• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.437-448
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• 2021

As the core technology of the Fourth Industrial Revolution, AI is applied to various fields of society(e.g. politics, culture, industry, economy, etc.) and causes revolutionary changes. Students who will lead the age of AI need the ability to recognize social changes due to AI, acquire AI related knowledge and utilize AI in various situations. However, it is difficult for elementary school students to understand the concept and principles of AI. Therefore, this study developed an AI education program by selecting educational contents and methods appropriate to the level of elementary school students, and investigated the educational effects of the program by applying it to an actual educational setting. The content selected in this study is 'Social Awareness on AI', 'Understanding AI' and 'Utilizing AI', and eight content elements were selected. To help students learn AI easily and pleasantly at their level, activity-centered education, convergence of subjects and project-based learning were selected as instructional methods, and 20 sessions of education program were developed and implemented. In addition, the effects of the program were analyzed concerning 'perception on AI', 'convergent thinking', 'creative problem-solving' and 'collaboration capability', and positive changes were verified for all four aspects.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5420-5429
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• 2011

The study objectively examined the effects on education results from the educational programs developed by adopting competency units of NCS(National Competency Standards)' technology sectors. The objects of the study are divided to learners and instructors. The learners were set bounds to vocational college students to take a degree and incumbent company workers. Research materials had been collected from April of 2010 to June of 2011. We use test papers and structured questionnaire for studying. And we analyzed by SPSS/WIN 17.0. we examined that student's got 1.4 point out of 3 points in their self-test paper before taking classes, below average grades in understanding contents of learning. And as frequency analysis on the after taking classes performance evaluation 62.48% of them answered they can perform their duties in better ways. On average, the company workers got 1.4 point out 3 point before taking classes. And as frequency of analysis on the performance evaluation 85.45% of them answered the can perform their duties in better ways. After instructors took classes on NCS, they gave highly 5.58 out of 7 poins about learners' job competence. On the whole, the educational programs using NCS had positive effects on education results.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Journal of Korean Library and Information Science Society
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• v.51 no.3
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• pp.103-121
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• 2020

This study aimed to analyze the research trends of school libraries from 1990 to July 2020. To this end, LDA topic modeling analysis was conducted to the domestic article abstracts related to school libraries. The total number of documents is 498 papers published by the four major domestic journals in Library and Information Science. The log-likelihood estimate criterion was used to determine the number of topics for topic modeling. As a result of the study, 27 topics were discovered, then, theory were categorized by eight subject areas: general, institutional system, building/equipment, operation/management, data organization, service, education, and others. The most popular research was library utilization classes (T27) and Information Utilization (T2). More than 20 studies were found in each evaluation index development (T13), school librarian placement (T24), learning information media utilization (T3), community public library (T7), library cooperation (T9), library use (T17), library research (T11), reading education (T4), collection development (T5), and education effects/teaching methods (T18).

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.235-242
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• 2018

The interest about 4th Industrial Revolution was impressively increased from newspapers, iindustry, government and academic sectors. Especially AI what could be felt by the skin of many peoples, already overpassed the ability of the human's even in creative areas. Namely, now many people start fo feel that the effect of the revolution is just infront of themselves. There were several issues in this trend, the ability of deep learning by machine, the identity of the human, the change of job environment and the concern about the social change etc. Recently many studies have been made about the 4th industrial revolution in many fields like as AI(artificial intelligence), CRISPR, big data and driverless car etc. As many positive effects and pessimistic effects are existed at the same time and many preventing actions are being suggested recently, these opinions will be compared and analyzed and better solutions will be found eventually. Several educational, political, scientific, social and ethical effects and solutions were studied and suggested in this study. Clear implication from the study is that the world we will live from now on is changing faster than ever in the social, industrial, political and educational environment. If it will reform the social systems according to those changes, a society (nation or government) will grasp the chance of its development or take-off, otherwise, it will consume the resources ineffectively and lose the competition as a whole society. But the method of that reform is not that apparent in many aspects as the revolution is progressing currently and its definition should be made whether in industrial or scientific aspect. The person or nation who will define it will have the advantage of leading the future of that business or society.

• Journal of Distribution Science
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• v.14 no.2
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• pp.65-72
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• 2016

