• The Journal of Society for e-Business Studies
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• v.18 no.3
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• pp.177-193
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• 2013

The study investigated how did Credu pioneer Korean e-Learning market and reinforce their core competencies and how did they promote business diversification analyzing type of learning motives and examining Credu real cases. Under the fierce competition in the current e-Learning market, Credu has maintained their core competencies and strengthened an active communication channels with customers with focusing on investment to core businesses. Credu has introduced more adventurous and challenging products and services with creating a new model for the types of learning motives. This study could introduce the new perspective about the type of learning motives in e-Learning area. Also, the case study of successful e-Learning company, Credu, could contribute to make more spread e-Learning market techniques in this field in practice.

• 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.

• Women's Health Nursing
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• v.22 no.4
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• pp.221-232
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• 2016

Purpose: The purpose of this study was to examine the applicability of a flipped learning course in Women's Health Nursing for nursing students. Methods: A total of 200 senior nursing students participated in flipped learning class of pregnancy, delivery and postpartum area, which included team-based learning and self-reflection for 8 weeks. One group pre-post test design was adopted and the changes in learning motivation and satisfaction were examined. In addition, reflective journals of the students were analyzed by making a qualitative content analysis. Results: Students showed a significant increase in score of learning motivation in the posttest (t=-4.47, p<.001). They had a mean of 3.90 in learning satisfaction out of possible five points. As a result of content analysis, three themes were selected: 'Improved attitude toward active learning', 'Burden caused by excessive workload', and 'Valuing to the team-based activity' To be specific, six sub-themes were selected, with three positive and three negative categories: 'improved class attention and understanding', 'positive class participation by preparing lessons in advance', 'peer interactions through discussion', 'A lot of time and effort consuming', 'stress caused by the burden of preparing lessons', and 'difficulties in cooperative activities'. Conclusion: This study supports and confirms that the flipped learning can be a creative instructional model of positive teaching-learning strategy in clinical nursing courses to enhance students' learning motivation.

• The Journal of Korean Association of Computer Education
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• v.7 no.4
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• pp.93-102
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• 2004

For children with hearing impairment, unlike the children without hearing impairment who can reconstruct their languages through the process of hearing and uttering, the inherent mechanism for language acquisition do not operate due to the loss of hearing ability. Therefore, to help hearing-impaired children develop their language ability, web-based language learning system should be constructed depending on the special qualities which the children possess in language learning process. When the system is being designed, it is necessary that words or expressions describing actions or situations be animated and that active situation-based language learning system be constructed to help them develop their power of observation. Moreover, the system needs to be developed through the use of alternative thinking strategy, antonyms, and contrastive words, and emphasis on facial expressions. This paper presents web-based language learning system which is suitable for hearing-impaired children in the way to reduce the grammatical errors they make and to improve their language learning.

• Journal of Internet of Things and Convergence
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• v.5 no.2
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• pp.89-94
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• 2019

The purpose of this study was to empirically analyze the motivation of university lifelong education and the factors that are important in selecting it. In the motivation for participation of adult learners, learning new knowledge and skills in the first place and learning new things in the second place were good, so that they could learn together in the third place. Active school investment in faculty and staff will be needed to increase program diversity and quality through professionalism. Adult learners are getting information from the system they are interacting with, and furthermore, they learn the information from the public relations of university professors. Since they are acquiring paths and information through acquaintances, it is necessary to continuously promote the curriculum to unspecified adult learners. Advertisement should take advantage of various convergence strategies such as hanging banners in the area, publicity of the subway, local newspapers, word of mouth, SNS, and the Internet.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.1
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• pp.141-148
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• 2018

In recent years, there has been a movement to create a learning city where people can learn and enjoy what they want whenever, wherever, and whenever, so that the self-realization of individuals and the quality of life can be enhanced to improve the competitiveness of the city as a whole, It is becoming active. Many developed countries in the world are supporting projects to build learning cities by utilizing schools and public facilities, thereby providing local residents with opportunities for self-growth and solving community problems. In Korea, too, there are various programs using idle facilities. However, there is a lack of education programs for local residents and learning programs by partnership with local communities. It is when spatial and software strategies are needed to build a successful learning city. Therefore, we want to systematically organize the spatial data of the facilities that can be learned, analyze the current problems, and explore various ways to utilize them. We also analyze the programs that residents need to implement real and efficient learning cities.

• Convergence Security Journal
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• v.13 no.2
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• pp.101-109
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• 2013

Problem Based Learning(PBL) is one of effective teaching-learning strategy for enhancing military students' motivation, comparing with the other study method. After monitoring military student classes, we conclude that PBL can enhance the effectiveness of learning in War history case study education and finds merits; PBL assists students to play a more active role in the class, induces students to solve problems independently, and makes the learning military situation real case study. The case study is common in social sciences and life sciences. Case studies may be descriptive or explanatory. It is good for War history education. The demerit of PBL is a costly method as students should spend more time and institutions should provide more manpower and materials. This study suggests that more empirical researches on alternative teaching methods, including PBL, to a lecture in War history education.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.579-590
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• 2021

This study examined the mediating effect of learning engagement in the relationship between self-regulation, teaching presence and computational thinking in online SW education. To verify the research problem, a blended learning model adopted SW liberal course at A Women's University located in Seoul, which 94 students were enrolled in, was selected. The results of this study and the implications are as follows: First, it was found that learning engagement mediated the relationship between self-regulation and computational thinking. Second, it was found that learning engagement mediated the relationship between teaching presence and computational thinking. This study suggested a plan to improve learners' active engagement and self-regulation strategy in online SW education. In addition, it is significant that this study considered a method for learners to perceive teaching presence in online learning environment.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.