• Journal of The Korean Association For Science Education
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• v.32 no.8
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• pp.1295-1317
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• 2012

There has been disagreement on the importance of definitions in science education. Yager (1983) believes that one crisis in science education was due to the considerable emphasis upon the learning of definitions. Hobson (2004) disagrees with physics textbooks that do not provide general definition on energy. Some textbooks explain that "there is no completely satisfactory definition of energy" or they can only "struggle to define it." In general, imprecise definitions in textbooks (Bauman, 1992) and inaccuracies in definition provided by teachers (Galili & Lehavi, 2006) may cause alternative conceptions. Besides, there are at least four challenges in defining physical concepts: precision, circularity, context and completeness in knowledge. These definitional problems that have been discussed in The Feynman Lectures, may impede the learning of physical concepts. A meta-study approach is employed to examine about five hundreds journal papers that may discuss definitions in physics, problems in defining physical concepts and how they may result in alternative conceptions. These journal papers are mainly selected from journals such as American Journal of Physics, International Journal of Science Education, Journal of Research in Science Teaching, Physics Education, The Physics Teachers, and so on. There are also comparisons of definitions with definitions from textbooks, Dictionaries of Physics, and English Dictionaries. To understand the nature of alternative conception, Lee et al. (2010) have suggested a theoretical framework to describe the learning issues by synthesizing cognitive psychology and science education approaches. Taking it a step further, this study incorporates the challenges in semantics and epistemology, proposes that there are at least four variants of alternative conceptions. We may coin the term, 'alternative definitions', to refer to the commonly available definitions, which have these four problems in defining physics concepts. Based on this study, alternative definitions may result in at least four variants of alternative conceptions. Note that these four definitional problems or challenges in definitions cannot be easily resolved. Educators should be cognizant of the four variants of alternative conceptions which can arise from alternative definitions. The concepts of alternative definitions can be useful and possibly generalized to science education and beyond.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.335-342
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• 2016

When dynamics changes occurred in an existing Bayesian Network (BN), the related parameters embedding on the BN have to be updated to new parameters adapting to changed patterns. In this case, these parameters have to be updated with the consideration of the causalities in the BN. This research suggests a framework for updating parameters dynamically using Expectation Maximization (EM) algorithm and Harmony Search (HS) algorithm among several Meta-Heuristics techniques. While EM is an effective algorithm for estimating hidden parameters, it has a limitation that the generated solution converges a local optimum in usual. In order to overcome the limitation, this paper applies HS for tracking the global optimum values of Maximum Likelihood Estimators (MLE) of parameters. The proposed method suggests a learning and propagation framework of BN with dynamic changes for overcoming disadvantages of EM algorithm and converging a global optimum value of MLE of parameters.

• Journal of Educational Research in Mathematics
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• v.26 no.3
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• pp.543-563
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• 2016

This study was designed as a meta-analysis to investigate the research trends in mathematics learning disabilities(MLD) area. The results of this study were as follows: The 201 researches targeted for the analysis can be categorized 4: characteristic of students with MLD, screening students with MLD, interventional teaching for students with MLD, and et cetera. Also, the outcomes of researches regarding intervention in MLD determined to have a large effect resulted in a total average of 0.958. Especially, as a result of analysing the effect size in accordance with teaching method variables in group-case designed researches, the effect was largest when direct instruction and strategy instruction was given. The effect was largest when the frequency of intervention was over 16 and under 20. The results in this study be summed up as follows. MLD can be served as a foundation in setting a direction for further research to improve in Korea.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.331-346
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• 2021

The purpose of this study was to comprehensively examine the effect of capstone design education on learning outcomes and propose directions for effective design and implementation of capstone design classes. For achieving this, a 21 studies meeting the standards among the academic journals and thesis published in Korea by September 2020 were selected, and based on 83 effect sizes, the meta analyses were carried out. The results of this study were as follows: First, the total effect size of capstone design education was 0.96, which is a large effect size. Second, the effect size was large in order of affective, cognitive, and social areas. Third, the effect size of vocational basic ability showed a large effect size while creativity showed a medium-sized one. Fourth, the effect size showed highest for design subject, the grade in the third or fourth, there was help from industrial corporation, theory and practice. Based on these results, this study proposed instructional design implications in order to increase the learning effects of capstone design in Korea.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.23-43
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• 2022

In order to apply AI (Artificial Intelligence) in various industries, interest in algorithm selection is increasing. Algorithm selection is largely determined by the experience of a data scientist. However, in the case of an inexperienced data scientist, an algorithm is selected through meta-learning based on dataset characteristics. However, since the selection process is a black box, it was not possible to know on what basis the existing algorithm recommendation was derived. Accordingly, this study uses k-means cluster analysis to classify types according to data set characteristics, and to explore suitable classification algorithms and methods for resolving class imbalance. As a result of this study, four types were derived, and an appropriate class imbalance resolution method and classification algorithm were recommended according to the data set type.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of Periodontal and Implant Science
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• v.54 no.1
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• pp.3-12
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• 2024

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

• Journal of The Korean Association of Information Education
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• v.12 no.4
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• pp.375-384
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• 2008

Computer programming has educational effect on improving high-level thinking abilities. However, students initially have to spend too much effort in learning the basic grammar and the usage model of programming languages, which negatively affects their eagerness in learning. To remedy this problem, we propose to apply the Scratch to a Game Developing Programming Class; Scratch is an easy-to-learn and intuitive Educational Programming Language (EPL) that helps improving the Meta-cognition and Self-efficacy of middle school students. Also we used the Demonstration-Practice instruction model with self-questioning method for activating the Meta-cognition. In summary, a game developing programming class using Scratch was shown to significantly improve the Meta-cognition of middle school students. However it was shown to insignificantly improve the Self-efficacy of girl students group.

• The Journal of Korean Association of Computer Education
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• v.18 no.1
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• pp.21-33
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• 2015

The purpose of this study has been found to be effective using web-based instruction than traditional teaching-learning method on academic achievement applying the meta-analysis method. The results of this study were as follows: First, The 85% subject of analysis of web-based instruction selected in this study turned out to be clear effective than traditional teaching-learning method in academic achievement of students. Second, Web-based instruction is more effective for academic achievement of elementary school students and university students than for middle school students and high school students relatively. Third, Web-based instruction is a most effective method in social subject and physical education but less effective in language subject. The overall results of this study concluded more powerful and big decisions which have integrated each different effects on academic achievement of studies web-based instruction method applying meta-analysis. Through this study, make better results were obtained and suggested the base line data and direction for follow up studies.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.1989-2001
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• 2024

Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on simulators or thermal-hydraulic codes to mimic the plants. However, it remains uncertain whether an AI model trained using a simulator can properly work in an actual NPP. To address this issue, this study suggests the use of metadata, which can give information about parameter trends. Referred to here as robust AI, this concept started with the idea that although the absolute value of a plant parameter differs between a simulator and actual NPP, the parameter trend is identical under the same scenario. Based on the proposed robust AI, this study designs an event diagnosis algorithm to classify abnormal and emergency scenarios in NPPs using prototypical learning. The algorithm was trained using a simulator referencing a Westinghouse 990 MWe reactor and then tested in different environments in Advanced Power Reactor 1400 MWe simulators. The algorithm demonstrated robustness with 100 % diagnostic accuracy (117 out of 117 scenarios). This indicates the potential of the robust AI-based algorithm to be used in actual plants.