• Human Ecology Research
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• v.61 no.1
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• pp.15-28
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• 2023

The aim of this study was to develop, implement, and evaluate the use of Artificial Intelligence in the prevention of violence among middle-school students. The sample for this study consisted of 20 first-year middle-school students who participated in theme selection activities in a free semester program as part of their home economics studies. The data for the study consisted of nine class observation logs, four group activity outputs, 30 class results, an online survey, and in-depth interviews with three students. A program called "R.U.OK" was developed by setting problematic situation for school violence prevention linked to the contents of the Home Economics Education(HEE) curriculum. After the program was implemented, the survey on the students' class satisfaction content elements, with AI-based learning activities and PBL and interest, displayed high points, with an average of 4.0 or higher. Our qualitative analysis produced four significant results. First, students' concerns about school violence had increased and they showed a change in attitude, having more empathy with friends and more interest in their surroundings. Second, digital and AI literacy had improved, and students' interest in digital media learning had increased. Third, there had been an improvement in problem-solving ability in terms of being able to think more critically and independently. Fourth, the results also demonstrated that there had been a positive effect on self-direction and an improved capacity for teamwork. This study was significant in demonstrating the effectiveness of a program for the prevention of school violence based on the use of digital technology in the educational environment.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.619-624
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• 2023

As the importance of digital competency education is highlighted, this study is a case study on block coding classes for elementary school students during vacation for the purpose of bridging the information education gap among students. The purpose of this study is to design and operate a play-centered block coding class program and find out if it is effective in improving students' interest. As a result of completing the teaching plan through the second consultation and revision, running the class, and analyzing the change in learning interest of the students through the t-test, the play-oriented block coding class designed in this study was effective in improving students' interest. In addition, it was possible to discover interesting elements such as student-led learning process and immersion through realistic play activities, friendship, collaboration, and communication through group activities. This study is significant in suggesting a plan to increase learning interest for students who are new to coding.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.41-50
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• 2024

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

• The Journal of the Korea Contents Association
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• v.11 no.8
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• pp.77-87
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• 2011

The advent of new platforms each year along with the advancement of technology provides a new opportunity for digital media designers to develop creative and innovative contents. This phenomenon affect the same way the students that major in the digital media, and the use of the platforms that is based on the new technology in the development of contents gives a newer and useful opportunity for learning to the students who recently study the digital media area. As the main technology of the recent digital media that attract many students' attention, we are presenting virtual reality display, movement cognition, physical engine and the gesture interface, and developed the consolidated platform based on these four technologies, and designed them in a way that can be more easily implemented in a simpler way. In order to study the efficiency of the platform with the objective of the development of digital media contents, we have developed four different prototype contents, and have measured based on the user's preference, efficiency and satisfaction. In the results of usability evaluation, functionality, effectiveness, efficiency, satisfaction were rated as 'high'. This results shows that the suggested 3D platform environment provides students to develop a rapid prototype fast and easy, and this may have a positive influence on students major in the digital media to conduct creative development research.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• 전자공학회논문지 IE
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• v.49 no.2
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• pp.65-74
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• 2012

The purpose of this study is to verify the prediction of mastery-approach goal orientation, task value, and self-regulated learning strategy on academic satisfaction and achievement of cyber engineering university students. For this study, 219 engineering students of H cyber university who enrolled in the spring semester of 2011 was chosen and completed web surveys. A hypothetical model proposed included mastery-approach goal orientation, task value, and self-regulated learning strategy as predictors, and academic satisfaction and achievement as criteria variables. The results of this study through multiple regression analysis indicated that task value(${\beta}$=.401) and self-regulated learning strategy(${\beta}$=.401) predicted significantly on academic satisfaction. In addition, self-regulated learning strategy(${\beta}$=.301) and mastery-approach goal orientation(${\beta}$=.196) predicted significantly on academic achievement. The result of this study suggested that mastery-approach goal orientation, task value, and self-regulated learning strategy should be considered for improving academic satisfaction and achievement in cyber engineering education.

