• Journal of Elementary Mathematics Education in Korea
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• v.22 no.2
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• pp.143-159
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• 2018

Since the mathematics learning achievement level is closely related to problem-solving ability, it is necessary to understand the relationship between problem-solving ability and meta-affect ability from the point of view of general mathematics learning ability. In this study, we compared the frequency analysis and the case analysis of the functional aspects of the meta-affect in elementary school students' problem-solving processes according to mathematics learning achievement level in parallel with frequency analysis and case analysis. In other words, the frequency of occurrence of meta-affect, the frequency of meta-affective type, and the frequency of meta-functional types of meta-affect were compared and analyzed according to the mathematics learning achievement level in the collaborative problem-solving activities of small group members with similar mathematics learning achievement level. In addition, we analyzed the representative cases of meta-affect by meta-functional types according to the mathematics learning achievement level in detail. As a result, meta-affect in problem-solving processes of the upper level group acted as relatively various types of meta-functions compared to the lower level group. And, the lower level group, the more affective factors acted in the problem-solving processes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2067-2081
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• 2024

Recently, image analysis research has been actively conducted due to the accumulation of big image data and the development of deep learning. Image analytics research has different characteristics from other data such as data size, real-time, image quality diversity, structural complexity, and security issues. In addition, a large amount of data is required to effectively analyze images with deep-learning models. However, in many fields, the data that can be collected is limited, so there is a need for meta learning based image analysis technology that can effectively train models with a small amount of data. This paper presents a comprehensive survey of meta-learning-based object-tracking techniques. This approach comprehensively explores object tracking methods and research that can achieve high performance in data-limited situations, including key challenges and future directions. It provides useful information for researchers in the field and can provide insights into future research directions.

• The Journal of Korean Academic Society of Nursing Education
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• v.18 no.2
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• pp.323-331
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• 2012

Purpose: Level of meta-cognition of students has been regarded as one of the crucial factors on web-based learning. This study aimed to describe interaction type in small group discussion of the nursing graduate students and to investigate learning consequences and interaction types in group discussion on meta-cognition level. Method: Twenty six graduate nursing students attending the class on-line at the K university in Seoul were included in the study. We measured their meta-cognition level and learning attitude. We also scored their individual and group reports as well as analyzed interaction type by reviewing the dialogue of the group discussion. Results: The participants showed low frequency of exploratory interaction and high frequency of integrative interaction in the cognitive interaction category. They showed frequent modification interaction in the meta-cognitive interaction category. Interestingly, the students with lower level of meta-cognition achieved significantly greater scores in the individual assignments. High functioning group consisting of the students with high meta-cognitive level produced greater group report. Conclusion: A new strategy is needed to encourage in-depth interaction in a group discussion of nursing students. Meta-cognitive level of the students should be considered to form a small group for discussion in order to improve group activities.

• Journal of Korean society of Dental Hygiene
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• v.20 no.2
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• pp.221-232
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• 2020

Objectives: The aim of this study was to suggest a method for training students majoring in dental hygiene with a sense of professionalism by identifying meta-cognition, efficient learning strategies, and self-directedness necessary to become a spontaneous, self-controlled learner. Methods: A survey was conducted on 316 students majoring in dental hygiene, and collected data were analyzed using SPSS, version 23.0. A post-hoc analysis was performed using descriptive statistics, t-test, analysis of variance, and Duncan's multiple range test, and Pearson's correlation coefficient was used to assess the relationship among meta-cognition, learning strategy, and self-directedness. Results: The meta-cognition, learning strategy, and self-directedness scores of students majoring in dental hygiene were 3.25, 3.08, and 3.12, respectively. Meta-cognition was significant because the grade was lower, and the previous semester grade and major satisfaction were higher. Learning strategy was significant because the previous semester grade and major satisfaction were higher among general high school students. Self-directedness was significantly low in students whose self-conviction score was below 2.0 in terms of the previous semester grade and significantly high with high self-satisfaction. Conclusions: Instructors at the dental hygiene department should acknowledge the importance of meta-cognition, find various teaching methods to improve learning strategy, and encourage students to participate in class by enhancing self-directedness in learning.

