• 대한공업교육학회지
• /
• v.44 no.1
• /
• pp.162-189
• /
• 2019

The purpose of this study is to develop and apply a Design Thinking-based Maker education program utilizing Arduino for middle school students. The study progress was made in four stages of preparation, development, implementation and evaluation according to the PDIE model. In this study, experts were verified for validity and pre-applied to students to improve the maker education program developed based on literature review. Then, it was applied to middle school club classes to check the effects through analysis of quantitative and qualitative data. In addition, the development of the program was completed by supplementing the improvements found in the course. The results of this study are as follows. First, the topics of the maker education program that can be used in middle schools were selected in consideration of the analysis of the 2015 revised curriculum, methods to using the Arduino, and social interest. Second, the program developed based on the selected topic consists of 4 classes of maker basic learning and 16 classes of design thinking-based maker activities. Third, the developed maker education program had a significant effect in improving STEAM literacy of middle school students, but did not have any significant effect in the interest in technology and orientation towards an engineering career. Fourth, learners were interested in the activities of designing and freely making by themselves, and they positively evaluated the experience of realizing the physical computing with Arduino. In addition, they practiced the spirit of a maker, such as autonomously collecting data and sharing them with colleagues, etc. while acting as a maker.

• Journal of Internet Computing and Services
• /
• v.20 no.3
• /
• pp.43-59
• /
• 2019

Today, with the spread of smartphones and the development of social networking services, structured and unstructured big data have stored exponentially. If we analyze them well, we will get useful information to be able to predict data for the future. Large amounts of data need to be collected first in order to analyze big data. The web is repository where these data are most stored. However, because the data size is large, there are also many data that have information that is not needed as much as there are data that have useful information. This has made it important to collect data efficiently, where data with unnecessary information is filtered and only collected data with useful information. Web crawlers cannot download all pages due to some constraints such as network bandwidth, operational time, and data storage. This is why we should avoid visiting many pages that are not relevant to what we want and download only important pages as soon as possible. This paper seeks to help resolve the above issues. First, We introduce basic web-crawling algorithms. For each algorithm, the time-complexity and pros and cons are described, and compared and analyzed. Next, we introduce the state-of-the-art web crawling algorithms that have improved the shortcomings of the basic web crawling algorithms. In addition, recent research trends show that the web crawling algorithms with special purposes such as collecting sentiment words are actively studied. We will one of the introduce Sentiment-aware web crawling techniques that is a proactive web crawling technique as a study of web crawling algorithms with special purpose. The result showed that the larger the data are, the higher the performance is and the more space is saved.

• Journal of Internet Computing and Services
• /
• v.20 no.4
• /
• pp.81-90
• /
• 2019

Since 2015, the government has been striving to strengthen the software capabilities required for future talent through software-oriented university in Korea. In the university selected as a software-oriented university, basic software education is given to all departments such as humanities, social science, engineering, natural science, arts and the sports within the university in order to foster convergent human resources with different knowledge and software literacy. In this paper, we analyze the contents of basic software education for twenty universities selected as software-oriented universities. As a result of analysis, most of the basic software education which is carried out to the students of the non-majors students was aimed at improvement of problem solving ability centered on computational thinking for future society and improvement of convergence ability based on computer science. It uses block-based educational programming language and text-based advanced programming language to adjust the difficulty of programming contents and contents reflecting characteristics of each major. Problem-based learning, project-based learning, and discussion method were used as the teaching and learning methods for problem solving. In the future, this paper will help to establish the systematic direction for basic software education of non-majors students.

• Journal of the Korean Institute of Gas
• /
• v.22 no.6
• /
• pp.136-143
• /
• 2018

Cellular automata have been applied to simulations in many fields such as astrophysics, social phenomena, fire spread, and evacuation. Using cellular automata, this study develops a model for consequence analysis of the dispersion of hazardous chemicals, which is required for risk assessments of and emergency responses for frequent chemical accidents. Unlike in cases of detailed plant safety design, real-time accident responses require fast and iterative calculations to reduce the uncertainty of the distribution of damage within the affected area. EPA ALOHA and KORA of National Institute of Chemical Safety have been popular choices for these analyses. However, this study proposes an initiative to supplement the model and code continuously and is different in its development of free software, specialized for small and medium enterprises. Compared to the full-scale computational fluid dynamics (CFD), which requires large amounts of computation time, the relative accuracy loss is compromised, and the convenience of the general user is improved. Using Python open-source libraries as well as meteorological information linkage, it is made possible to expand and update the functions continuously. Users can easily obtain the results by simply inputting the layout of the plant and the materials used. Accuracy is verified against full-scale CFD simulations, and it will be distributed as open source software, supporting GPU-accelerated computing for fast computation.

