• East Asian mathematical journal
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• v.38 no.4
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• pp.497-516
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• 2022

In this study, we tried to devise a method to activate meta-affect in the aspect of supporting mathematics teaching and learning according to the need to find specific strategies and teaching and learning methods to activate learners' meta-affect in mathematics subjects, which are highly influenced by psychological factors. To this end, the definitional and conceptual elements of meta-affect which are the basis of this study, were identified from previous studies. Reflecting these factors, a teaching and learning model that activates meta-affect was devised, and a meta-affect activation strategy applied in the model was constructed. The mathematics teaching and learning model that activates meta-affect developed in this study was refined by verifying its suitability and convenience in the field through expert advice and application of actual mathematics classes. The developed model is meaningful in that it proposed a variety of practical teaching and learning methods that activate the meta-affect of learners in a mathematical learning situation.

• The Journal of Korean Association of Computer Education
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• v.6 no.3
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• pp.75-85
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• 2003

This study develops the System(MetaGene) to create meta-data for learning object based on SCORM including meta-data of Assets. SCO, Contents Aggregation and metadata of Contents Package. API function cocle is embeded in Learning Object for interfacing API adopter in LMS to support SCORM and for tracking on learning process based on data models. Also, the learning objects are packaged the PIF(Packaged Interchange File) to transmit with LMS. MetaGene is verified by $SCORM^{(TM)}$ Conformance TestSuite for meta-data of learning objects, manifest file of Contents Packaging.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.68-74
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• 2019

Along with the deeper architecture in the deep learning approaches, the need for the data becomes very big. In the real problem, to get huge data in some disciplines is very costly. Therefore, learning on limited data in the recent years turns to be a very appealing area. Meta-learning offers a new perspective to learn a model with this limitation. A state-of-the-art model that is made using a meta-learning framework, Meta-SGD, is proposed with a key idea of learning a hyperparameter or a learning rate of the fast adaptation stage in the outer update. However, this learning rate usually is set to be very small. In consequence, the objective function of SGD will give a little improvement to our weight parameters. In other words, the prior is being a key value of getting a good adaptation. As a goal of meta-learning approaches, learning using a single gradient step in the inner update may lead to a bad performance. Especially if the prior that we use is far from the expected one, or it works in the opposite way that it is very effective to adapt the model. By this reason, we propose to add a weight term to decrease, or increase in some conditions, the effect of this prior. The experiment on few-shot learning shows that emphasizing or weakening the prior can give better performance than using its original value.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.83-91
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• 1997

DELVAUX is an inductive learning environment that learns Bayesian classification rules from a set o examples. In DELVAUX, a genetic a, pp.oach is employed to learn the best rule-set, in which a population consists of rule-sets and rule-sets generate offspring by exchanging some of their rules. We have explored a meta-learning a, pp.oach in the DELVAUX learning environment to improve the classification performance of the DELVAUX system. The meta-learning a, pp.oach learns the bias of a classifier so that it can evaluate the prediction made by the classifier for a given example and thereby improve the overall performance of a classifier system. The paper discusses the meta-learning a, pp.oach in details and presents some empirical results that show the improvement we can achieve with the meta-learning a, pp.oach.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.98-107
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• 2018

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.23 no.4
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• pp.430-439
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• 2016

Purpose: The purpose of this study was to contribute to the development of an efficient teaching-learning method by analyzing effects of writing reflective journals on meta-cognition and problem solving ability in nursing students in education applying blended learning for fundamental nursing skills. Methods: The research design was a one-group pretest-posttest design, done to assess changes in meta-cognition and problem solving ability. Participants were 63 nursing students taking the fundamental nursing skills course at one college in Gyeonggi Province. The course was offered from March 21 to June 3, 2016. Data were collected using pre and post tests given before and after writing of reflective journals in blended learning. Data were analyzed using t-test, ANOVA, $Scheff{\acute{e}}^{\prime}s$ test and paired t-test with SPSS Statistics version 20.0. Results: The results of this study show that scores for meta-cognition and problem solving ability of these students were all above average. There was a statistically significant difference in meta-cognition between pre and post writing of reflective journals but not for problem-solving ability. Conclusion: The findings indicate that writing a reflective journal in blended learning is an efficient teaching-learning method to improve meta-cognition in nursing students.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.35-40
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• 2015

The paper proposes a meta-learning approach for building multi-classifier systems in a GA-based inductive learning environment. In our meta-learning approach, a classifier consists of a general classifier and a meta-classifier. We obtain a meta-classifier from classification results of its general classifier by applying a learning algorithm to them. The role of the meta-classifier is to evaluate the classification result of its general classifier and decide whether to participate into a final decision-making process or not. The classification system draws a decision by combining classification results that are evaluated as correct ones by meta-classifiers. We present empirical results that evaluate the effect of our meta-learning approach on the performance of multi-classifier systems.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.1
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• pp.5-15
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• 2021

Purpose: This study aimed to provide a systematic review and meta-analysis of research on flipped learning effects applied to university students. Methods: The random effect model was applied to 21 papers to calculate the effect size. To verify the moderation effect, a meta regression analysis and meta ANOVA were performed. Publication bias was verified through a funnel plot, and then an Egger's regression test was conducted. Results: The overall average effect size was .69 (95% CI: .51-.87), showing a median effect size, which was statistically significant. The outcome variables were in the order of learning motivation (Hedges' g=.83), self-directed learning (Hedges' g=.78), learning achievement (Hedges' g=.66), and efficacy (Hedges' g=.50), which were statistically significant. Conclusion: Flipped learning was found to be statistically significant in improving self-directed learning, learning motivation, efficacy, and learning achievement amng university students. It is suggestd that this method be actively applied in university education.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.395-398
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• 2007

최근 인공지능 분야에서는 강화학습(Reinforcement Learning)에 대한 관심이 크게 증폭되고 있으며, 여러 관련 분야에 적용되고 있다. 본 논문에서는 강화학습 기법 중 액터-크리틱 계열에 속하는 RLS-NAC 알고리즘을 활용하여 Kimura의 기는 로봇의 이동을 다룰 때에 중요 파라미터의 결정을 위하여 meta-learning 기법을 활용하는 방안에 고려한다.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.125-133
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• 2017

Recently, as the programming education has become essential in school, discussion of how to teach programming has been important. This study performed a meta-analysis of the effect size depending on the teaching and learning method for the programming education. 78 research data selected from 45 papers were analyzed from cognitive and affective aspects according to dependent variables. The analysis from the cognitive aspect showed that there was no statistically significant difference in the effect size depending on whether or not the teaching and learning method was specified in the research paper. Meta-analysis of the research data where the teaching and learning method was designated displayed significances in CPS, PBL and Storytelling. Unlike the cognitive aspect, the analysis from the affective aspect showed that the effect size of the research data without the specified teaching and learning method was larger than those with specified teaching and learning method with a statistical significance. Meta-analysis of the data according to the teaching and learning method displayed no statistical significance. Based upon these research results, this study suggested implications for the effective programming education.