• Journal of Digital Convergence
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• v.16 no.3
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• pp.37-45
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• 2018

The purpose of this study was to understand language learning strategies use, language learning beliefs, and listening achievement of Korean EFL learners. The participants was sixty-nine university students who enrolled in blended learning classes for English listening. Data was collected and analyzed to see if there were any differences in strategies and beliefs between students who improved their listening test score and those who did not. The results showed that students showing improvement at the post-test used more language learning strategies and had a high level of motivation. Statistically significant correlation was found between motivation and strategies such as cognitive and meta-cognitive strategies.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.383-390
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• 2014

In recent years, the convergence of IT and IT sightseeing tour has emerged as a fusion of academic disciplines in the future. Convergence study of social data analysis, raising the heat. Social Network Services (SNS) being utilized in many areas of marketing and to apply the case study is also increasing. This study is based u-smart tourist information systems for mobile learning content design. This is the pattern of things in the template library for things to increase the effectiveness of the learning content to mobile learning content to be converted to a. Design of mobile learning content using u-smart things smart phone app (App) and XMI to go through the design process of utilizing the heat. Future through the design process by implementing a mobile learning content to meet information quality tourist information content to create mobile learning content and learning things that can be content to live it up advantage.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.1-6
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• 2020

The purpose of this study is to theoretically understand hope theory suggested by Snyder and confirm its utility in the school settings. We analyzed the survey data responded by general high school students to find clustering types of hope and mean difference of learning strategies by each type through ANOVA. Results are as follows. First, hope by cluster analysis resulted in four types. Second, hope and learning strategy showed statistically positive correlation. Especially two sub-variables of hope and meta-cognition had highest correlation. Researchers suggested the direction of a future study to investigate structural relation among hope profile, student achievement, adjustment, and etc.

• Advances in Computational Design
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• v.2 no.4
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• pp.313-331
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• 2017

In this study, teaching-learning based optimization (TLBO) is improved by incorporating model of multiple teachers, adaptive teaching factor, self-motivated learning, and learning through tutorial. Modified TLBO (MTLBO) is applied for simultaneous topology, shape, and size optimization of space and planar trusses to study its effectiveness. All the benchmark problems are subjected to stress, displacement, and kinematic stability constraints while design variables are discrete and continuous. Analyses of unacceptable and singular topologies are prohibited by seeing element connectivity through Grubler's criterion and the positive definiteness. Performance of MTLBO is compared to TLBO and state-of-the-art algorithms available in literature, such as a genetic algorithm (GA), improved GA, force method and GA, ant colony optimization, adaptive multi-population differential evolution, a firefly algorithm, group search optimization (GSO), improved GSO, and intelligent garbage can decision-making model evolution algorithm. It is observed that MTLBO has performed better or found nearly the same optimum solutions.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1000-1013
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• 2017

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.89-104
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• 2024

Cloud-based e-learning (CBEL) represents a promising technological frontier. Existing literature has presented a diverse array of findings regarding the determinants that influence the adoption of CBEL. The primary objective of this study is to conduct an exhaustive examination of the available literature, aiming to determine the key predictors of CBEL utilization by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. A comprehensive review of 35 articles was undertaken, shedding light on the status of CBEL as an evolving field. Notably, there has been a discernible downturn in related research output during the COVID-19 pandemic, underscoring the temporal dynamics of this subject. It is noteworthy that a significant portion of this research has emanated from the Asian continent. Furthermore, the dominance of the technology acceptance model (TAM) in research frameworks is affirmed by our findings. Through a rigorous thematic analysis, our study identified five overarching themes, each encompassing a diverse range of sub-themes. These themes encompass 1) technological factors, 2) individual factors, 3) organizational factors, 4) environmental factors, and 5) security factors. This categorization provides a structured framework for understanding the multifaceted nature of CBEL adoption determinants. Our study serves as a compass, guiding future research endeavours in this domain. It underscores the imperative for further investigations utilizing diverse theoretical frameworks, contextual settings, research methodologies, and variables. This call for diversity and expansion in research efforts reflects the dynamic nature of CBEL and the evolving landscape of e-learning technologies.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.551-553
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• 1999

This paper explores the utility of a new classifier fusion approach to discrimination. Multiple classifier fusion, a popular approach in the field of pattern recognition, uses estimates of each individual classifier's local accuracy on training data sets. In this paper we investigate the effectiveness of fusion methods compared to individual algorithms, including the artificial neural network and k-nearest neighbor techniques. Moreover, we propose an efficient meta-classifier architecture based on an approximation of the posterior Bayes probabilities for learning the oracle.

• 한국정보교육학회:학술대회논문집
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• 2008.01a
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• pp.228-235
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• 2008

21세기 인류는 디지털 기술 혁신과 정보 통신 혁명으로 물리적 공간과 가상공간이 통합되는 새로운 유비쿼터스 시대를 맞이하고 있다. 미국, 일본, 유럽 등 많은 선진 국들은 유비쿼터스 컴퓨팅 혁명을 자국의 경쟁력 강화를 위한 핵심 패러다임으로 인식하고 유비쿼터스 관련 연구에 박차를 가하고 있다. 이에 본 연구에서는 엄선된 u-러닝 분석 사례의 고찰을 통해 'u-러닝 연구 방향 설정 시 고려할 사항','u-러닝 학습 모델 개발 시 고려할 사항' 및 'u-러닝 환경 조성 시 고려할 사항'등을 도출해 U-러닝 활성화 방안을 모색하였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2189-2198
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• 1994

In this paper, a self-tuning fuzzy logig controller is implemented to control a DC servo motor by the self-tuning technique based on fuzzy meta-rules with learning in several algorithms to improve the performance of the fuzzy logic controller used in a fuzzy control system. Simulations and experimental results of the self-tuning fuzzy logic controller are compared with those of the fuzzy logic controller to evaluate its performance.