• Journal of Educational Research in Mathematics
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• v.27 no.4
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• pp.791-810
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• 2017

This study explored the possibility of using visual models in teaching proportional reasoning based on the review of previous studies. Many studies on proportional reasoning emphasize that students tend to simply apply formal procedures without understanding the meaning behind them and that using visual models may be an alternative to help students develop informal strategies and proportional reasoning. Given these, we re-constructed and implemented the unit of a textbook to teach sixth graders proportional reasoning with ratio table, double number line, and double tape diagram. The results of this study showed that such visual models helped students understand the meaning of proportion, explore the properties of proportion, and solve proportional problems. However, several difficulties that students experienced in using the visual models led us to suggest cautionary notes when to teach proportional reasoning with visual models. As such, this study is expected to provide empirical information for textbook developers and teachers who teach proportional reasoning with visual models.

• 한국정보교육학회:학술대회논문집
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• 2010.08a
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• pp.95-99
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• 2010

In this study, using the program in elementary school, scratch, based on user-centered design model, a high-level (High Level) step by applying prototyping techniques for application development, training and present a model applied to investigate reports that validate the effectiveness. The results of this study, problem solving and logical thinking ability in elementary school for the education of the new approach to application development is expected to be.

• Journal of Gifted/Talented Education
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• v.9 no.2
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• pp.73-101
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• 1999

The purpose of this study was to suggest an advanced system for educating scientifically gifted children in the Center for Science Talented Education at Kyungpook National University. Several suggestions based on analysis of current identifying-process and instructional materials for scientifically gifted children were provided for advancing the educational system of the center. First, this study suggested a three-step procedure to identify procedure emphasized students reasoning skills as one of important characteristics of the gifted child. Second, this study provided an instructional model for developing hypothesis testing skills in scientifically gifted children. The model was originally based on Lawson's scientific reasoning processes and learning cycle mode. Third, this study also suggested an effective administration system of the Center for Science Talented Education. Further, this study suggested effective ways on research works for advancing the center, educating instructors, the cyber center for remote education, and international co-works for developing the gifted children's potential abilities.

• Knowledge Management Research
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• v.1 no.1
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• pp.81-90
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• 2000

This study proposes the knowledge discovery process for the effective mining of knowledge on the web. The proposed knowledge discovery process uses the Prior knowledge base and the Prior knowledge management system to reflect tacit knowledge in addition to explicit knowledge. The prior knowledge management system constructs the prior knowledge base using a fuzzy cognitive map, and defines information to be extracted from the web. In addition, it transforms the extracted information into the form being handled in mining process. Experiments using case-based reasoning and neural network" are performed to verify the usefulness of the proposed model. The experimental results are encouraging and prove the usefulness of the proposed model.

• Journal of Korean Home Economics Education Association
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• v.13 no.3
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• pp.1-11
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• 2001

The purpose in this study was to investigate stages of concern of home economics teachers about the practical reasoning instruction(PRI) innovation configuration. levels of use. using Concerns Based Adoption Model(CBAM) The design of the study was descriptive. The Questionnaires of stages of concern. levels of use. and innovation configuration developed by Hall in 1987 were used. Data from HE teachers were collected through mailing. focus interview. and phone calls. Mean. percents. an frequencies were used to describe stages of concern, level of use. and innovation configuration. The results of the study were as follows: 1. The highest number of HE teacher respondents was stage 1. Information in March and july. 2 In terms of mean of PRI configuration. percentile. the HE teachers had implemented over 63% of the PRI elements and the cooperative learning elements in both March and July. However. of evaluation elements, 33% in March and 47% in July had been conducted. 3. The highest number of HE teacher respondents was level 3. Mechanical level in March. and July.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.285-288
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• 2006

Most previous studies on improving the effectiveness of CBR have focused on the similarity function aspect or optimization of case features and their weights. However, according to some of the prior research, finding the optimal k parameter for the k-nearest neighbor (k-NN) is also crucial for improving the performance of the CBR system. Nonetheless, there have been few attempts 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 that combine, as well as the weight of each feature. The new model is applied to the real-world case of a major telecommunication company in Korea in order to build the prediction model for the customer profitability level. Experimental results show that our GA-optimized CBR approach outperforms other AI techniques for this mulriclass classification problem.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.51-60
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• 2006

The goal of qualitative analysis is to capture and formalize qualitative and intuitive knowledge about physical world. Qualitative reasoning has been successfully applied to electric and mechanical mechanism domains, in which most of reasoning has focused on simulation. This paper introduces a qualitative comparative analysis technique which predicts how a change in a given situation propagates. We developed a comparative analysis technique which extends previous research by including a reasoning technique about the relative rate of the change of a parameter. Previous research focuses only on the relative change of a parameter. Causal model for the given situation is generated from qualitative domain model. The propagation by the change in causal relations are traced by applying our comparative analysis. By providing explanation as well as prediction for the given change, our technique is expected to be used in design, diagnosis, intelligent tutoring system, environmental evaluation.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.11-16
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• 2019

People are subject to time sanctions in a busy modern society. Therefore, people find it difficult to eat simple junk food and even exercise, which is bad for their health. As a result, the incidence of chronic diseases is increasing. Also, the importance of making accurate and appropriate inferences to individual characteristics is growing due to unnecessary information overload phenomenon. In this paper, we propose a knowledge reasoning model using association rules and cluster analysis of multi-contexts. The proposed method provides a personalized healthcare to users by generating association rules based on the clusters based on multi-context information. This can reduce the incidence of each disease by inferring the risk for each disease. In addition, the model proposed by the performance assessment shows that the F-measure value is 0.027 higher than the comparison model, and is highly regarded than the comparison model.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Korean Home Economics Education Association
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• v.21 no.2
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• pp.187-202
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• 2009

The purpose of this study was to develop a practical reasoning model for the 'parenthood' part of the 'marriage and child rearing' unit of high school first-grade technology & home economics based on the instruction objectives selected from 11 different kinds of textbooks, and to examine the effects of the model. Learning objectives and contents were selected, and a practical reasoning teaching model of six sessions was developed and implemented in class. The subjects in this study were students in five first-year classes in a high school located in the city of Icheon, Gyeonggi province. The effectiveness of the model was analyzed by conducting a survey on the students before and after its application. Assessments were also conducted on the lessons applied. Students who received instruction according to the practical reasoning model underwent a significant change, as they displayed higher scores in understanding the meaning of parenthood, preparation for parenthood, and the role of parenting. As a result of applying the model, it was found that the classes proved to be helpful to the students.