• Journal of Educational Research in Mathematics
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• v.25 no.1
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• pp.113-137
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• 2015

The objective of the present study is to systematically examine the effects of on the cognitive and affective domains of elementary, middle, and high school students by conducting a meta-analysis. To this end, this study selected 31 research papers that had analyzed the effects of applying, and performed a meta-analysis of the findings presented in each research paper. The results obtained from the meta-analysis are presented as follows. First, both the collaborative learning program and the peer tutoring program for underachieving students in math manifested an above average size of effect in the cognitive domain. In particular, the effect was the greatest at the elementary school level, and out of the two programs, peer tutoring was identified to have a sizable effect. Second, both programs displayed an above average size of effect in the affective domain, and peer tutoring was identified to have a higher effect than collaborative learning. In addition, when the programs were compared based on school levels, the size of effect was highest at the elementary school level followed by middle school and high school, in that order. When compared based on the criteria of the affective domain, self-efficacy in math, learners' attitude toward math, and learners' interest in math were identified to. Finally, this study presented suggestions for teaching underachieving students in math and conducting follow-up studies based on the analysis results.

• Journal of The Korean Association For Science Education
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• v.38 no.6
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• pp.875-883
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• 2018

This study examined overall effect of STEAM programs on student learning through meta-analysis of journal articles published for the past six years. We examined the areas of effects that the research tested and analyzed overall effect across the research. We first identified academic journal articles that utilized quasi-experimental design in examining STEAM effects on student learning and presented appropriate data for meta-analysis such as effect size. A total of 63 articles were identified to be appropriate for meta-analysis. Using R packages, we first identified outliers and eliminated them in the analysis of mean effect size. Thus, 172 effect sizes from 60 studies were analyzed. The results showed that the mean effect was medium (effect size = 0.52). Analysis showed that moderators of the effect were affective measures, thinking skills, character measures, and career aspirations, which meant the studies that measured these variables had more effect than achievement measures. On the other hand, the school level (elementary, middle, and high school), the absence or presence of student products as program requirements, hours of intervention, and sample size did not moderate the effect. Thus, regardless of these variables STEAM programs produced medium effect in general. Based on these results, further research areas and topics are suggested.

• The Journal of Korean Association of Computer Education
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• v.14 no.3
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• pp.25-36
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• 2011

The purpose of this study was to analyze the effects of educational programming language(EPL) using the meta-analysis method. In order to achieve the purpose of this study, t-test and F-test were performed for the effect size differences between the variables. The results of the study were as follows: First, EPL turned out to be highly effective in improving learning effects. The total mean of effect size was as big as 1.01 and the value of $U_3$ was 84.38%. EPL increased the learning effect by 34.38% compared with the control group. Second, the moderator variables such as subject, publication type, and learner's school age there was no statistically significant differences. By designing the experiment nonequivalent control group pretest-posttest design showed statistically significant effect size compared with single group pretest-posttest design. Third, the mean effect sizes of the dependent variables were as follows: Creativity 1.90, problem solving ability 1.25, logical thinking ability 1.18, learning motivation 0.81, and achievement 0.59. EPL showed positive effect than traditional teaching and learning method comprehensively.

• Journal of Science Education
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• v.32 no.2
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• pp.51-70
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• 2008

The purposes of this study were to analyze the domestic research trend of the STS Education and to evaluate the application effects of STS teaching model by using Meta-analysis. The selected research articles were 180 articles including 104 of the master's theses and 76 of science education journal articles published from 1991 to 2006. For the evaluation of the effects of STS teaching and learning model, 56 articles were selected finally. The mean effect size of the application effects of STS teaching and learning was 0.40. The result indicated that STS teaching had more positive effects than the traditional teaching on enhancing student's attitude for science, academic achievement in science, inquiry ability, attitude for environment and knowledge for environment. Especially, it had shown the most positive effects on improvement in the attitude for environment. Therefore, it might be better to reflect these results for the best effect of STS teaching. To assess STS education on the whole, it is necessary to synthesize the effects of STS teaching and learning model and the results of the study on teachers' and students' understanding about the interrelation of science, technology and society.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.41-47
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• 2020

This study aims to investigate effects of brain-based learning. 27 primary studies were selected through rigorous search process and analyzed through meta-analytic methods. Research findings are as follows. First, the total effect size was .67. Second, the effect of dependent variables was academic achievement, cognitive domain, and affective domain in order. Third, with respect to types of cognitive domain, the effect was self-regulation, creativity, competence, communication, and research ability in order. Fourth, the effect of affective domains was sociality, learning interest, and subject attitude in order. Fifth, regarding development of cognitive ability, the effect size was combined, brain training, learning environments, and right brain activities in order. Sixth, the effect of learning activities was memory improvement and attention enhancement in order.

