• East Asian mathematical journal
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• v.34 no.4
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• pp.451-462
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• 2018

This study analyzed the learning components of the web-based adaptive math learning programs in order to develop adaptive math learning program using artificial intelligence. The components of the web-based adaptive math learning program set for analysis are classified into learning process presentation, concept learning, problem presentation, problem solving process, and learning result processing then analyzed three programs. As a result of analysis, the typical characteristic of components is that it uses a method of repeatedly presenting the same type of problem in order to learn one concept.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.69-81
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• 2020

The objective of this study is to seek ways to maximize learning effects from e-learning by drawing improvement directions through investigating and analyzing an awareness of e-learning among e-learning attendees. The study was conducted among the attendees who are taking the e-learning program operated by K University and collected data from the students taking second semester in 2018 with the use of structured questionnaires. For data processing, SPSS Statistics 22.0 and AMOS were used, along with such analytical methods as frequency anslysis, descriptive statistical analysis, ANOVA (Analysis of Variance), t-analysis and cross tabulation. For significant data, it conducted an analysis by carrying out the Scheffe's test. According to the findings from this study, they showed a significant difference only in gender and curriculum desired to be opened in the question about e-learning participation motives per background factor. As for the learners' motives to study, it was confirmed that they tend to become more biased on time utilization and convenience of learning methods. The analysis of which factor of the three - learning factors, system factors and instructor's factors - has greatest effects on learning satisfaction indicated that learning factors influenced learning satisfaction the most in accordance with values for non-standard coefficient beta, followed by instructor factors which had a direct effect.

• Educational Technology International
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• v.22 no.2
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• pp.255-283
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• 2021

In the early 2020, COVID-19 changed the traditional way of teaching and learning. This paper aimed to explore the impact of college students' perception of course quality on their online learning satisfaction. A total of 4,812 valid samples were extracted, and the difference analysis and hierarchical regression analysis were used to make an empirical analysis of college students' online learning satisfaction. The research results were as follows. Firstly, there was no difference in online learning satisfaction among students by gender and grade. Secondly, learning assessment, course materials, course activities and learner interaction, and course production had a significant positive impact on online learning satisfaction. Course overview and course objectives had an insignificant correlation with online learning satisfaction. Thirdly, the total effect of online learning satisfaction was as follows. Course production had the greatest effect, followed by course activities and student-student interactions, followed by course materials. It was the learning evaluation that showed the least effect. This study can provide empirical reference for college teachers on how to continuously improve online teaching and increase students' satisfaction with online learning.

• ETRI Journal
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• v.42 no.2
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• pp.292-304
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• 2020

As side-channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side-channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning-based side-channel analysis. In particular, we outline how deep learning is applied to side-channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.3
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• pp.872-880
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• 2016

The purpose of this study is to find the research trends relating to flipped learning through keyword network analysis. For investigating this topic, final 100 papers (removed due to overlap in all 205 papers) were selected as subjects from the result of research databases such as RISS, DBPIA, and KISS. After keyword extraction, coding, and data cleaning, we made a 2-mode network with final 202 keywords. In order to find out the research trends, frequency analysis, social network structural property analysis based on co-keyword network modeling, and social network centrality analysis were used. Followings were the results of the research: (a) Achievement, writing, blended learning, teaching and learning model, learner centered education, cooperative leaning, and learning motivation, and self-regulated learning were found to be the most common keywords except flipped learning. (b) Density was .088, and geodesic distance was 3.150 based on keyword network type 2. (c) Teaching and learning model, blended learning, and satisfaction were centrally located and closed related to other keywords. Satisfaction, teaching and learning model blended learning, motivation, writing, communication, and achievement were playing an intermediary role among other keywords.

• The Journal of Information Systems
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• v.31 no.1
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• pp.219-238
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• 2022

Purpose The purpose of this study is to investigate the structural relationships between interest, interaction level, presence, which are the characteristics of metaverse, learning immersion, and learning satisfaction, which are learning factors. Design/methodology/approach A questionnaire survey technique was used to achieve the purpose of the study. A questionnaire survey was conducted from November 22 to December 5, 2021, with students with experience in non-face-to-face classes using Gather Town and a total of 114 copies of the questionnaire excluding those with insincere answers were used for empirical analysis. SPSS Win ver.23.0 was used for basic statistical analysis, and AMOS 22.0 was used for the establishment and analysis of a structural equation model. Findings According to the study findings, interest and interaction levels had effects on learning immersion and learning presence, self-efficacy on learning presence, and learning immersion and learning presence on learning satisfaction. This study is meaningful in that it conducted an empirical study to find variables for improving learning immersion by conducting classes based on metaverse. Based on the findings of this study, it was found that interest and interaction, which are the biggest characteristics of metaverse, sustain learning participation and immersion and increase presence thereby enhancing learning satisfaction so that the possibilities of metaverse as a next generation education platform passing the limit of existing real time video platforms can be peeped.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.572-587
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• 2020

