• Journal of Industrial Convergence
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• v.20 no.2
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• pp.21-27
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• 2022

Purpose: The purpose of this study is to investigate the structural relationship between self-directed learning, online class environment, and learning satisfaction of general high school students. 219 12th grade students in Sejong B High School responded to the survey questionnaire. For data analysis, correlation analysis and structural equation models were implemented. Results are as follows. First, there was a moderate or higher correlation between self-directed learning, online class environment, and learning satisfaction. Second, the model fit of the structural model among variables was good. Self-directed learning had an effect on the online class environment, and the online class environment had a positive effect on learning satisfaction. However, self-directed learning had no statistically significant effect on learning satisfaction. The researcher found the implication that learners' online class satisfaction showed a synergistic effect when students' self-directed learning ability and educators' excellent class environment are created. Also, the researcher proposed to analyze online learning satisfaction by comprehensively considering the individual, family, and school factors of various learners.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.479-493
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• 2010

Along with the transformation of the knowledge-based environment, e-learning has become a main teaching and learning method, prompting various research efforts to be conducted in this field. One major research area in e-learning involves adaptive learning systems that provide personalized learning content according to each learner's characteristics by taking into consideration a variety of learning circumstances. Active research on ontology-based adaptive learning systems has recently been conducted to provide more efficient and adaptive learning content. In this paper, we design and propose an adaptive learning system based on the concept lattice of Formal Concept Analysis (FCA) with the same objectives as those of ontology approaches. However, we are in pursuit of a system that is suitable for learning of specific domains and one that allows users to more freely and easily build their own adaptive learning systems. The proposed system automatically classifies the learning objects and concepts of an evolved domain in the structure of a concept lattice based on the relationships between the objects and concepts. In addition, the system adaptively constructs and presents the learning structure of the concept lattice according to each student's level of knowledge, learning style, learning preference and the learning state of each concept.

• Journal of Engineering Education Research
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• v.24 no.3
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• pp.21-31
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• 2021

Flipped learning, which involves listening to lectures at home and performing dynamic group-based problem-solving activities in the classroom, is recently evaluated as a learner-centered teaching method, and interest and applications in engineering education are increasing. Therefore, this study aims to provide practical guidelines for successful application through empirical research analysis on the use of flipped learning in domestic engineering education. Through the selection criteria and keyword search, a systematic review of 36 articles was conducted. As a result of the analysis, flipped learning research in engineering education has increased sharply since 2016, focusing on academic journals and reporting its application cases and effects. Most of the research supported that flipped learning was effective not only for learners' learning activities(e.g., academic achievement, satisfaction, engagement, learning-flow, interaction), but also for individualized learning and securing sufficient practice time. It was often used in major classes with 15 to less than 50 students, especially in computer-related major courses. Most of them consisted of watching lecture videos, active learning activities, and lectures by instructors, and showed differences in management strategies for each class type. Based on the analysis results, suggestions for effective flipped learning management in future engineering education were presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.299-304
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• 2023

Among artificial intelligence techniques, deep learning is a model that has been used in many places and has proven its effectiveness. However, deep learning models are not used effectively in everywhere. In this paper, we will show the limitations of deep learning models through comparison of regression analysis and deep learning models, and present a guide for effective use of deep learning models. In addition, among various techniques used for optimization of deep learning models, data normalization and data shuffling techniques, which are widely used, are compared and evaluated based on actual data to provide guidelines for increasing the accuracy and value of deep learning models.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.162-165
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• 2003

Differential learning relies on the differentiated values of nodes, whereas the conventional learning depends on the values themselves of nodes. In this paper, I elucidate the differential learning in the framework maximum likelihood learning of linear generative model with latent variables obeying random walk. I apply the idea of differential learning to the problem independent component analysis(ICA), which leads to differential ICA. Algorithm derivation using the natural gradient and local stability analysis are provided. Usefulness of the algorithm is emphasized in the case of blind separation of temporally correlated sources and is demonstrated through a simple numerical example.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.233-244
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• 2023

Although artificial intelligence and machine learning technologies have been used in various fields, problems with personal information protection have arisen based on centralized data collection and processing. Federated learning has been proposed to solve this problem. Federated learning is a process in which clients who own data in a distributed data environment learn a model using their own data and collectively create an artificial intelligence model by centrally collecting learning results. Unlike the centralized method, Federated learning has the advantage of not having to send the client's data to the central server. In this paper, we quantitatively present the performance improvement when federated learning is applied using the building change detection learning data. As a result, it has been confirmed that the performance when federated learning was applied was about 29% higher on average than the performance when it was not applied. As a future work, we plan to propose a method that can effectively reduce the number of federated learning rounds to improve the convergence time of federated learning.

