• Communications of Mathematical Education
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• v.35 no.4
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• pp.359-376
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• 2021

In this study, in accordance with the research trend that the learner's emotions expressed positively or negatively in mathematics learning or the learner's beliefs and attitudes toward mathematics learning affect the results of mathematics learning, the learner's emotions and affective factors are analyzed in the learner's own learning. A power that can be adjusted according to a goal or purpose is needed, and I tried to explain this power through meta-affect. To this end, the meaning of the definitional and conceptual factors of meta-affect was explored based on prior studies. Affective factors of meta-affect were viewed as emotions, attitudes, and beliefs, and conceptual factors of meta-affect were viewed as awareness, evaluating, controlling, utilization, and monitoring, and the meaning of each conceptual factor was also defined. In this study, the conceptual factors and meanings of meta-affect in terms of using them to help in learning mathematics by controlling them, beyond the identification or examination of the characteristics of the affective factors, which are meaningfully dealt with in the field of mathematics education.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.1
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• pp.148-155
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• 2014

The purpose of this research was to analyze the effects of smart learning. By using meta analysis method, twenty MA and Ph.D degree papers published from 2006 to 2013 were analyzed and 104 effect sizes were calculated. Followings were the results of the research: (a) Smart learning turned out to be more statistically effective comparing to traditional education. The total mean effect size was .886 and the value of U3 was 66.53%. (b) All effect size of sub dependent variables(ie, academic achievement, learning satisfaction, learning attitude) were also effective by adapting smart learning. (c) The moderated variables likes learner characteristics, learning content, and interaction had high effect sizes. Operation system variable had a low effect size but it was not significant.

• Journal of Dental Rehabilitation and Applied Science
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• v.38 no.4
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• pp.196-203
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• 2022

Purpose: This study aimed to evaluate the accuracy and clinical usability of an identification model using deep learning for 79 dental implant types. Materials and Methods: A total of 45396 implant fixture images were collected through panoramic radiographs of patients who received implant treatment from 2001 to 2020 at 30 dental clinics. The collected implant images were 79 types from 18 manufacturers. EfficientNet and Meta Pseudo Labels algorithms were used. For EfficientNet, EfficientNet-B0 and EfficientNet-B4 were used as submodels. For Meta Pseudo Labels, two models were applied according to the widen factor. Top 1 accuracy was measured for EfficientNet and top 1 and top 5 accuracy for Meta Pseudo Labels were measured. Results: EfficientNet-B0 and EfficientNet-B4 showed top 1 accuracy of 89.4. Meta Pseudo Labels 1 showed top 1 accuracy of 87.96, and Meta pseudo labels 2 with increased widen factor showed 88.35. In Top5 Accuracy, the score of Meta Pseudo Labels 1 was 97.90, which was 0.11% higher than 97.79 of Meta Pseudo Labels 2. Conclusion: All four deep learning algorithms used for implant identification in this study showed close to 90% accuracy. In order to increase the clinical applicability of deep learning for implant identification, it will be necessary to collect a wider amount of data and develop a fine-tuned algorithm for implant identification.

• School Mathematics
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• v.16 no.3
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• pp.409-444
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• 2014

There have been many discussions of teaching and learning mathematics through modeling activities in mathematics education research community. Although there has been some agreement regarding modeling activity as an alternative way to support mathematics teaching and learning, there is still no clear consensus on these issues. This paper reports a case study which aims to identify ways to design modeling tasks and instruction to foster meta-level learning, and investigate how modeling activities can facilitate meta-level learning. From the results of teaching experiment, this study examines the potential of modeling activities in mathematics teaching and learning.

• Journal of Korean Elementary Science Education
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• v.40 no.1
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• pp.13-21
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• 2021

This study analyzed the effects of 'online biology learning using E-learning system' on elementary school students' science-related attitudes. Samples of the study were composed of 95 sixth-grade students of N elementary school in Seoul, Korea. The learning was conducted for 11 times over a month. The main results of this study are as follows. First, for the paired t-test, a statistically significant difference between the pre and post scores of science-related attitudes was found. After conducting the online biology learning science related attitudes scores of students generally declined. "The boredom caused by simply watching online biology contents" is the decisive cause of the decline in science-related attitude scores analyzed through interviews. Second, in ANCOVA, according to 'levels of meta-cognition'. there was no statistically significant difference in scores of science-related attitudes. but, there was statistically significant difference in science-related attitudes according to 'adoption of scientific attitudes'. Students of high meta-cognition type showed a greater decline in scores than students of low meta-cognition type. Based on the results of this study, implications for research of online biology education and elementary science education are discussed.

