• Journal of Elementary Mathematics Education in Korea
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• v.15 no.2
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• pp.437-461
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• 2011

The purpose of this study was to examine the metacognition of mathematically gifted students in the problem-solving process of the given task in a bid to give some significant suggestions on the improvement of their problem-solving skills. The given task was to count the number of regular squares at the n${\times}$n geoboard. The subjects in this study were three mathematically gifted elementary students who were respectively selected from three leading gifted education institutions in our country: a community gifted class, a gifted education institution attached to the Office of Education and a university-affiliated science gifted education institution. The students who were selected from the first, second and third institutions were hereinafter called student C, student B and student A respectively. While they received three-hour instruction, a participant observation was made by this researcher, and the instruction was videotaped. The participant observation record, videotape and their worksheets were analyzed, and they were interviewed after the instruction to make a qualitative case study. The findings of the study were as follows: First, the students made use of different generalization strategies when they solved the given problem. Second, there were specific metacognitive elements in each stage of their problem-solving process. Third, there was a mutually influential interaction among every area of metacognition in the problem-solving process. Fourth, which metacognitive components impacted on their success or failure of problem solving was ascertained.

• Journal of the Korean earth science society
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• v.26 no.6
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• pp.501-510
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• 2005

An analysis was done on the “inquiry sections” of Earth Science chapters of 10th grade science textbooks. The Inquiry sections were classified into different types and the frequencies of basic process skills, integrated process skills, and inquiry activities were measured in section to find out whether they sufficiently satisfy the requirements based on the 7th National Curriculum. The number of selected science textbooks that have been used in high school for this study were eleven. The number of inquiry tasks were on an average of 24.0. The types of inquiry sections and the elements of basic and integrated process skills were different in every textbooks. The number of inquiry activities were also different and analyzed more than those presented. They were not integrated activities but presented as scientific process skills. The basic process skills and integrated process skills presented in textbooks were $16\%\;and\;77.2\%$, respectively. However, the distribution of two kinds of process skills were analyzed to be $45.6\%\;and\;55.4\%$, respectively. In the process skills, the frequencies of inferring $(49.5\%)$ and data interpretation (68.7%) were the highest; however, the other process skills including recognizing problem, formulating hypothesis and generalization were not even presented in any of the text books. Due to the lack of the definitions of Science process skills and inquiry activities in the 7th National Curriculum, each text book defined these terms differently. It suggests that the meaning of inquiry, science process skills, and inquiry activities should be operationally defined in the national curriculum and the criteria for construction of inquiry activities are required.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.408-416
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• 2022

The new millennium is characterized by an unprecedented breakthrough in knowledge and information and communication technologies, and the challenges of the XXI century require modernized paradigms of interaction in all spheres of life. Education continues to play a key role in national and global growth. The key role of education and its leadership in developing creative potential, as the main paradigm of the countries' stability, have significantly influenced educational centers. The developers of educational programs use information technologies as an incentive to develop creative potential of educational process. Professional training of the educational candidate is enhanced by the use of information technologies, so the educational applicants should develop technological skills to be productive members of society. Using the latest achievements in the field of information technologies for the organization of the educational process helps to form the operational style of education applicants' thinking, which provides the ability to acquire skills of processing information, that is presented in the text, graphic, tabular form, and increase the level of general and informational culture necessary for better orientation in the modern information space. The purpose of the research is to determine the effectiveness of information technologies as an incentive to develop creative potential of educational process on the basis of the survey, to establish advantages and ability to provide high-quality education in the context of using information technologies. Methods of research: comparative analysis; systematization; generalization, survey. Results. Based on the survey conducted among students and teachers, it has been found out that the teachers use the following information technologies for the development of creative potential of the educational process: to provide video and audio communication process (100%), Moodle (95,6%), Duolingo (89,7%), LinguaLeo (89%), Google Forms (88%) and Adobe Captivate Prime (80,6%). It is determined that modular digital learning environments (97,9%), interactive exercises tools (96,3%), ICT for video and audio communication (96%) and interactive exercises tools (95,1%) are most conducive to the development of creative potential of the educational process. As a result of the research, it was revealed that implementation of information technologies for the development of creative potential of educational process in educational institutions is a complex process due to a large number of variables, which should be taken into account both on the educational course and on the individual level. It has been determined that the using the model of implementation information technologies for the development of creative potential in educational process, which is stimulated due to this model, benefits both students and teachers by establishing a reliable bilateral connection between teacher and education applicant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1036-1057
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• 2013

