• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.233-240
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• 2011

Social Media is the open online tool and media platform for sharing and participation of users opinion, experience, viewpoiont, so general situation that is one-side flowing from production to consume doesn't act, and while use of two-way, user create contents use of sharing and participation. This social media include Blog, Social Network Service(SNS), Wiki, User Create Contents(UCC), Micro Blog, 5 types. In broad terms, Social Learning is self-learning that user sharing with coperation and collective intelligence through Social Media, and in few wards Social Learning is learning for Social Media. In this research, we define Social Media and Social Learning, and research of method of use of Elementary Education.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.180-185
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• 2012

Social Learning is a new study model of future knowledge information society. In different existing study, it concentrate on relationship with others and design to connect studying with social effect as a study platform using social media such as Blog, SNS, UCC, Microblog. In my paper, social learning characteristics are described to understand social learning, that is 3 keyword such as context, connectivity, collaboration. Also we investigate social media characteristics and social media how to be used social learning. Also social learning system building method using facebook is presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.189-194
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• 2013

Social learning is a new study model of future knowledge information society. In different existing study, it lay stress on individual activity and collaborative study with others. It is useful to apply social media services to build social learning platform for collaborative study. In my paper, after existing social media services and social platforms are investigated and analyzed, an effective social learning platform applyng social media services is presented. Also differences and superiority compared to other social platforms is presented through new social learning platform architecture and screen design.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.495-509
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• 2011

According to the rapid technological development such as ubiquitous environments, there has been growing interest in learning with social media as known as social learning. This study was conducted to analyze various participation types of social media contents aiming to explore strategies for facilitating learning. Specifically, the research model was established by two aspects in using social media contents. First was classified by writings and readings in contents, which consists of prosumers, producers, consumers, and non-participants. Second criterion was categorized by instruction-related and instruction-nonrelated, which is learning contents, learning management, emotional expression, and social activities. In order to acquire empirical data, a set of fourteen undergraduate students participated in this research for eight weeks using a microblog. Based on the analyses on the data through learning activities, three learning strategies were suggested to facilitate social media based learning: analysis on learners, role of the instructor, and instructional model design.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.358-368
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• 2022

Social media is a window for everyone, individuals, communities, and companies to spread ideas and promote trends and products. With these opportunities, challenges and problems related to security, privacy and rights arose. Also, the data accumulated from social media has become a fertile source for many analytics, inference, and experimentation with new technologies in the field of data science. In this chapter, emphasis will be given to methods of trend analysis, especially ensemble learning methods. Ensemble learning methods embrace the concept of cooperation between different learning methods rather than competition between them. Therefore, in this chapter, we will discuss the most important trends in ensemble learning and their applications in analysing social media data and anticipating the most important future trends.

• The Journal of Information Systems
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• v.28 no.4
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• pp.293-311
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• 2019

Purpose This paper is to make a meta-analysis of the relationship between the social media use and learning performance as well as its potential moderating variables to clarify the differences in research conclusions in existing literatures, and refine the situational and method factors that affect the relationship between them. Methodology Meta-analysis used in this study can combine the quantitative data from different empirical studies, focus on the same research problem, and finally reach a research conclusion. Findings The results show that social media use and learning performance have a moderating positive correlation. The moderating effect test of usage scenarios shows that social media types, usage groups, application platforms and discipline fields have moderating effects on the relationship between social media use and learning performance. The moderating effect test of the research method found that measurement models, data attributes and learning performance indicators also had moderating effects on the relationship between social media use and learning performance.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.27-34
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• 2017

As information technology has been developed in a rapid way, a lot of users get to be familiar with social media. Accordingly, the possibility of social media for educational use has increased. From the view point of learning, social media help learners make communities of practice that can lead to collective intelligence. In this study, two different types of social media, synchronous and asynchronous, were compared in terms of usage patterns in the e-learning settings of college level. Content analysis has figured out four factors: learning content, tasks and assignments, emotional communications, and chatting. There found to be a statistical differences in the postings in all of the factors except tasks and assignments. In the qualitative interviews, the participants told various usage patterns of synchronous and asynchronous social media. In sum, the learners generally preferred synchronous social media. Rather, asynchronous social media were mainly used for deep thinking and summarizing. Last, suggestions were made to improve educational environments for the learners in the digital and social media age.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.523-534
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• 2015

Social learning is a form to support learners' active engagement and participation in learning with other learners and instructors by using social media. The concept of social learning should be considered beyond the simple use of social media for learning or education. This study aims to apply the understanding of social learning based on the theoretical background of social theories of learning in designing and developing a program for workforce education. As a pilot test, the newly developed social learning program was implemented to 302 employees with the title of 'Innovative Display Strategy for POP". 138 employees successfully completed the social learning course that focuses on delivering contents in time-line based platform, supporting interactions among students, and working effectively through small smart devices in their workplace. The results were derived from three kinds of data-source: learner's log data, their final evaluation score, and the survey to measure the satisfaction about social learning. Finally the implications for social learning were discussed in terms of the program revision and directions for future application.

• Journal of Information Science Theory and Practice
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• v.12 no.1
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• pp.39-59
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• 2024

An online social network is a platform that is continuously expanding, which enables groups of people to share their views and communicate with one another using the Internet. The social relations among members of the public are significantly improved because of this gesture. Despite these advantages and opportunities, criminals are continuing to broaden their attempts to exploit people by making use of techniques and approaches designed to undermine and exploit their victims for criminal activities. The field of digital forensics, on the other hand, has made significant progress in reducing the impact of this risk. Even though most of these digital forensic investigation techniques are carried out manually, most of these methods are not usually appropriate for use with online social networks due to their complexity, growth in data volumes, and technical issues that are present in these environments. In both civil and criminal cases, including sexual harassment, intellectual property theft, cyberstalking, online terrorism, and cyberbullying, forensic investigations on social media platforms have become more crucial. This study explores the use of machine learning techniques for addressing criminal incidents on social media platforms, particularly during forensic investigations. In addition, it outlines some of the difficulties encountered by forensic investigators while investigating crimes on social networking sites.

• 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.