• School Mathematics
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• v.13 no.3
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• pp.469-484
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• 2011

Students experience various affects in solving mathematical problem and learning mathematics. Focusing on meta-affect in affective domain, we explored the types of meta-affect. Our research provides illustrative examples and analysis of meta-affect during solving problem. As a result, meta-affect has four types i.e. monitoring of affect, evaluation of emotion, control of emotion, and utilization of affect. And meta-affect is a main key to decide how to handle affect and influence student's cognitive strategies and affect.

• Journal of KIISE:Software and Applications
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• v.36 no.6
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• pp.498-507
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• 2009

There has been insufficient studies on the effect of programming language learning on logical thinking ability. Each study result on the improvement degree and items of logical thinking ability is different according to the object of the study, its method, and the learning subject, which makes the generalization process difficult. Moreover, the necessity of programming language learning seems not proved, because it is not apparent whether the improvement of logical thinking ability is due to the advancement of knowledge or programming language learning. In this study, we instructed educational programming languages to elementary students in 6th grade for 7 hours, investigated its effect on logical-thinking ability by the meta-cognition level, and compared the result with that of computer skill learning. As a result, for Dolittle, LOGO, and Powerpoint learning groups, the logical-thinking ability of high meta-cognition level students has increased with significance, but that of low meta-cognition level students has significantly increased for Dolittle and LOGO groups only. However, regardless of meta-cognition levels, there was no significant difference of logical-thinking ability between all three groups.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.1
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• pp.128-136
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• 2017

The purpose of this research is to analyze the effects of smart learning in both general education and fisheries & marine education through meta-analysis. To find the effects size, we had collected 112 studies from graduation theses and journal articles. Followings are the results of the research: (a) Smart learning turns out to be more statistically effective comparing to traditional education. The total effect size of smart learning is .768 and the value of U3 is 61.50%. (b) There is no significant difference between general education and fisheries & marine education in the view of effect size. (c) There is a significant difference in subjects, type of publication, and size of members in experimental group. High school student group has the most effect size of smart learning.

• Smart Structures and Systems
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• v.32 no.2
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• pp.123-133
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• 2023

This paper proposes a thermography-based coating thickness estimation method for steel structures using model-agnostic meta-learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured using an infrared (IR) camera. The measured heat responses are then analyzed using model-agnostic meta-learning to estimate the coating thickness, which is visualized throughout the inspection surface of the steel structure. Current coating thickness estimation methods rely on point measurement and their inspection area is limited to a single point, whereas the proposed method can inspect a larger area with higher accuracy. In contrast to previous ANN-based methods, which require a large amount of data for training and validation, the proposed method can estimate the coating thickness using only 10- pixel points for each material. In addition, the proposed model has broader applicability than previous methods, allowing it to be applied to various materials after meta-training. The performance of the proposed method was validated using laboratory-scale and field tests with different coating materials; the results demonstrated that the error of the proposed method was less than 5% when estimating coating thicknesses ranging from 40 to 500 ㎛.

• Smart Media Journal
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• v.12 no.1
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• pp.24-31
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• 2023

Meta-learning is metacognition that instantly distinguishes between knowing and unknown. It is a learning method that adapts and solves new problems by self-learning with a small amount of data.A few-shot learning method is a type of meta-learning method that accurately predicts query data even with a very small support set. In this study, we propose a method to solve the limitations of the prototype created with the mean-point vector of each class. For this purpose, we use the few-shot learning method that created the prototype used in the few-shot learning method as the median prototype. For quantitative evaluation, a handwriting recognition dataset and mini-Imagenet dataset were used and compared with the existing method. Through the experimental results, it was confirmed that the performance was improved compared to the existing method.