Wireless body area network (WBAN) is a promising candidate for future health monitoring system. Nevertheless, the path to mature solutions is still facing a lot of challenges that need to be overcome. Energy efficient scheduling is one of these challenges given the scarcity of available energy of biosensors and the lack of portability. Therefore, researchers from academia, industry and health sectors are working together to realize practical solutions for these challenges. The main difficulty in WBAN is the uncertainty in the state of the monitored system. Intelligent learning approaches such as a Markov Decision Process (MDP) were proposed to tackle this issue. A Markov Decision Process (MDP) is a form of Markov Chain in which the transition matrix depends on the action taken by the decision maker (agent) at each time step. The agent receives a reward, which depends on the action and the state. The goal is to find a function, called a policy, which specifies which action to take in each state, so as to maximize some utility functions (e.g., the mean or expected discounted sum) of the sequence of rewards. A partially Observable Markov Decision Processes (POMDP) is a generalization of Markov decision processes that allows for the incomplete information regarding the state of the system. In this case, the state is not visible to the agent. This has many applications in operations research and artificial intelligence. Due to incomplete knowledge of the system, this uncertainty makes formulating and solving POMDP models mathematically complex and computationally expensive. Limited progress has been made in terms of applying POMPD to real applications. In this paper, we surveyed the existing methods and algorithms for solving POMDP in the general domain and in particular in Wireless body area network (WBAN). In addition, the papers discussed recent real implementation of POMDP on practical problems of WBAN. We believe that this work will provide valuable insights for the newcomers who would like to pursue related research in the domain of WBAN.

• Journal of Elementary Mathematics Education in Korea
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• v.20 no.1
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• pp.131-148
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• 2016

Mathematical formal justification may be seen as a bridge towards the proof. By requiring the mathematically gifted students to prove the generalized patterned task rather than the implementation of deductive justification, may present challenges for the students. So the research questions are as follow: (1) What are the difficulties the mathematically gifted elementary students may encounter when formal justification were to be shifted into a generalized form from the given patterned challenges? (2) How should the teacher guide the mathematically gifted elementary students' process of transition to formal justification? The conclusions are as follow: (1) In order to implement a formal justification, the recognition of and attitude to justifying took an imperative role. (2) The students will be able to recall previously learned deductive experiment and the procedural steps of that experiment, if the mathematically gifted students possess adequate amount of attitude previously mentioned as the 'mathematical attitude to justify'. In addition, we developed the process of questioning to guide the elementary gifted students to formal justification.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.319-332
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• 2013

Previous research on learning and memory has focused on how they are constructed through past experiences. Recent studies, however, have shed light on that such cognitive processes are in service of higher goals of maximizing future rewards. This review paper aims to introduce and discuss a related line of research. First, this paper introduces researches that show goal-directed model-based reinforcement learning, in which agents choose a behavior that does not necessarily bring immediate rewards but will allow future rewards, based on generalization and analogical extrapolation. It also reviews studies on neural substrates of goal-directed learning, and discusses that cognitive process implicated in striatal dopaminergic signals can also influence memory. Especially, memory is not a merely passive process of storing and retrieving past experiences homogeneously, but rather results of a decision-making process to serve higher goals. The body of research suggests that information on future rewards can have influence on current cognitive processing in a retrospective manner.

• Design & Manufacturing
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• v.11 no.1
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• pp.1-6
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• 2017

In these days, due to generalization of using smart mobile phone and wearable device such as smart watch, demand of Cover-glass and touch screen panel for protecting display increases. With increasing the demand of Cover-glass, slimming technique is promising for weight lightening, zero bezel. Cover-glass produced by this technique is required to decreasing thickness with increase strength. In the Cover-glass manufacturing process, mechanical processing and chemical processing has improve in the strength. Generally, Diamond electrodeposition wheel is used in mechanical process. Reinforced glass with the characteristics of the brittle and high hardness was manufactured by using a diamond electrodeposition wheel. At this time, Because of surface of the tool present non-uniform distribution of diamond particle, it has generate Loading of wheel and it has been decrease life of grinding tool, efficiency of grinding, quality and shape accuracy of workpiece. Thus Research is needed to controling particle distribution of diamond electrodeposition wheel uniformly. And it is necessary to study micro hole machining such as proximity senser hole, speaker hole positioned Cover-glass. Reinforced glass with the characteristics of the brittle and high hardness is difficult to machining. Processing of reinforced glass have generated wear of tool, micro cracks. Also, it is decreasing shape accuracy. In this paper, We conducted a study on how to control particle distribution uniformly about the diamond tool manufactured using elecetodeposition processing. It analyzed the factors that affect the arrangement of the particles in the electrodeposition process by design of experiment. And There is produced the grinding tool, which derives an optimum deposition conditions, for processing Cover-glass edge and the machinability was evaluated.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.132-137
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• 2007

Experimental software data capturing the essence of software projects (expressed e.g., in terms of their complexity and development time) have been a subject of intensive modeling. In this study, we introduce a new category of Hybrid Fuzzy Neural Networks (gHFNN) and discuss their comprehensive design methodology. The gHFNN architecture results from highly synergistic linkages between Fuzzy Neural Networks (FNN) and Polynomial Neural Networks (PNN). We develop a rule-based model consisting of a number of "if-then" statements whose antecedents are formed in the input space and linked with the consequents (conclusion pats) formed in the output space. In this framework, FNNs contribute to the formation of the premise part of the overall network structure of the gHFNN. The consequences of the rules are designed with the aid of genetically endowed PNNs. The experiments reported in this study deal with well-known software data such as the NASA dataset. In comparison with the previously discussed approaches, the proposed self-organizing networks are more accurate and yield significant generalization abilities.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.