• Korean Medical Education Review
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• v.23 no.2
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• pp.118-127
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• 2021

Due to the coronavirus disease 2019 pandemic, the new norm of online learning has been recognized as core to medical institutions for academic continuity, and students are expected to be motivated and engaged in learning while maintaining distance from other peers and educators. To facilitate students' and educators' newly defined roles in online medical education settings, it is crucial to understand how students are actively motivated and engaged in learning. Hence, this study explored medical students' motivational regulation profiles and examined the effects of motivational regulation strategies (MRS) on cognitive learning and learning engagement for online learning. Data were collected after the end of the first semester in 2020 from a sample of 334 medical students enrolled at a public university school of medicine. Latent profile analysis indicated three subgroups with different motivational regulation profiles: the low-profile, medium-profile, and high-profile groups. Regarding different MRS patterns in the high-profile group, mastery self-talk, performance approach self-talk, and the self-consequating strategy appeared to be most applicable for regulating learners' motivation. Analysis of variance showed that the profile groups with higher levels of MRS use were connected to a higher willingness to use cognitive learning strategies and a higher degree of engagement in online learning. The findings of this study emphasize the use of specific sets of MRS to support learning motivation and the need to design effective self-regulated learning environments in online medical education settings.

• Journal of the Korean institute of surface engineering
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• v.56 no.6
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• pp.353-370
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• 2023

Corrosion detection and analysis is a very important topic in reducing costs and preventing disasters. Recently, image processing techniques have been widely applied to corrosion identification and analysis. In this work, we briefly introduces traditional image processing techniques and machine learning algorithms applied to detect or analyze corrosion in various fields. Recently, machine learning, especially CNN-based algorithms, have been widely applied to corrosion detection. Additionally, research on applying machine learning to region segmentation is very actively underway. The corrosion is reddish and brown in color and has a very irregular shape, so a combination of techniques that consider color and texture, various mathematical techniques, and machine learning algorithms are used to detect and analyze corrosion. We present examples of the application of traditional image processing techniques and machine learning to corrosion detection and analysis.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.1
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• pp.209-238
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• 2023

This study aims to propose the main learning contents, functions and activation plans for building an e-learning platform for libraries through a literature review, case analysis and expert survey. Through the literature review, it was found that libraries must play a role in providing high-quality online education for users in the e-learning ecosystem. Based on the previous studies, a learning function analysis tool was developed for the analysis of the library's e-learning platform. Based on this, the learning contents, learning functions and characteristics of library e-learning platforms were analyzed, and expert surveys and interviews were conducted. As a results, the construction of a platform for effectively applying learning processes and technology is essential for the library's sustainable e-learning services. The contents that should be provided for characteristics of library education, reading guidance, information literacy instruction, library usage instruction, and the latest IT technologies. And The main learning functions include the ability to conduct video lectures and real-time classes among learning types, and learning activity support functions, a cloud platform support function and a personalized environment support function. Additionally, suggested re-education for library staff to improve their technical skills and the formation of an e-learning team.

• Journal of vocational education research
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• v.35 no.6
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• pp.65-84
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• 2016

This study aims to explore the duty system of the lifelong learning counseling, and to analyze the needs of counseling practice which are conceived by lifelong education practitioners. Based on the related prior studies, the duty system of lifelong learning counseling was investigated and classified. Also, differences of how to recognize the importance of counseling job and how to practice counseling are assessed by Borich method. After data were collected by practitioners from lifelong education field, the dependent t-test and the Borich needs assessment formula were used for analysis of the collected data. The results are as follows: the 4 subdivided duties of lifelong learning counseling are formation of relationship, learner's analysis, learning promotion, and follow-up management. The 11 tasks are learner's interview, providing learning information, analysis of learner's characteristics and needs, learning level diagnosis, diagnosis of learning inhibiting factors, promotion of learning motivation, advice of learning course and learning method, support of study circle activity, career planning counseling, follow-up counseling, and counseling evaluation. According to the needs assessment, learner's analysis is conceived as the most important duty among the 4 sub-duties, and learner's analysis is regarded as second important duty by the counseling practitioners. Among the 11 tasks, providing learning information is the most important tasks among counseling practitioners, and analysis of learner's characteristics and needs is followed as second task. The duty system of the lifelong learning counseling and needs assessment data can be used as the basic data for lifelong education practitioners to conduct the duty of lifelong learning counseling efficiently and to support the lifelong learning plan according to learner's characteristics.