express the social and emotional aspects of one's life in ways that enable the successful management of life tasks such as learning, forming relationships, solving everyday problems, and adapting to the complex demands of growth and development. And it is also the process of acquiring and effectively applying the knowledge, attitudes, and skills necessary to recognize and manage emotions. Five key competencies such as self-awareness, social awareness, responsible decision making, self-management, relationship skills are taught, practiced, and reinforced through SEL programming. Moral education and social and emotional learning have emerged as two prominent formal approaches used schools to provide guidance for students' behavior. social awareness and relationship skills are necessary for succeeding in school, in the family, in the community, in life in general. Equipped with such skills, attitudes and beliefs, young children are more likely to make healty, caring, ethical, and responsible decisions and to avoid engaging in behaviors with negative consequences such as interpersonal violence and bullying.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.610-632
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• 2024

Mobile Learning (M-learning) adoption and success in supporting students' learning engagement mainly depend on many factors. Therefore, this study systematically reviews the literature, synthesizes and analyzes the predictors of M-learning adoption, and uses success for students' learning engagement. Literature from 2016 to 2023 in various databases is covered in this study. Based on the review's findings, the factors that influence students' learning engagement when it comes to M-learning usage and adoption, can be divided into technical, pedagogical, and social factors. More specifically, technical factors include mobile devices availability and quality, connectivity to the internet, and user-friendly interfaces, pedagogical factors include effective instructional design, teaching methods, and assessment strategies, and social factors include motivation of students, social interaction and perceived enjoyment - all these factors have a significant impact on the M-learning adoption and use success. The findings of the review also indicated that M-learning has a key role in enhancing the learning engagement of students through different ways, like increasing their motivation, attention, and participation in their process of learning, paving the way for interaction and building relationships opportunities with peers and instructors, which in turn, can lead to strengthening the learning environment. The implications of these findings extend beyond immediate educational contexts, offering vital insights for future educational technology strategies and policy decisions, particularly in addressing global educational challenges and embracing technological advancements in learning.

• Journal of Fashion Business
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• v.13 no.5
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• pp.93-101
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• 2009

The purpose of this study was to examine the effects of PBL (Problem based-learning) on the social psychology of clothing course. Thirty-seven undergraduate university students completed a 15-week capstone course in a clothing and textiles department. Eighty-one percent of the participants were majoring in the clothing and textiles. The study was conducted two focus group interview with 37 undergraduate students. The participants demonstrated positive attitude toward the PBL (Problem Based Learning) in Social Psychology of Clothing course. The results showed that the students have more opportunities to practice collaboration within the team and to increase their self-esteem and self-confidence through the 15 week of teamwork. The participants were developed to express their opinion actively and solving the problem skills. Eighty percent of the instructors had a positive attitude toward the achievement of the course objectives. Seventy-five percent of the instructors expressed the difficulty of student's evaluation. Fifty-five percent of the instructors had a difficulty of development of module problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.109-134
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• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.

• The Journal of Korean Association of Computer Education
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• v.15 no.2
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• pp.19-28
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• 2012

Recent popularity of smart devices and social media seem to increase much interests on social learning, Despite of positive expectation on technology-based social learning, there are not many successful cases and practices of how to apply hands-on technologies and measure educational results. In this study, we tried to promote idea-sharing among learners in a classroom using web-based peer-review of assignments on a specific topic. Then we investigated effects of idea-sharing among learners in terms of individual knowledge construction. Experimental results show that idea-sharing promotes knowledge convergence and divergence, and then knowledge construction at learner's own.

• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.35-54
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• 2010

Why does e-Learning service for individuals remain in the early development stage? To find the answers of this question, we adopted usefulness and intention to continued use as dependent variables based on technology acceptance model and inferred convenience, cost-effectiveness, social presence, interactivity, concentration, and procrastination as independent variables based on literature review and interview with e-Learning users. Convenience and cost-effectiveness of e-Learning tend to enhance usefulness and/or intention to continued use, while lack of social presence, interactivity, and concentration of e-Learning and academic procrastination tend to hinder usefulness and/or intention to continued use. To prove this research model, we used a data set collected from the survey. The respondents of survey were the undergraduate students who used voluntarily e-Learning. Data analysis was conducted using 275 respondents by partial least square. The analysis result of causal relation indicated that convenience and cost-effectiveness influenced both usefulness and intention to continued use, and that cost-effectiveness and concentration influenced only intention to continued use. But, interactivity and procrastination did not influence usefulness and intention to continued use.

• Asia pacific journal of information systems
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• v.25 no.4
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• pp.642-661
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• 2015

Interpersonal trust has become essential for online communities because people have managed to be in a situation without face-to-face encounters. To identify the structural relationships between interpersonal trust and learning performance, we analyzed the relationship between two types of trust, namely, cognitive and affective, as well as two dimensions of learning performance, namely, learning satisfaction and knowledge application. We also identified the moderating role of social norms in the relationship between trust and learning performance. Results of analysis are as follows. First, cognitive trust significantly affected the two dimensions of performance. Second, affective trust exhibited a significant effect on learning satisfaction, but did not affect knowledge application. Third, the relationships between the two performance factors were significant and direct. Lastly, social norms appeared to moderate the effects of cognitive trust on knowledge application and affective trust on satisfaction. These findings suggest that organizations, which would like to optimize task-oriented performance of their learning communities, should consider linking strategies between community satisfaction and practical knowledge application.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.585-595
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• 2017

This study investigates how the emotion-based learning motivation enhancement program influences learning motivation and social support of college students. The developed final program consists of Learning Motivation I, Learning Coaching, and Learning Motivation II, which has 12 sessions. In each session, every student was guided to have reflection time by writing self-evaluation and reflection paper. The participants were 38 students majoring in engineering at K-college located in G city who took one liberal arts subject based on psychology during the 1st semester in 2016 and who were divided into an experimental group (19 students) and a control group (19 students) by non-probability sampling method. In the experimental group, emotion-based learning motivation enhancement program was totally processed 12 times, one class in a week, by one main lecturer and one assistant lecturer. For data analysis, independent sample t-tests, paired samples t-tests, and review analysis were conducted. The study results are as follows. First, the experimental group participating in emotion-based learning motivation enhancement program had more significant differences in learning motivation, and both self-confidence and self-contentment among sub-components than the control group. Second, the experimental group had no significant differences in social support, compared with the control group. The impression writing analysis of the experimental group showed that this program affected learning motivation and social support. Lastly, the study discussions and implications are described.

• Journal of The Korean Association of Information Education
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• v.8 no.3
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• pp.321-332
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• 2004

Recently, learning how to learn is a matter of concern among many pedagogists and become the important subject matter of school education. That's why there are many learners who fail to get good results compared with their potential abilities and efforts, as well as the necessity to educate independent and self-determined learner by teaching learning strategies that can help leaners to get new information and skills by themselves in an information-oriented society was emphasized. Therefore, this study is to utilize the learning strategy in order to improve the learners study skills, and grope for the method of self-directed learning about the social studies centered on the learners. And, it is to utilize the reciprocal teaching with the learning strategy for improving the learners study skills. Also, it is to make the learners induced to participate in the teaching for social studies actively, grafting the characteristics of web on the reciprocal teaching. According to this intent, this study was developed as three steps. As for the first step, it investigated the reciprocal teaching, a teaching-learning theory to apply to the program, and the theoretical background about web utility in the social studies. On the second step, it embodied the reciprocal teaching-learning system about the we-based social studies that will be available in the self-directed learning centered on the learners. Lastly, it analysed the change of recognition about the learners social study skills and the learning the social studies after applying the materialized website to the actual lesson.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.