• Journal of the Korea Convergence Society
• /
• v.10 no.7
• /
• pp.15-21
• /
• 2019

Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.

• Journal of Digital Convergence
• /
• v.17 no.8
• /
• pp.15-26
• /
• 2019

This study was to explore the domestic research trends, and education and operation cases concerned with Korean colleges in the fourth industrial revolution era. It was conducted through the analysis of 114 academic papers registered to the Korea Research Foundation, the newspaper articles, and the main 4-year university homepage from 2016 to April 2019. The results was as follows. Research papers have been increasing since 2016; research were conducted by humanities and social sciences as well as engineering academics interesting in research topics such as technologies, curriculum, and teaching and learning by mainly using quantitative research, literature research. As for the college education, reorganization of the undergraduate and majors centered on the science and engineering field, teaching and learning related with learner's participation and performance, and provide efficient academic affairs management and career guidance using Chatbot or Cloud computing. Industry-academia cooperation was focuses on the field of science and engineering. In future research, it is necessary to explore the research on college students' career and employment, the research on academic affairs management and infrastructure, the relational research considering the variables among college students and faculties, and the qualitative and mixed method approach.

• The Journal of the Korea Contents Association
• /
• v.19 no.5
• /
• pp.187-193
• /
• 2019

Key-value storage engines are an essential component of growing demand in many computing environments, including social networks, online e-commerce, and cloud services. Recent key-value storage engines offer many features such as transaction, versioning, and replication. In a key-value storage engine, transaction processing provides atomicity through Write-Ahead-Logging (WAL), and a synchronous commit method for transaction processing flushes log data before the transaction completes. According to our observation, flushing log data to persistent storage is a performance bottleneck for key-value storage engines due to the significant overhead of fsync() calls despite the various optimizations of existing systems. In this article, we propose a group synchronization method to improve the performance of the key-value storage engine. We also design and implement a transaction scheduling method to perform other transactions while the system processes fsync() calls. The proposed method is an efficient way to reduce the number of frequent fsync() calls in the synchronous commit while supporting the same level of transaction provided by the existing system. We implement our scheme on the WiredTiger storage engine and our experimental results show that the proposed system improves the performance of key-value workloads over existing systems.

• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.57-69
• /
• 2020

As more people share their opinions in online communities, such as Internet portals and social networking services, more opinions are manipulated for the benefit of particular individuals and groups. In particular, when manipulations occur for political purposes, they influence election results as well as government policies and the quality of life. This type of manipulation has targeted the general public, and their analysis and detection has also focused on such manipulation. However, to more efficiently spread propaganda, recent manipulations have targeted common interest groups(e.g., a group of those interested in real estate) and propagated information whose content and style are customized to those groups. This work characterizes such manipulations on common interest groups and proposes method to detect manipulations. To this end, we collected and analyzed opinions posted on 10 common interest groups before and after an election. As a result, we found that manipulations on common interest groups indeed occurred and were gradually increasing toward the election date. We also proposed a detection system that examines individual opinions, their authors, and their collaborators. Using the collected opinions, we demonstrated that the proposed system can accurately classify more than 90% of manipulated opinions and that many of these opinions were posted by multiple collaborators. We believe that regular audits of opinions using the proposed system can quickly isolate manipulations and decrease their impact. Moreover, the proposed features can be used to identify manipulations in domains other than politics.

• Journal of the Korea Convergence Society
• /
• v.12 no.3
• /
• pp.177-186
• /
• 2021

The 2015 revised curriculum is structured around the core competencies of the 21st century, this is in line with the world's flow of education, such as OECD Education 2030. A future practical leading model was studied to provide a variety of creative teaching and learning experiences to elementary and Secondary students using intelligent information technology to cultivate core competencies such as ICT and computing thinking. In order for this practical model to stably settle the school field, the training was planned and operated to strengthen the creative convergence education capacity required by the teachers at the unit school through various types of the training. In particular, a nationwide administrators training program was operated for three years, reflecting the new curriculum, teaching and learning methods, and evaluation that can lead to future convergence talent training. In this paper, the perception of creative convergence education was investigated and analyzed considering the influence that administrators may have on the school field. Based on this, through the three-year operation results of the training, it was intended to establish a new training method for stable access to future creative convergence education under the post-corona era's social issues.

• Journal of Internet Computing and Services
• /
• v.23 no.4
• /
• pp.21-33
• /
• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.