• International Journal of Contents
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• v.16 no.3
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• pp.18-32
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• 2020

The aim of this study was to perform a meta-analysis of the learning outcomes of immersive learning technologies in English language teaching (ELT). This study examined 12 articles, yielding a total of 20 effect sizes. The Comprehensive Meta-Analysis (CMA) program was employed for data analysis. The findings revealed that the overall effect size was 0.84, implying a large effect size. Additionally, the mean effect sizes of the dependent variables revealed a large effect size for both the cognitive and affective domains. Furthermore, the study analyzed the impact of moderator variables such as sample scale, technology type, tool type, work type, program type, duration (sessions), the degree of immersion, instructional technique, and augmented reality (AR) type. Among the moderators, the degree of immersion was found to be statistically significant. In conclusion, the study results suggested that immersive learning technologies had a positive impact on learning in ELT.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.95-100
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• 2021

This paper introduces model-based meta reinforcement learning as a control for the manipulator of an underwater construction robot. Model-based meta reinforcement learning updates the model fast using recent experience in a real application and transfers the model to model predictive control which computes control inputs of the manipulator to reach the target position. The simulation environment for model-based meta reinforcement learning is established using MuJoCo and Gazebo. The real environment of manipulator control for underwater construction robot is set to deal with model uncertainties.

• Journal of the Korean Society of Radiology
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• v.13 no.3
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• pp.407-414
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• 2019

Recently, after the reorganization as the basis of NCS education, various learning methods are being sought for improving the basic occupational ability and job ability required by NCS, and the evaluation method accordingly is urgently needed. The purpose of this study was to evaluate the applicability of meta-cognitive learning and Havruta learning as evaluation cases in order to improve the job skills and basic skills required in the NCS curriculum. As a result, the meta-cognitive learning response sample statistic showed an average of 2.6883 when the pre-meta-cognitive learning questionnaire was a 5-point scale, and an average of 4.2468 after the meta-cognitive learning questionnaire. The correlation coefficient was 0.782 and the significance probability was 0.045. In the case of the Havruta learning correspondence sample statistic, the average of 3.1515 when the preliminary Havruta learning questionnaire was a 5 point scale and the average of the post-Havruta learning questionnaire was 4.3853, which was improved by 1.23 points. The correlation coefficient was 0.631 and the significance probability was 0.049. Meta-cognitive learning and Havruta learning were found to be correlated. The mean of meta cognition was 3.4675 and the mean of Havruta was 3.7684. Metacognitive learning and Havruta learning were -0.042 And there was no statistically significant difference. Therefore, the learning method to improve the job ability should be applied considering the characteristics of the subject.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.2
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• pp.217-239
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• 2010

This study is to understanding characteristics of Mathematical gifted children by comparing and analyzing of the learning strategies between gifted children and average students. The result of this study is as below. First, the mathematical gifted children's application ability on the cognitive meta-cognitive strategies and learning resources management strategies was higher than average students. Second, in case of learning resources management strategies between gender, male mathematical gifted students's t-test showed higher than female gifted students. Also, in case of average students, male student's t-test for the learning motive was higher than average female students. Third, mathematical gifted children are positive correlation among the learning motive, self-efficacy, cognitive meta-cognitive strategies, and learning resources management strategies. And in case of average student, it had a positive correlation among the learning motive, self-efficacy, and cognitive meta-cognitive strategies. But there is no correlation between learning strategies and cognitive meta-cognitive strategies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.689-696
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• 2015

In this study, a learning system based on smart learning is proposed so that students with learning disabilities can learn the effective use of meta-cognitive to solve problems arising during the learning process. The features of the proposed system are as follow. First, it is possible to achieve students' individualized learning by use of smart devices and smart education system. Second, it is possible to provide the constant repetition learning for students. Third, students can improve their achievement using the proposed app. The proposed smart education system using meta-cognition was applied to some learning disabilities students. The following results were obtained. First, the disabled students could have an interest in learning math and improve confidence. Second, the student's mathematical problem-solving skills have improved. Third, students' individualized and self-directed learning was achieved.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.