• Korean Journal of Culture and Social Issue
• /
• v.14 no.1
• /
• pp.77-99
• /
• 2008

The purpose of this paper is to examine learning motivational process models, based on Self-Determination Theory(SDT) in academic settings. I examined if SDT's assumptions would fit for Korean adolescents, using a learning motivation scale(LMS), Basic Needs-autonomy, competence, and relatedness-Satisfaction Scale(BNSS), academic grades and life-quality scales, and also tried to search for the adequate motivational process models for Korean adolescents through regression analysis and structural equation model analysis. The results of this study are as follows. Basic needs satisfaction influences positively on the development of self-determinative motivation, which influences positively on academic achievement. But academic achievement and self-determinative motivation doesn't always influence on subjective well-being positively. And Korean adolescents who study autonomously or achieve good grades, are not better in a dimension of subjective well-being than others. Basic needs satisfaction while growing is more important than any other variables to improve adolescents' autonomous motivation, academic achievement and subjective life qualities.

• Journal of Distribution Science
• /
• v.21 no.4
• /
• pp.47-56
• /
• 2023

Purpose: the research aims to analyze the impact of the distribution of knowledge through online learning on the intellectual potential of PhD students and produce recommendations for policy to improve intellectual capacity. During the literature review, it was determined that a large number of studies examined the impact of online learning on the quality of education at different levels. Research design, data and methodology: the research methodology is based on subjective assessment and studying the students' opinions. The basis of the study was a comprehensive analysis of primary data obtained through a sociological survey of PhD students. 324 respondents from humanitarian, medical and natural faculties participated in the survey. Results: the study revealed that online learning helps increase students' intellectual potential. PhD students had a positive attitude towards the transition from traditional education to online learning. It should be noted that, according to the results, the most popular gadgets were laptops and smartphones, which were characterized by high mobility and ease of use. Based on the obtained results, recommendations were developed for the formation of online learning with a focus on increasing students' intellectual potential. Conclusions: based on the results of the assessment of educational and innovative potential, policy recommendations and further research in this area were proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.494-510
• /
• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

• Journal of the Korean Institute of Educational Facilities
• /
• v.12 no.2
• /
• pp.5-13
• /
• 2005

The school library plays an important role to improve the student's mental activity and cultivate the formation of character and emotion. Especially in the information society, the students must collect, select and utilize the necessary information themselves. The school library has to collect, put in order and keep the book, audiovisual material and necessary information beyond it about the entire education and to offer the students and teachers them for the general school education. The school library has to be a center of school education and drive forwards the spontaneous and subjective learning activity as well as progress the education process. The school library must be the place to preserve the student's subjective study ability, that is, consolidate the materials as the study center and be composed of proper study materials for the solving-problem and research. Also to be perfect in the role of the information center, the school library has to be consolidated in accordance with development of various information softwares and ways including the newspaper, magazine, video, CD, laser disk and computer. It is required for the information of school library to be corresponded actively to the multi-media society.

• Journal of Korean Society of Rural Planning
• /
• v.27 no.2
• /
• pp.77-89
• /
• 2021

To predict the level of residents' participation in rural tourism project, we used agent-based model. The decision-making mechanism which calculates the utility related to attitude, subjective norm, perceived behavioral control of planned behavior theory was applied to the residents' decision to participate. As a result of the simulation over a period of 20 years, in the baseline scenario set similar to the general process of promoting rural projects, the proportion of indigenous people decreased and the participation rate decreased. In the scenarios with different learning frequencies in perceived behavioral control, overall participation rate decreased. Learning every five years had the effect of increasing the participation rate slightly. Participation rates increased significantly in the scenario that consider economic aspects and reputation in attitude and did not decline in the scenario where population composition was maintained. The virtuous cycle effect of subjective norm according to changes in participation rate due to influence of attitude and perceived behavioral control shows the dynamic relationship.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.77-82
• /
• 2023

