• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.887-890
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• 1993

By introducing the notion of constraint-oriented fuzzy inference, we will show that it provides us ways of fuzzy control methods that has abilities of adaptation, learning and self-organization. The basic supporting techniques behind these abilities are“hard”processing by Artificial Intelligence or traditional computational framework and“soft”processing by Neural Network or Genetic Algorithm techniques. The reason that these techniques can be incorporated to fuzzy control systems is that the notion of“constraint”itself has two fundamental properties, that is, the“modularity”property due to its declarativeness and the“logicality”property due to its two-valuedness. From the former property, the modularity property, decomposing and integrating constraints can be done easily and efficiently, which enables us to carry out the above“soft”processing. From the latter property, the logicality property, Qualitative Reasoning and Instance Generalization by Symbolic Reasoning an be carried out, thus enabling the“hard”processing.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.107-111
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• 2005

When a pedagogical agent system aims to provide students with interactive help, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. That is, it must do both assessment and plan recognition. These modeling tasks involve a high level of uncertainty when students are allowed to follow various lines of reasoning and are not required to show all their reasoning explicitly. In this paper, the student model for interactive edutainment applications is proposed. This model is based on Bayesian Networks to expose constructs and parameters of rules and propositions pertaining to game and problem solving activities. This student model could be utilized as the emotion generation model for student and agent as well.

• Journal of Educational Research in Mathematics
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• v.20 no.4
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• pp.493-509
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• 2010

Researchers have suggested that educators have to focus their attention on variability and reasoning about variation as means of developing students' statistical thinking in school mathematics. This paper investigated knowledge for the teaching of variability and reasoning about variation; what are sources of variability, how to cope with variability, what are types of variability, how to recognize variability, and the relationship between statistical problem solving and variability. The results involve: discussion on the sources of variability and how to cope with variability promotes students' awareness of different types of variability and students' motivation in the following steps in the statistical activity; emphasis on reasoning about variation in teaching representation of data accords with objectives of statistics education; reexamination of curriculum for statistics education is needed, which has a content-oriented arrangement.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.

• Journal of Korean Physical Therapy Science
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• v.18 no.1
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• pp.33-49
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• 2011

The process of physical therapy uses a problem-solving approach to enhance a patients's functioning status. The International Classification of Functioning, Disability and Health(ICF) is the common concept for the functioning in the world. Physical therapists require the ability to identify problems, formulate hypothesis, and plan intervention strategies through clinical reasoning. In the clinical process, physical therapists need to use standard and common languages in speech and in documentation. The purpose of this study was to suggest the process of making strategy for efficient intervention, examining and evaluating the functional problem of the person with stroke using ICF tools. For the first step in this process model, therapists could list the information relating to functional problems used by the ICF Core set and then could identify the interaction among the problems using the ICF assessment sheet. For the next step, therapist is needed to make the hypothesis and hypothesis testing, and then set a primary functional goals and therapeutic goals in detail after prioritizing the problems to be managed based on the problem list. Finally, after setting the identified problems as the purpose of intervention through the hypothesis testing, therapist could do some intervention after making a plan to solve these problems, and find out the outcomes using the ICF evaluation display. This report illustrates how to apply the process based on ICF concept into physical therapy practice. Making a decision for the most efficient intervention requires that therapists use the clinical reasoning process based on ICF concept.

• Journal of Korean Home Economics Education Association
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• v.22 no.4
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• pp.109-138
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• 2010

This study examined how practical reasoning process is reflected in the 2007 revised home economics textbook of the 7th grade that is being used in schools since 2010. For a textbook analysis of the components of practical reasoning process, 12 published textbooks were all analyzed, and analysis areas were decided by reconstructing the textbook system into introduction, body contents, learning activities, evaluation. Analysis criteria were extracted from the previous literatures. Practical reasoning process was classified into Valued Ends, Context and Background, Alternatives and Means, on sequence, and Action and Reflection. As a result of analyzing categories and problems that practical reasoning process is reflected in the textbook, firstly, here was a category, which Valued Ends was absent in the practical reasoning process. In the relevant category, general problem solving progress and practical reasoning process could not be differentiated, and activity was developed with 'strategies for solution' as objective. Second, there was a category that the practical reasoning process was reflected in body contents. In this category, knowledge, concept, principle, and theory were insufficiently dealt with. Third, there was a category that reflected components of practical reasoning process but was covered with general problem solving process. Fourth, there was a category that described theme of sub-unit and body contents from the different curriculum perspectives. A curriculum perspective works as the foundation of preparing class contents or consistency of methods when developing lessons. Therefore, confusion might occur when a teacher plans a class with this kind of textbook. Fifth, a category that suggested partial components of practical reasoning process was shown the most and this category is difficult to maximize effects of practical reasoning process.