• The Mathematical Education
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• v.60 no.1
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• pp.41-59
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• 2021

The purpose of this study is to synthesize a comprehensive and general conclusion about the effects of mathematics classes using peer tutoring on the cognitive (mathematics learning achievement) and affective domains. For this purpose, a total of 61 individual studies were meta-analyzed in this study to calculate the effect size, measuring the strength of the relationship between mathematics classes using peer tutoring and either the cognitive or affective domain. As a result of this study, it was confirmed that mathematics classes using peer tutoring generally have a medium effect size in both cognitive and affective domains. Also, it was found that level of school, type of student, learning location, class time, tutor education or prior training are significant variables that affect the impact of mathematics classes using peer tutoring on the cognitive and affective domains. These results suggest specific ideas on how to design and operate peer tutoring in school mathematics classes on the basis of different variables.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.31 no.6
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• pp.258-263
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• 2021

ZnO varistor is a semiconductor device which can serve to protect the circuit from surge voltage because its non-linear I-V characteristics by controlling the microstructure of grain and grain boundaries. In order to obtain desired electrical properties, it is important to control microstructure evolution during the sintering process. In this research, we defined a dataset composed of process conditions of sintering and relative permittivity of sintered body, and collected experimental dataset with DOE. Meta-models can predict permittivity were developed by learning the collected experimental dataset on various machine learning algorithms. By utilizing the meta-model, we can derive optimized sintering conditions that could show the maximum permittivity from the numerical-based HMA (Hybrid Metaheuristic Algorithm) optimization algorithm. It is possible to search the optimal process conditions with minimum number of experiments if meta-model-based optimization is applied to ceramic processing.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.163-176
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• 2024

The aim was to evaluate the effects of simulation based learning in psychiatry on self-efficacy, problem solving ability, and knowledge of nursing students. PubMed, Cochrane Library, Embase, CINAHL, KISS, RISS, and ScienceOn were searched until July 2023. A systematic review and meta-analysis was conducted of 22 studies (20 reports), with a total of 1,414 nursing students. Overall, simulation based learning in psychiatry appeared to have beneficial effects on self-efficacy (ES = 0.65, p < 0.001, I²=71%), problem solving ability (ES = 0.15, p < 0.001, I²=27%), and knowledge (ES = 0.45, p = 0.003, I²=84%). These results demonstrate that, if integrated appropriately, a simulation educational approach can be used as an active learning methodology in psychiatric academic settings.

• Imaging Science in Dentistry
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• v.54 no.3
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• pp.232-239
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• 2024

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures. Materials and Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command. Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913). Conclusion: This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.

• Journal of Korea Multimedia Society
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• v.10 no.6
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• pp.754-759
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• 2007

Methodology of ontology building based on Web resources will not only reduce significantly the ontology construction period, but also enhance the quality of the ontology. Remarkable progress has been achieved in this regard, but they encounter similar difficulties, such as the Web data extraction and knowledge acquisition. This paper researches on the characteristics of ontology construction data, including dynamics, largeness, variation and openness and other features, and the fundamental issue of ontology construction - formalized representation method. Then, the key technologies used in and the difficulties with ontology construction are summarized. A software Model-OntoMaker (Ontology Maker) is designed. The model is innovative in two regards: (1) the improvement of generality: the meta learning machine will dynamically pick appropriate ontology learning methodologies for data of different domains, thus optimizing the results; (2) the merged processing of (semi-) structural and non-structural data. In addition, as known to all wetland researchers, information sharing is vital to wetland exploitation and protection, while wetland ontology construction is the basic task for information sharing. OntoMaker constructs the wetland ontologies, and the model in this work can also be referred to other environmental domains.