• Journal of Information Processing Systems
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• v.17 no.3
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• pp.453-461
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• 2021

Many deep learning approaches have been studied for image classification in computer vision. However, there are not enough data to generate accurate models in medical fields, and many datasets are not annotated. This study presents a new method that can use both unlabeled and labeled data. The proposed method is applied to classify cervix images into normal versus cancerous, and we demonstrate the results. First, we use a patch self-supervised learning for training the global context of the image using an unlabeled image dataset. Second, we generate a classifier model by using the transferred knowledge from self-supervised learning. We also apply attention learning to capture the local features of the image. The combined method provides better performance than state-of-the-art approaches in accuracy and sensitivity.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.969-975
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• 2011

The purpose of this study is to investigate the effects of learners' self-motivational attitudes, use of instructional function, and understanding of successful learning on achievement in college e-learning classes. The study analyzed 297 college students' questionnaire about their internet learning attitudes based on how they understand e-learning and use various internet functions for their learning achievement. After factor analyses, the results found that there were three major factors such as self-motivational attitude, use of instructional function, and understanding of successful learning out of 15 survey items. Multiple regression showed that the self-motivational factor affects the learning achievement with overall three factors analyses. This result indicates that college e-learning classes should focus on the analysis of learners' self-motivational issues in college e-learning classes. This study suggest that the relationship between learners' e-learning experience and learning achievement should be examined in the near future to show how it affects on learners e-learning class management and their achievement.

• Korean Journal of Child Studies
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• v.29 no.3
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• pp.273-288
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• 2008

Types of motivation for self-determination studied in the current research included intrinsic motivation (IM) to know, IM to accomplish, IM to experience stimulation; external, introjected, identified regulation; and amotivation as related to children's self-regulated learning strategies (use of cognitive strategy and self-regulation). The Motivated Strategies for Learning Questionnaire (Pintrich & DeGroot, 1990) and the Academic Motivation Scale (Vallerand et al., 1992, 1993) were administered to 123 $5^{th}$ graders (63 girls, 60 boys). Data were analyzed by Pearson's correlation, multiple regression, and canonical correlation analysis. Results indicated that IM to accomplish, IM to know, and identified regulation positively and amotivation negatively affected self-regulated learning strategies. IM to accomplish and IM to know positively and amotivation negatively contributed to use of cognitive strategy and self-regulation.

• Educational Technology International
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• v.6 no.1
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• pp.81-101
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• 2005

Campus-based universities have provided face-to-face instruction traditionally. But recently, it is becoming a trend that they provide blended learning which combines e-learning and f2f instruction. Therefore, traditional university has been installing the ICT related convenience for the faculty and students to use easily to their classes. The purpose of this study is to develop quality indicators of ICT-related support for proper blended learning in traditional campus-based universities. This indicators are used for measuring the quality of ICT-related services at university level for quality education. To this end, first, we reviewed literature about quality indicators of university evaluation and e-learning. Second,we did case study. We selected and analyzed one university for a case, And we identified what elements are perceived important to faculty for more efficient use of technology to their class. Third, we summarized all this data and established the quality indicators framework of ICT-related components for blended learning in campus-based universities. Then, these indicators were revised after the expert evaluation. And then 10 experts and practitioners scored importance rating. Finally, we sum them up to 17 indicators and 48 sub-indicators in three phases (input, process, output). Among them, e-learning related organization or body, usability of Learning Management System, and quality assessment system got the highest scores. These indicators are supposed to contribute to measure the quality of ICT-related environment for blended learning and to provide informations about what is required for efficient blended learning in the campus-based universities.

• Journal of The Korean Association For Science Education
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• v.18 no.3
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• pp.327-336
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• 1998

The purposes of this study were to investigate 6th-graders' use of cognitive strategies and metacognitive strategies in usual learning and testing situations, and to compare the difference in the use of the strategies by students' science achievement, learning motivation, and gender. The relationship among these strategies, science achievement, and learning motivation were also examined, and the portion of variance of explanation for achievement score was studied by a multiple regression analysis. The results showed that high-achieving students used more cognitive strategies and metacognitive strategies in usual learning and more cognitive strategies in testing situations than low-achieving students. Highly motivated students used more cognitive and metacognitive strategies than poorly motivated students in all situations. Elementary female students used more learning strategies than male students in usual learning. On the other hand, no gender differences was found to be significant in the use of strategies in testing situations. These learning strategies were significantly correlated with the science achievement and motivation scores. The cognitive strategies in usual learning accounted for the significant portion of the variance of the achievement score. Educational implications are discussed.

