• The Mathematical Education
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• v.59 no.1
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• pp.31-45
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• 2020

Despite the trend of the times and the government's efforts to implement policies, a number of students, having difficulties in learning math, are still growing. Reflecting this, many studies related to underachiever of learning mathematics were conducted in the field of mathematics education. Most of these studies, however, were intended to find the cause of underachievers of learning mathematics or experimental studies that applied the specific teaching procedures to the underachievers of learning mathematics and found their effectiveness in terms of academic achievement, compared it beforehand. Thus, in this study, 49 studies, from including theses and published journal papers from 2001, were meta-analyzed to find out how effective the teaching treatment for underachievers of learning mathematics has improved academic performance. As a result of this analysis, we found that teaching treatment generally have moderate effect sizes for children with having difficulties in learning mathematics. It was also possible to analyze the effect of various interventions and to obtain some suggestions on which circumstances the greatest effect could be achieved. Teaching treatments for underachiever of learning mathematics could have greater effectiveness in elementary school level, institution certified testing tool was used, targeted for each individual, taught by peer student, lasted for at least 8 weeks, and using teaching aids and ICT tools.

• Convergence Security Journal
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• v.8 no.4
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• pp.81-89
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• 2008

This paper introduces u-learning service model based on context awareness. Also, it concentrates on agent-based WPAN technology, OSGi based middleware design, and the application mechanism such as context manager/profile manager provided by agents/server. Especially, we'll introduce the meta structure and its management algorithm, which can be updated with learning experience dynamically. So, we can provide learner with personalized profile and dynamic context for seamless learning service. The OSGi middleware is applied to our meta structure as a conceptual infrastructure.

• Journal of The Korean Association For Science Education
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• v.23 no.4
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• pp.375-385
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• 2003

In an effort to provide information about the effect of Microcomputer-Based Laboratory (MBL) use in science teaching and learning on student achievement and attitudes, a review of research analyzed studies was done between 1981 and 2001, using a meta-analysis procedure. Thirty-seven published and unpublished studies were reviewed. Use of MBL was shown to be potentially effective in the following condition of class; two students, physics teaching, more than one topic, or at the college level. Appropriate research design strategies, financial support (including hardware and software), and the use of more than one instrument for assessing the effect of the MBL instruction are crucial factors for more informative research studies. While helpful in many respects, the prior research revealed a number of problems related to the use of MBL in school science teaching and learning. The prior research does not support the desired intention described in the theory-based outcomes and reveals so little about how teachers and students use MBL, how it influences their teaching and learning, and how effectively it fits into the existing science curriculum. In order to know if the integration of MBL in the existing school science is worth it or not, more careful research design and comprehensive research should be done.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3138-3150
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• 2021

Multimedia services on the Internet are continuously increasing. Accordingly, the demand for a technology for efficiently delivering multimedia traffic is also constantly increasing. The multicast technique, that delivers the same content to several destinations, is constantly being developed. This technique delivers a content from a source to all destinations through the multicast tree. The multicast tree with low cost increases the utilization of network resources. However, the finding of the optimal multicast tree that has the minimum link costs is very difficult and its calculation complexity is the same as the complexity of the Steiner tree calculation which is NP-complete. Therefore, we need an effective way to obtain a multicast tree with low cost and less calculation time on SDN-based smart network platforms. In this paper, we propose a new multicast tree generation algorithm which produces a multicast tree using an agent trained by model-based meta reinforcement learning. Experiments verified that the proposed algorithm generated multicast trees in less time compared with existing approximation algorithms. It produced multicast trees with low cost in a dynamic network environment compared with the previous DQN-based algorithm.

• The Journal of Korean Association of Computer Education
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• v.19 no.2
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• pp.21-29
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• 2016

The introduction of educational programming language has changed programming learning environment to learn programming through various learning activities. We need to analyze how effective these learning activities could be in programming learning. We performed a meta analysis of the programming learning effects according to 8 types of learning activities. The 44 studies were collected from 1993 to 2015 for the meta analysis. The study data of 77 were extracted among 44 studies through several steps. The major results were as follows. The effect size of cognitive domain was shown to be mid-level with .595 and the effect size of affective domain was shown to be mid-level with .594. We analysed according to learning activities. The effect size were no significant difference between learning activities in the cognitive domain. But simulation, animation and mathematical activities was shown to be more consistent results and mid-level effect size. Although the effect size were no significant difference, the homogeneity was shown to be high in the affective domain. The implications were suggested from research findings. First, it is desirable that learners learn programming according to various learning activity themes. Second, instructors should pay attention to simulation, animation and mathmatics activities. Third, researchers need research to find another factors for effective learning.

• Journal of Korean Elementary Science Education
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• v.33 no.2
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• pp.322-334
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• 2014

In this study, the researcher aimed to increase productive reflection of pre-service elementary teachers through meta-analysis of their own reflective journals. The meta-analysis activities are expected to enhance their learning effectively as a kind of self-assesment. During 8 week simulation teaching, 26 pre-service teachers kept individual journal writing and the meta-analysis activities were implemented twice (after the 3rd and 8th week). Right after the first meta-analysis, the pre-service teachers' productive reflection increased clearly. However this would not guarantee the effect would last long time period. By analyzing 8 week reflective journals, reports on meta-analysis activities and small group interview, this study shed light on practical ways of enhancing reflective teacher education.

• Research in Community and Public Health Nursing
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• v.18 no.1
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• pp.146-155
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• 2007

Purpose: This study was performed to examine the degree of meta cognition, critical thinking and self-efficacy and to identify correlations among the meta cognition, critical thinking, and self-efficacy of nursing students studying through PBL. Method: The subjects were 140 nursing students who had studied through PBL over three terms at C College. Data were collected from August to September, 2005 using a structured questionnaire and analyzed using SPSS 10.0. Results: The mean score of meta cognition was 40.14 (SD=6.02), critical thinking was 181.46 (SD=14.49), and self-efficacy was 942.93 (SD=167.05). There was a statistically significant positive correlation between meta cognition and self-efficacy and age. Also, meta cognition had a positive correlation with appropriateness to nursing and interest in nursing knowledge, and critical thinking had a positive correlation with appropriateness to nursing, interest in nursing knowledge, interest in lab on campus and interest in clinical practicum. There were statistically significant positive correlations among meta cognition, critical thinking and self-efficacy. Conclusion: Based on above results, further research should be done about many factors influencing nursing students' problem solving abilities for the development and application of many teaching methods for improving nursing students' meta cognition, critical thinking and self-efficacy.

• The Journal of Korean Association of Computer Education
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• v.17 no.5
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• pp.53-68
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• 2014

The purpose of this study has been found to be effective using ICT teaching-learning than traditional teaching-learning method on academic achievement applying the meta-analysis method. This study set the following questions to be answered. 1. The 85% subject of analysis of ICT-using teaching-learning selected in this study turned out to be clear effective than traditional teaching-learning method in academic achievement of students. 2. ICT-using teaching-learning is more effective for academic achievement of elementary school students and university students than for middle school students and high school students relatively. 3. ICT-using teaching-learning is a most effective method in subject of art and physical education and social subject but less effective in mathematics subject.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.77-82
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• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.