• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.529-539
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• 2017

The purpose of this study is to categorize and analyze the communication competency of engineering undergraduates and to develop educational content in order to improve that. In this study, communication competency of engineering undergraduates was categorized into three areas: critical thinking, scientific communication, and media literacy. As a means to improve communication competency, the experience with and perception of writing were investigated. The communication competency of undergraduates needs to be improved overall. There is a high need for writing programs that enhance critical thinking oriented around practice. It suggests flipped learning based on smart education, E-community, problem-solving programs based on action learning, cooperative learning programs, reflection journals & portfolio, and collaborative writing programs as educational content. The results of this study can be used as basic data to design competency-based communication curriculum and practical applications for engineering undergraduates.

• The Journal of Korean Institute of Information Technology
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• v.15 no.12
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• pp.173-180
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• 2017

In this paper, we propose a project-based learning using Arduino as an example of embedded system class in engineering students. By introducing these Project-Based Learning(PBL) into engineering education, students became able to actualize individual theories that they had learned through their major curriculum and they were given the experience to build up their field work ability by participating in the whole project development process. We conducted a questionnaire survey to investigate the education effect of PBL before and after class and the results were analyzed using SPSS statistical program. Since PBL is mainly operated by a team system, communication skills and teamwork within the organization can be improved through interactions among the members. All of the materials produced during the course of the project could be used to make portfolio of students, which could be of great help to data for employment activities after graduation.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• 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.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.37-48
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• 2017

Recently, the importance of software education is growing because computational thinking of software education is recognized as a key means of future economic development. Also human resources who will lead the 4th industrial revolution need convergence and creativity, computational thinking based on critical thinking, communication, and collaborative learning is known to be effective in creativity education. Software education is also a time needed to reflect social issues such as collaboration with developers sharing interests and open source development methods. Github is a leading social coding site that facilitates collaborative work among developers and supports community activities in open software development. In this study, we apply operational cases of basic learning of social coding sites, learning for storage server with sources and outputs of lectures, and open collaborative learning by using Github. And we propose educational model consisted of four stages: Introduction to Github, Using Repository, Applying Social Coding, Making personal portfolio and Assessment. The proposal of this paper is very effective for software education by attracting interest and leading to pride in the student.

• Journal of the Korean Society of Floral Art and Design
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• no.45
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• pp.47-56
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• 2021

It was applied by making the models such as the prior learning (e-learning), modeling by manual, learner's practice, 1:1 teaching coaching, self evaluation, coaching behavior assessment(primary, secondary), and self-directed practice. First, the cognitive practice education through the prior learning is very essential in the practice of floral design. Second, the practice class of floral design is a class where the professor generally set an example first, and the learners followed. Third, this study was to prepare the checklist, reflect it through the self evaluation, and prepare the evaluation form in accordance with the element, principle, and technical parts of floral design about the finished works. Fourth, contrary to the existing class completing within the class hour, the practice class is a process of trying to do self-directed practice, returning to home. Fifth, this study was to evaluate the works the learner made once again through the sketching and photographing by placing the work process of portfolio at the last step. To conclude, this study has found that such series of process through six steps on the practice form by the learner only would be excellent teaching learning model to improving the basic capacity of floral design. Accordingly, the development of teaching materials related to this and adaptation in the field in the future is considered as it will be very helpful to the learners' self-directed learning.

• The Journal of Korean Academic Society of Nursing Education
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• v.21 no.2
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• pp.202-214
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• 2015

Purpose: The purpose of this study was to develop extracurricular coaching programs to improve communication skills and leadership for nursing students and evaluate the effects of the programs. Methods: The 8-week extracurricular coaching program was developed based on the Joo, Whitmore and Hong models. A quasi-experimental design was used. The subjects were selected by two full-time nursing professors training students at one university in city G. The subjects were chosen from among the advisees of these two professors. Of the students who participated in this study, 29 were in the experimental group and 27 were in the control group. Data was analyzed through t-test and Mann Whitney U-test. Results: The experimental group showed significantly higher post-test scores in communication skills, communication as a nursing outcome, observation of communication, leadership, and leadership as a nursing outcome than those of the control group. However, the experimental group did not reveal significantly higher post-test scores in the number of leadership activities using a portfolio than those of the control group. Conclusion: This extracurricular coaching program can help cultivate important, basic grounding as well as achieve nursing student learning outcomes upon graduation.

• Korean Medical Education Review
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• v.23 no.2
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• pp.90-103
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• 2021

This study examined the current status of the medical professionalism curriculum in Korea to suggest a plan to move towards the formation of a professional identity. Professionalism education data from 28 Korean medical schools were analyzed, including the number of courses, required or elective status, corresponding credits, major course contents, and teaching and evaluation methods. Considerable variation was found in the number of courses and credits in the professionalism curriculum between medical schools. The course contents were structured to expand learners' experiences, including the essence and knowledge of professionalism, understanding of oneself, social interaction with others, and the role of doctors in society and the healthcare system. The most common teaching methods were lectures and discussions, while reflective writing, coaching, feedback, and role models were used by fewer than 50% of medical schools. Written tests, assignments and reports, discussions, and presentations were frequently used as evaluation methods, but portfolio and self-evaluation rates were relatively low. White coat ceremonies were conducted in 96.2% of medical schools, and 22.2% had no code of conduct. Based on the above results, the author suggests that professional identity formation should be explicitly included in learning outcomes and educational contents, and that professional identity formation courses need to be added to each year of the program. The author also proposes the need to expand teaching methods such as reflective writing, feedback, dilemma discussion, and positive role models, to incorporate various evaluation methods such as portfolios, self-assessment, and moral reasoning, and to strengthen faculty development.

• Korean Medical Education Review
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• v.26 no.1
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• pp.19-26
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• 2024

This study examines a systematic and effective approach to career guidance in medical education, with a particular focus on the 6-year integrated career guidance education framework implemented at the College of Medicine, The Catholic University of Korea. Based on the "New SLICE" educational development principles, this framework comprehensively addresses the needs of medical students in career planning and development. It is structured into three phases: understanding yourself, exploring options, and choosing a specialty. The first phase, understanding yourself, helps students to recognize their strengths, weaknesses, aptitudes, and potentials, thereby setting the direction for future career choices. This phase includes various psychological tests and Self-Development and Portfolio courses. The second phase, exploring options, enables students to engage in related activities such as research and practical training, providing direct and indirect experiences across various fields. This phase offers courses including Medical Field Experience, Career Guidance through the Learning Community & Advisory Professors, and Student Participation in Professor Research Projects. The final phase, choosing a specialty, involves students making decisions based on in-depth self-assessment and exploration of majors, with a capstone project being a significant component. Maximizing the efficiency of career decision-making requires integration between the basic medical curriculum and postgraduate education. Including the period up to residency entrance in the framework is necessary for effective career guidance education.