• Journal of Korean Elementary Science Education
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• v.42 no.3
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• pp.421-433
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• 2023

This study examined the reasoning of gifted elementary science students in a socioscientific issues (SSI) classroom discussion on COVID-19-related trash disposal challenges. This study aimed to understand the characteristics of evidence use and decision-making difficulties in each type of SSI-related reasoning. To this end, the transcripts of 17 gifted students of elementary science discussing SSIs in a classroom were analyzed within the framework of informal reasoning. The analysis framework was categorized into three types according to the primary influence involved in reasoning: rational, emotional, and intuitive. The analysis showed that students exhibited four categories of evidence use in SSI reasoning. First, in the rational reasoning category, students deemed and recorded scientific knowledge, numbers, and statistics as objective evidence. However, students who experienced difficulty in investigating such scientific data were less likely to have factored them in subsequent decisions. Second, in the emotional reasoning category, students' solutions varied considerably depending on the perspective they empathized with and reasoned from. Differences in their views led to conflicting perspectives on SSIs and consequent disagreement. Third, in the intuitive reasoning category, students disagreed with the opinions of their peers but did not explain their positions precisely. Intuitive reasoning also created challenges as students avoided problem-solving in the discussion and did not critically examine their opinions. Fourth, a mixed category of reasoning emerged: intuition combined with rationality or emotion. When combined with emotion, intuitive reasoning was characterized by deep empathy arising from personal experience, and when combined with rationality, the result was only an impulsive reaction. These findings indicate that research on student understanding and faculty knowledge of SSIs discussed in classrooms should consider the difficulties in informal reasoning and decision-making.

• School Mathematics
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• v.11 no.3
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• pp.351-368
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• 2009

This study presents data from a year-long teaching experiment which illustrate how two seventh graders modified their multiplicative thinking and interacted with their representing symbols in the context of combinatorial problem situations. Damon was at the process of construction of recursively multiplicative thinking by modifying his multiplicative reasoning, but Carol appeared to remain at the stage of a binary multiplicative scheme. The two students' struggles with their representing symbols or represented symbols by the teacher show that even well-organized symbolic systems from teachers' perspective do not necessarily help students advance their mathematical capacity.

• Journal of Elementary Mathematics Education in Korea
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• v.15 no.3
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• pp.641-654
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• 2011

In order to grow students' rational and creative problem-solving ability which is one of the primary goals in mathematics education. students' proper understanding of mathematical concepts, principles, and rules must be backed up as its foundational basis. For the relevant teaching strategies. National Mathematics Curriculum advises that students should be allowed to discover and justify the concepts, principles, and rules by themselves not only through the concrete hands-on activities but also through inquiry-based activities based on the learning topics experienced from the diverse phenomena in their surroundings. Hereby, this paper, firstly, looks into both the meaning and the inductive reasoning process of mathematical principles and rules, secondly, suggest "learning through discovery teaching method" for the proper teaching of the mathematical principles and rules recommended by the National Curriculum, and, thirdly, examines the possible discovery-led teaching strategies using inductive methods with the related matters to be attended to.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.178-184
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• 2003

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the similarity function or optimization of case features and their weights. However, according to some of prior researches, finding the optimal k parameter for k-nearest neighbor (k-NN) is also crucial to improve the performance of CBR system. Nonetheless, there have been few attempts which have tried to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the new model to the real-world case provided by an online shopping mall in Korea. Experimental results show that a GA-optimized k-NN approach outperforms other AI techniques for purchasing behavior forecasting.