Experience API provides a learner-centered model for learning data collection and learning process recording. In particular, it can record learning data from multiple data sources. Therefore, Experience API provides very good support for ubiquitous learning. In this paper, we put forward the architecture of ubiquitous learning system and the method of reading the learning record from the ubiquitous learning system. We analyze students' learning behavior from two aspects: horizontal and vertical, and give the analysis results. The system can provide personalized suggestions for learners according to the results of learning analysis. According to the feedback from learners, we can see that this u-learning system can greatly improve learning interest and quality of learners.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.103-113
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• 2018

The purpose of this study supports the establishment of national e-learning policy by analyzing e-learning status and current status of higher education. Enhance the competitiveness of higher education through sharing information between universities. And to improve e-learning quality management. We surveyed the current status of e-learning in 341 universities and questionnaires about e-learning content, e-learning application form, e-learning platform status was surveyed through each school's learning management system. As a result, the infrastructure of e-learning, the rate of platforms secured, and the contents are increasing gradually each year; however, still, not all students can receive the services equally. Dedicated servers and learning management systems were secured by more than 70% of general universities. In the current development status of e-learning content, multimedia, animation, and text forms are gradually decreasing, but video contents are increasing every year. Most of the online contents were used in the e-learning contents by application type, and blended learning, flipped learning, and mooc is not yet actively used since they are still in the beginning stage. Learning analysis techniques should be supported in order to easily use online learning contents such as flipped learning and mooc. We suggest that the effectiveness of e-learning should be measured and the current state of learning analysis for customized learning should be done. This study aims to contribute to the improvement of competitiveness of higher education by sharing information about e-learning among universities as a basis for improvement of e-learning policy. Future tasks are to improve the customized learning environment by adding whether the system environment for learning analysis is provided at the time of the survey.

• Journal of the Korean Institute of Educational Facilities
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• v.27 no.5
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• pp.13-23
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• 2020

From the perspective of smart learning space, this study aims to take a look at the learning space of college students who do online remote learning in the COVID-19 era, and analyze their demands on what smart learning space they want. Furthermore, this study intends to provide valuable implications for the technology-enhanced learning environments by deriving design elements that make up the university's smart learning space. To achieve these goals, we conducted a review of prior studies, interviews with experts, and case analysis on smart learning spaces of major Korean and foreign universities, which are considered as innovative cases. Additionally, in order to confirm the difference between the importance level recognized by the college students and the current performance level for nine components of technology and for ten components of spaces and facilities, a paired t-test and an Importance-Performance Analysis (IPA) were carried out. According to the result of IPA analysis, Internet of Things from the technological aspects, a desk that can supply power and a flexible learning space from the aspects of spaces and facilities were found to have much lower performance than the importance. This result is meaningful in suggesting key design components for smart campus development in the post-COVID-19 world.

• Educational Technology International
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• v.22 no.2
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• pp.169-197
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• 2021

Learning agility as high potentials is drawing attention as a competency for leading an uncertain future society. The present study aims to determine the factors of learning agility in higher education context for future society. To address this goal, Major factors related to learning agility were derived through literature review and statistically verified. For statistical analysis, the nationwide data were collected from 1,000 undergraduate students in South Korea by National Youth Policy Institute. The participants asked to answer 29 items of learning agility questionnaires (LAQ). The collected data were analyzed by descriptive statistical analysis, exploratory factor analysis, and confirmatory factor analysis. As a result, learning agility items were verified normality and reliability. Learning agility was identified seven factors; challenging mind, learning responsibility, reflecting experience, intellectual curiosity, systemic thinking, change adaptability, and logical thinking. Also, the structural model fit of the seven factors of learning agility was also confirmed to be good. Based on the findings of the present study, empirical, theoretical, and practical contributions were presented, and suggestions for further research were proposed in detail.