• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.105-111
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• 2018

STEAM and S / W education in public education are effective in fostering talented people and the talents of the 4th industrial revolution era. It is necessary to expand the teachers for this purpose, to find out and apply various learning materials, and to improve education environment for fusion talent education. An open learning platform is effective in reducing private education costs and supplementing public education. Especially, it is useful for flip learning combined with classroom (off-line). In this case, teacher's role can be transformed into active teaching activities and research activities, which can speed up normalization of public education and reduce private education.In particular, the core functions of the MOOC platform for elementary education are 'creative instructional design and contents development function', 'digital teaching and learning curation', 'big data based learner customization', 'learning participation' flip learning and social Learning function.Through this study, it is expected that discussion on the introduction of MOOC for career and admission education for adolescents including elementary education will be established and the Korean youth MOOC platform will be developed and developed as a global advanced model of education democratization.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.11-21
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• 2020

This study is a phenomenological study that aimed to understand the meaning of nursing students' experience of class using flipped learning method. The participants are 8 senior nursing students. The data collected by individual in-depth interviews and analyzed by Colaizzi's method. As a result of this study, 35 key themes and 11 clusters of themes were derived. The 11 thematic categories are classified in pre-education, in-classroom, and post-education. At the pre-education, the theme clusters are 'lack of information', 'psychological burden', 'different teaching methods', 'improvement of self-directed learning ability', and 'different learner's achievement'. At the in-class, the theme clusters are 'efficient teaching direction' and 'confidence improvement'. At the post-education stage, the theme clusters are 'positive influence on class', 'strengthening self-pay', 'not preferred', and 'lecture preference'. Therefore, a more diversified and in-depth repetitive study is suggested in order to apply the flipped learning method to the nursing major.

• Journal of The Korean Association of Information Education
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• v.22 no.1
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• pp.151-158
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• 2018

The purpose of this study is to develop a system for predicting students' collaborative problem solving ability based on the ICT factors of PISA 2015 that affect collaborative problem solving ability. The PISA 2015 computer-based collaborative problem-solving capability evaluation included 5,581 students in Korea. As a research method, correlation analysis was used to select meaningful variables. And the collaborative problem solving ability prediction model was created by using the deep learning method. As a result of the model generation, we were able to predict collaborative problem solving ability with about 95% accuracy for the test data set. Based on this model, a collaborative problem solving ability prediction system was designed and implemented. This research is expected to provide a new perspective on applying big data and artificial intelligence in decision making for ICT input and use in education.

• Design Convergence Study
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• v.16 no.5
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• pp.125-140
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• 2017

This study explores potential capability of a machine learning approach for monitoring urban morphology based on an evident case study. The case study conveys year 2006 investigations on interpreting urban morphology of Boston Main Streets by applying a machine learning approach. From the lesson of the precedent study, in 2016, another field research and interview was conducted to compare changes in urban situation, data commons culture, and technology innovation during the decade. This paper describes open possibilities to advance urban monitoring for morphological changes. Most of all, a multi-participatory data platform enables managing urban data system in real time. Second, collaboration with machines with artificial intelligence can intervene the framework of the urban management system as well as transform it through new demands of innovative industries. Recently, urban regeneration became a dominant urban planning strategy in Korean, therefore, urban monitoring is on demand. It is timely important to correspond to in-situ problems based on empirical research.

• Information Systems Review
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• v.22 no.1
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• pp.1-16
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• 2020

Blockchain is emerging as a technology that can build trust between users participating in the system. As interest of Blockchain has increased, previous studies have mainly focused on cryptocurrency and application methods related to Blockchain technology. On the other hand, the studies on the stable implementation of Blockchain were rarely conducted. Typically, uncle block in the Blockchain plays an important role in the stable implementation of the Blockhain system, but no study was conducted on this. Drawing on this recognition, this study attempts to predict the uncle block of Blockchain using machine learning method, Blockchain information, and macro-economic factors. The results of artificial neural network and support vector machine analysis, Blockchain information and macro-economic factors contributed to the prediction of uncle block of Blockchain. In addition, artificial neural network using only Blockchain information provided the best performance for predicting the occurrence of uncle block. This study suggests ways to lead and contribute to Blockchain research in information systems filed.

• Journal of Science Education
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• v.40 no.1
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• pp.52-71
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• 2016

The purpose of this study is to analyze the learning activities which use asexual reproduction learning application(app) of students with special needs through the Cultural Historical Activity(CHAT). The asexual reproduction learning app was developed for students with special needs, and analyzed the learning activities of students with special needs in perspective of CHAT. The app was developed as subsidiary study material for asexual reproduction inquiry learning. Main functions of the app were composed of concept learning, problem solving, video playing, and report writing. According the CHAT analysis, findings indicated that students with special needs as the subject were able to organize division of labor demonstrated in cooperative learning with the object to actively participate in the class by using the tool of an app. This study also showed effective teaching strategy for improvement of learning achievement and task behavior level of student with special needs.

• Korean Journal of Comparative Education
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• v.24 no.5
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• pp.27-51
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• 2014

The objective of this study is to analyze problems and needs regarding recent education informatization in China and to seek implications for prospective international education cooperation between Korea and China. Toward this end, 76 individual and team reports submitted by Chinese trainees participated in APEC e-Learning Training Program from 2006 to 2013 were analyzed. The results are as follows. First, the most critical problem related to Chinese education informatization was identified as a lack of educational resources. The next three problems identified were, in order of importance, a lack of motivation to use ICT in education, the absence of a system for management and evaluation, and labor shortages in the supply of teachers and professional personnel. Second, with regard to the changing annual trends in China's education informatization needs, the issues of education/training and organizational environment to activate ICT use in education have been ranked high for the last eight years. In contrast, the matter of infrastructure has not been cited as a problem since 2008. However, more recently, the lack of relevant policy and the management and evaluation system have been raised, emphasizing the need for more systematic and professional policies and administrative systems.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.62-76
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• 2023

Recently, machine learning (ML) techniques have been actively applied for seismic trace interpolation. However, because most research is based on training-inference strategies that treat missing trace gather data as a 2D image with a blank area, a sufficient number of fully sampled data are required for training. This study proposes trace interpolation using ML, which uses only irregularly sampled field data, both in training and inference, by modifying the training-inference strategies of trace-based interpolation techniques. In this study, we describe a method for constructing networks that vary depending on the maximum number of consecutive gaps in seismic field data and the training method. To verify the applicability of the proposed method to field data, we applied our method to time-migrated seismic data acquired from the Vincent oilfield in the Exmouth Sub-basin area of Western Australia and compared the results with those of the conventional trace interpolation method. Both methods showed high interpolation performance, as confirmed by quantitative indicators, and the interpolation performance was uniformly good at all frequencies.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.179-200
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• 2021

This study is aimed at analyzing cases of management focusing on a reading club using the blended learning at school library and the relevant programs of the free semester system. Therefore, the study has designed a research model for the blended-based management of school libraries, and cases of activities of reading clubs at school libraries for participants in programs linked with the free semester system have been analyzed. As a result of the analysis, first, the confidence level was satisfied in all areas of stability, consistency, predictability and verification on confidence level for related variables of the research model. Second, a meaningful relation has been verified in the correlation analysis between the blended activities and activities of the career search and the career design. Third, as a meaningful static effect has been shown in the contact-free activities in the areas of activities of the blended learning and activities of the career search and the career design, it was verified that programs linked with reading clubs of the free semester system have higher positive effects in the contact-free activities. Last but not least, programs related to local governments to support reading clubs at school libraries have been presented, and management of the blended learning at school libraries has been suggested.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.