Purpose - Recently on-the-job training (OJT) has become the most preferred training and development method with the emergence of the concept that workplace is the best place where learning take place. But many researchers argue that OJT is not helpful for the performance of organization because OJT is not systematic and mostly depend on quality of trainer. Since Jacobs & McGriffin introduced S-OJT (structured OJT), there has been plenty of researches. But most of the researches have focused mainly on employee's attitude and organizational performance caused by S-OJT and neglected a holistic approach of S-OJT as a system. S-OJT need to be analyzed comprehensively to understand training performance because S-OJT is operated as a system consist of input, process, and organizational context. Although S-OJT may create unintended consequences, there were few researches to explore them. Thus, the purpose of this study is to identify the critical success factors for S-OJT and to find unintended consequences of it. Research design, data and methodology - We conducted a case study on M business unit of A company which developed and has been implementing S-OJT program for years. We designed and prepared the process, collected and analyzed data for the study. We set the theoretical framework to analyze the case after reviewing theories and previous studies on S-OJT. We collected and analyzed internal reports and interview results of the employees of the M business unit. We tried to collect as many information as possible to secure the validity of the research results. Results - The critical success factors identified in the study are as follow. First, it is important to select and train proper trainers for S-OJT. Second, it is needed to develop structured training module. Third, organization have to use effective communication system like on-line community. Forth, trainer should have proper skills for training such as facilitating skill, coaching skill, and delivering skill etc. Fifth, proper learning place is needed. Sixth, organizational support is important especially, immediate supervisor support and concern is critical. Eleventh, it is needed to consider situational contexts. Among them, overload to the trainer will affect the effectiveness of S-OJT. In this study, we found an additional unintended consequence. "To teach is the best way to learn." Experience as a trainer give employee an opportunity to organize one's knowledge and skill and to attain facilitation skill, coaching skill, and relation skill. Thus, organization may use S-OJT to train the potential talent. Conclusions - Many organizations introduced S-OJT to train the newcomers because S-OJT drew attention as an important tool to develop employees. Following this trend, there has been increasing number of researches to find the results of S-OJT and identify the determinants of S-OJT success. However, most of the researches concentrated on finding effects of some factors neglecting holistic approach. This study tried to identify critical success factors affecting effectiveness of S-OJT by using case study and find additional unintended consequence. The results of the study will be useful for organizations which have a plan to adopt S-OJT.

• Information Systems Review
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• v.22 no.1
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• pp.59-72
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• 2020

Artificial Intelligence is establishing itself as a familiar tool from an intractable concept. In this trend, financial sector is also looking to improve the problem of existing system which includes Fraud Detection System (FDS). It is being difficult to detect sophisticated cyber financial fraud using original rule-based FDS. This is because diversification of payment environment and increasing number of electronic financial transactions has been emerged. In order to overcome present FDS, this paper suggests 3 types of artificial intelligence models, Generative Adversarial Network (GAN), Deep Neural Network (DNN), and Convolutional Neural Network (CNN). GAN proves how data imbalance problem can be developed while DNN and CNN show how abnormal financial trading patterns can be precisely detected. In conclusion, among the experiments on this paper, WGAN has the highest improvement effects on data imbalance problem. DNN model reflects more effects on fraud classification comparatively.

• The Journal of Sustainable Design and Educational Environment Research
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• v.19 no.3
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• pp.38-47
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• 2020

This study analyzed the perceptions of teachers to establish the direction for managing the space outside an elementary school. Satisfaction with outdoor school spaces is influenced by the satisfaction with tree and flower plantation and outdoor rest spaces. This study found that the longer the working years of a teacher, the higher their awareness of the importance and necessity of outdoor spaces in the school. Respondents emphasized the lack of manpower and budget, as well as the indifference of the administration as hindrances to the management of outdoor spaces in the school. The outdoor space in the school should include a secure play area, plant education space, class practice spaces, and a rest area. Furthermore, the space outside the elementary school should support learning, playing, and resting. To this end, facilities such as benches, pergolas, outdoor classrooms, ecological ponds, farms, and flower beds should be provided. In an outdoor space, plants featured in textbooks, seasonal plants, and those that provide shade can be planted along with labels to provide information and thereby promote learning. The teachers expected that the management of the external space will have an educational and emotional effect on students. In response to the innovation of the school spaces, it is necessary to continuously manage the external spaces to achieve educational and emotional effects by organically connecting the outdoor spaces with the indoor space. For this purpose, it is required to provide support for securing budgets and manpower, and to introduce relevant policies.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Venture Innovation
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• v.6 no.2
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• pp.123-144
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• 2023

In the age of 100 years, it had become very important to prepare for unexpected dangers. This study was conducted to analyze the factors affecting the sales performance of financial planners. We analyzed the influence relationship of sales training, sales management, and customer orientation on sales performance, and furthermore, analyzed the impact of these influence relationships. To this end, sales training was subdivided into customer development, sales competency, and learning agility. Customer orientation was subdivided into the use of customer management system, SNS use, and customer service provision. Sales management was subdivided into goal orientation, manager leadership, and compensation system. The effect of these detailed variables on sales performance was empirically analyzed. To this end, a survey was conducted targeting currently active financial planners. The survey was conducted for a month in January 2023, and 250 valid samples were analyzed. The results of the empirical analysis were as follows. Customer development and learning agility had a significant positive (+) effect on sales performance. Sales competency were not tested for significance. Among customer orientations, SNS use and customer service provision had a significant positive (+) effect on sales performance. The use of the customer management system was not tested for significance. Among sales management, goal orientation and compensation system had a significant positive (+) effect on sales performance. Manager leadership was not tested for significance. The influence of variables that significantly affect sales performance was in the order of goal orientation, customer service provision, compensation system, slearning agility, customer development, and SNS use. Based on these research results, academic and practical implications were presented.