The article considers online discussion as an interactive learning method in the conditions of distance learning. The essence of discussion and the stages of its organization are described. The main objective of discussion in distance learning is defined as the stimulation of interest in learning and the involvement of various viewpoints in an active discussion of the stated problems. The key role in ensuring the efficiency of a discussion is identified. The article develops a model for organizing asynchronous online discussions on the Moodle platform, highlighting the sequence of stages and their content. An experimental study of the use of the discussion method in the training of students in distance learning conditions is carried out. Based on the results of the methodological experiment, conclusions are drawn about student interest in online discussions. The authors conclude that the interest of students of different specialties in asynchronous online discussions varies, and the greatest interest is demonstrated by linguistics students. Nevertheless, the differences in student interest in online discussions by groups (specialties) are more likely attributable to subjective factors, which do not affect the overall picture in a major way.

• The Journal of Industrial Distribution & Business
• /
• v.11 no.7
• /
• pp.29-40
• /
• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• Journal of dental hygiene science
• /
• v.23 no.4
• /
• pp.271-281
• /
• 2023

Background: This study aims to improve dental hygiene education by investigating the relationship between metacognition, learning flow, and problem-solving abilities in dental hygiene majors. Methods: A survey was conducted on 2nd to 4th-year students from dental hygiene programs, with 132 responses analyzed. Data analysis involved t-tests and ANOVA to examine the differences in metacognition, learning flow, and problem-solving abilities based on the general characteristics. Multiple regression analysis was employed to investigate the factors influencing the dependent variable, which is problem-solving abilities. The collected data were analyzed using SPSS. Results: First, when comparing metacognition, learning flow, and problem-solving abilities based on the general characteristics of the study participants, statistically significant differences were observed in common factors such as major satisfaction, subjective academic performance, GPA (grade point average), and reason for major choice (p<0.05). Second, it was found that there is a significant positive correlation between metacognition, learning flow, and problem-solving abilities in dental hygiene students (r≥0.79, p<0.05). In other words, higher levels of metacognition and learning flow were associated with better problem-solving abilities. Third, factors influencing problem-solving abilities were identified, with both metacognition and learning flow having a statistically significant positive impact. It was also noted that metacognition had a greater influence on problem-solving abilities compared to learning flow (adjusted R²=0.815, p<0.05). Conclusion: To enhance the core competency of problem-solving abilities, it is essential to improve metacognition and learning flow. To enhance metacognition and promote learning flow, strategies such as goal setting, utilizing effective learning methods, boosting self-efficacy, managing the learning environment, choosing activities that foster immersion, stress management, self-assessment and feedback integration, improving focus, and utilization a variety of learning experiences will be necessary.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.5
• /
• pp.349-356
• /
• 2017

In this study, we propose a method to investigate topographic characteristics by applying machine learning which is an artificial intelligence analysis method based on the spatial data constructed using UAV and the training data created through spatial analysis. This method provides an alternative to the subjective judgment and accuracy of spatial data, which is a problem of existing topographic characteristics survey for officially assessed land price. The analysis method of this study is expected to improve the problems of topographic characteristics survey method of existing field researchers and contribute to more accurate decision of officially assessed land price by providing more objective land survey method.

• ETRI Journal
• /
• v.43 no.4
• /
• pp.694-701
• /
• 2021

Catastrophic events cause casualties, damage property, and lead to huge social impacts. To build common standards and facilitate international communications regarding disasters, the relevant authorities in social management rank them in subjectively imposed terms such as direct economic losses and loss of life. Terrorist attacks involving uncertain human factors, which are roughly graded based on the rule of property damage, are even more difficult to interpret and assess. In this paper, we collected 114 183 open-source records of terrorist attacks and used a machine learning method to grade them synthetically in an automatic and objective way. No subjective claims or personal preferences were involved in the grading, and each derived common factor contains the comprehensive and rich information of many variables. Our work presents a new automatic ranking approach and is suitable for a broad range of gradation problems. Furthermore, we can use this model to grade all such attacks globally and visualize them to provide new insights.