• Educational Technology International
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• v.20 no.2
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• pp.195-221
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• 2019

This study explored middle and high school students' learning experiences using e-textbooks in online learning courses. Data were collected from in-depth interviews. The interviewees for this study were 19 students who enrolled voluntarily in an online mathematics and science inquiry program, actively participated in the online learning. The students generally have high academic achievement and motivation for learning in science and mathematics. Data were analyzed based on a grounded theory approach. As a result, the characteristics of the online learning environment using e-textbooks were conceptualized via three different categories including temporal, spatial, and technical. Such characteristics of the learning environment were able to provoke self-directed learning, extended learning, interactive learning, in-depth learning, improved ICT literacy, and formation of positive emotions and learning habits. Most of the learners showed positive feedback towards the use of e-textbooks, while some mentioned the technical limitations compared to conventional paper-based learning. This study suggested that e-textbooks are likely to induce positive experiences for learners in the context of online learning, so it is necessary to design contents that utilize various functions and advantages of electronic teaching materials in order to use e-textbooks effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.610-632
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• 2024

Mobile Learning (M-learning) adoption and success in supporting students' learning engagement mainly depend on many factors. Therefore, this study systematically reviews the literature, synthesizes and analyzes the predictors of M-learning adoption, and uses success for students' learning engagement. Literature from 2016 to 2023 in various databases is covered in this study. Based on the review's findings, the factors that influence students' learning engagement when it comes to M-learning usage and adoption, can be divided into technical, pedagogical, and social factors. More specifically, technical factors include mobile devices availability and quality, connectivity to the internet, and user-friendly interfaces, pedagogical factors include effective instructional design, teaching methods, and assessment strategies, and social factors include motivation of students, social interaction and perceived enjoyment - all these factors have a significant impact on the M-learning adoption and use success. The findings of the review also indicated that M-learning has a key role in enhancing the learning engagement of students through different ways, like increasing their motivation, attention, and participation in their process of learning, paving the way for interaction and building relationships opportunities with peers and instructors, which in turn, can lead to strengthening the learning environment. The implications of these findings extend beyond immediate educational contexts, offering vital insights for future educational technology strategies and policy decisions, particularly in addressing global educational challenges and embracing technological advancements in learning.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.14 no.2
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• pp.27-40
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• 2003

In the society of lifelong learning, a school library has to play an important educational role in improving self-directed learning ability that school education is seeking. First of all, This study makes it clear that the goal of school library use education is to develop self-directed learning ability. It carries out a research into the present condition of school library use education which is an important educational role of a school library. Finally this study proposes some improvement plans in school library use education, as mentioned below : 1. Building an administration system of school library media center. 2. Re-establishing information literacy as culture education 3. Team teaching by a curriculum based approach

• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.233-240
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• 2011

Social Media is the open online tool and media platform for sharing and participation of users opinion, experience, viewpoiont, so general situation that is one-side flowing from production to consume doesn't act, and while use of two-way, user create contents use of sharing and participation. This social media include Blog, Social Network Service(SNS), Wiki, User Create Contents(UCC), Micro Blog, 5 types. In broad terms, Social Learning is self-learning that user sharing with coperation and collective intelligence through Social Media, and in few wards Social Learning is learning for Social Media. In this research, we define Social Media and Social Learning, and research of method of use of Elementary Education.

• Journal of Medicine and Life Science
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• v.21 no.3
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• pp.92-101
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• 2024

This study investigated how the use of a conversational artificial intelligence (AI) chatbot improved medical students' patient-centered communication (PCC) skills and how it affected their motivation to learn using innovative interactive tools such as AI chatbots throughout their careers. This study adopted a one-group post-test-only design to investigate the impact of AI chatbot-based learning on medical students' PCC skills, their learning motivation with AI chatbots, and their perception towards the use of AI chatbots in their learning. After a series of classroom activities, including metaverse exploration, AI chatbot-based learning activities, and classroom discussions, 43 medical students completed three surveys that measured their motivation to learn using AI tools for medical education, their perception towards the use of AI chatbots in their learning, and their self-assessment of their PCC skills. Our findings revealed significant correlations among learning motivation, PCC scores, and perception variables. Notably, the perception towards AI chatbot-based learning and AI chatbot learning motivation showed a very strong positive correlation (r=0.72), indicating that motivated students were more likely to perceive chatbots as beneficial educational tools. Additionally, a moderate correlation between motivation and self-assessed PCC skills (r=0.54) indicated that students motivated to use AI chatbots tended to rate their PCC skills more favorably. Similarly, a positive relationship (r=0.68) between students' perceptions of chatbot usage and their self-assessed PCC skills indicated that enhancing students' perceptions of AI tools could lead to better educational outcomes.