• Journal of Science Education
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• v.42 no.2
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• pp.95-105
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• 2018

The purpose of this study was to develop a digital textbook on 'structure and contraction mechanism of skeletal muscle' with the learning model for biomimicry-based convergence. The unit of 'structure and contraction mechanism of skeletal muscle' is a part of Life Science I in high school. The convergence learning model was designed with three phases of biomimicry-based convergence (Exploration-Design-Implementation) including 3D modeling & printing. The developed digital textbook was composed of 8 sessions which contains the following learning contents : Exploration of skeletal muscle, creative designing of skeletal muscle using sketch application and 3D modeling, convergent implementing of the designed using 3D printing, exploration of muscle contraction, creative designing of muscle contraction using sketch application and 3D modeling, and convergent implementing of the designed using 3D printing. Each session is also involved in the contents of gallery widgets, media widgets, keynote widgets, sketch widgets, the cloud, polling widgets, and review widgets for interactive and mobile learning. After administering the developed digital textbook to 20 high school students, it was shown a positive effectiveness on life science learning for high school students. Moreover, the digital textbook was evaluated as to promote student's abilities on creative designs and implementation related to biomimicry-based convergence. The digital textbook was also shown a favorable response on students' interest and self-directed learning on life science.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.266-279
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• 2021

This study applied the e-PBL (e-Project-based learning) method for "Urban Forest Management" courses in the Department of Forest Science at S University to progress in university forest education. e-PBL effectively motivates self-directed learning, problem-solving, communication skills, and learners' responsibility by enabling them to choose, design, and perform their projects. Due to the COVID-19 pandemic in 2020, learners were encouraged to use online media to carry out projects and submit presentations for the campus forest. Learners' educational effects were subsequently investigated through a five-point Likert scale. This study discovered a positive effect on learners' motivation and interest (4.17) through e-PBL. Learners responded that e-PBL also helped their understanding regarding the subject (4.17). In addition, this study provided evidence that the e-PBL method was helpful in problem-solving (4.25), communication (4.33), and decision-making skills (4.21). According to learners' responses, there are positive indications that learners were satisfied with e-PBL. Learners responded that interactions and communications with team members could improve their understanding of the subject. Hence, there is scope for improving an efficient and successful e-PBL model suitable for university forest education by providing more efficient instructional time management, e-PBL method guidelines, and institutional support.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.133-145
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• 2022

Compared to the continuously increasing dog population and industry size in Korea, systematic analysis of related data and research on breed classification methods are very insufficient. In this paper, an automatic breed classification method is proposed using deep learning technology for 14 major dog breeds domestically raised. To do this, dog images are collected for deep learning training and a dataset is built, and a breed classification algorithm is created by performing transfer learning based on VGG-16 and Resnet-34 as backbone networks. In order to check the transfer learning effect of the two models on dog images, we compared the use of pre-trained weights and the experiment of updating the weights. When fine tuning was performed based on VGG-16 backbone network, in the final model, the accuracy of Top 1 was about 89% and that of Top 3 was about 94%, respectively. The domestic dog breed classification method and data construction proposed in this paper have the potential to be used for various application purposes, such as classification of abandoned and lost dog breeds in animal protection centers or utilization in pet-feed industry.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1208-1215
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• 2017

Recently, people's attention and worries about fine particulate matter have been increasing. Due to the construction and maintenance costs, there are insufficient air quality monitoring stations. As a result, people have limited information about the concentration of fine particulate matter, depending on the location. Studies have been undertaken to estimate the fine particle concentrations in areas without a measurement station. Yet there are limitations in that the estimate cannot take account of other factors that affect the concentration of fine particle. In order to solve these problems, we propose a framework for estimating the concentration of fine particulate matter of a specific area using meteorological data and traffic data. Since there are more grids without a monitor station than grids with a monitor station, we used a domain adversarial neural network based on the domain adaptation method. The features extracted from meteorological data and traffic data are learned in the network, and the air quality index of the corresponding area is then predicted by the generated model. Experimental results demonstrate that the proposed method performs better as the number of source data increases than the method using conditional random fields.