• East Asian mathematical journal
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• v.33 no.4
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• pp.353-380
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• 2017

The purpose of this study is to analyze the multiplication units in the elementary school mathematics. In the 2015 revised curriculum, students learn multiplication in $2^{nd}$ grade. The multiplication units is divided into two: multiplication and multiplication facts. In these two units, we mainly analyze situations involving multiplication, models for teaching multiplication, and multiplication strategies for teaching multiplication facts in relation to Subject Matter Knowledge. We called these contents Multiplication Matter Knowledge. We examined the precedent study with regard to multiplication at the elementary mathematics. As results, we prepared an analysis framework for this study. This study was conducted according to qualitative research methods, expecially 'qualitative contents analysis'. The contents here refer to Multiplication Matter Knowledge that can be found in the elementary mathematics textbooks and working books etc. As results of analysis, We can confirm that various multiplication situations and multiplication models are presented in the textbooks. And it has been examined that various multiplication properties are presented in the textbook according to the multiplication strategy levels. We insist elementary school teachers should be aware of these Multiplication Matter Knowledge. This study aims to provide elementary school teachers with basic data in these contexts.

• Management Science and Financial Engineering
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• v.15 no.2
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• pp.101-118
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• 2009

In quantitative contexts, data mining is widely applied to the prediction of stock prices from financial time-series. However, few studies have examined the potential of data mining for shedding light on the qualitative problem-solving knowledge of experts who make stock price predictions. This paper presents a GA-based data mining approach to characterizing the qualitative knowledge of such experts, based on their observed predictions. This study is the first of its kind in the GA literature. The results indicate that this approach generates rules with higher accuracy and greater coverage than inductive learning methods or neural networks. They also indicate considerable agreement between the GA method and expert problem-solving approaches. Therefore, the proposed method offers a suitable tool for eliciting and representing expert decision rules, and thus constitutes an effective means of predicting the stock price index.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.229-235
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• 2022

This study aims to substantiate an interdisciplinary methodology for automated analysis and qualitative assessment of legislation and court decisions. The development of this kind of methodology will make it possible to fill a number of methodological gaps in various research areas, including law effectiveness assessment and legal monitoring. We have defined a methodology based on the interdisciplinary principles and tools. In general, it should be noted that even at the level of qualitative assessment made with the use of the methodology described above, the accumulation of knowledge about the relationship between legal objectives, indicators and computer methods of their identification can reduce the role of expert knowledge and subjective factor in the process of assessment, planning, forecasting and control over the state of legislation and law enforcement. Automation of intellectual processes becomes inevitable in a digital society, but, releasing experts from routine work, simultaneously reorients it to development of interdisciplinary methods and control over their application.

• Korean Management Science Review
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• v.12 no.3
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• pp.135-161
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• 1995

This paper is concerned with a conceptual design of a knowledge-based modeler for network analysis. The "knowledge-based modeler" approach is suggested as a method for incorporating the user's qualitative knowledge and subjective decison in the course of the mathematical modeling and the subsequent solution procedure. The submodules of the proposed modeler such as database, model/algorithm base and functional knowledge bases are identified and the flows of information between the submodules are sequentially defined. A prototype system is implemented for experimental purpose by using the application software GURU.ware GURU.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.102-106
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• 1997

Grinding has difficulty in satisfying the qualitative knowledge based on the skilled expert as well as quantitative data for all user. Design of grinding database is based on the frame-based model for utilizing the empirical and qualitative knowledge. Inthis paper, basic strategy to develop the grinding database by frame-based model, which is strongly dependent upon experience and intuition, frame-base model, which is strongly dependent upon experience and intuition, is described. Design of grinding database is based on the frame-based model for utilizing the ambiguous knowledge and inference is accomplised by the object-oriented paradigm system.

• Korean Journal of Human Ecology
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• v.19 no.6
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• pp.945-965
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• 2010

The present study examined the cultural competence of university students on migrants and multicultural society in Korea. A total of 11 university students living in Seoul, Gyeonggido and Chungcheondo, were selected for the interview. The participants provided their cultural cognition, cultural knowledge and cultural skill on a variety of issues related to migrants and multicultural society in Korea. Interview results indicated that participants perceived negative and positive affects to Korean society and prejudice about immigrants, thaught economic support as a emergent policy, and didn't experience multicultural society. They wanted to take a trip and learning foreign language. On the cultural skill participants had progressive perspectives about immigrants' right, discrimination and evaluation and had a multiculturism based on assimilation. Implications of the study and directions for future research were discussed. It is needed to educations and programs for development of university students' cultural competence.

• Journal of Korean Port Research
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• v.6 no.2
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• pp.3-24
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• 1992

In the fuzzy control of shop the qualitative knowledge and information that the ship's operators have acquired through their experience can be logically described by the Linguistic control Rule (LCR). The algorithm of the control is made of the LCR and the control of the shop is performed by processing this algorithm implementing a computer. The problem in the fuzzy control is that it is very difficult to describe qualitative human knowledge in the LCR correctly. To tackle this difficulty a Fuzzy Neural Network (FNN) was introduced in this paper. The characteristics of the multi-layer FNN control system applied to the ship's steering system is investigated through the computer simulation, and the results were compared with those of the ordinary fuzzy control system of a ship. The results showed that the FNN method is a very effective to translate human knowledge into the LCR.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.4
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• pp.56-61
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• 2000

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skilful engineers. Grinding operations include a large number of functional parameters, since there are several ways of coping with grinding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workship, the other is the quantitative method which utilizes the experimental data obtained by a sensor. But, they are all difficult to accomplish from the grinding trouble-shooting system. The reason is that grinding troubles are now easily controlled in the quantitative method, and therefore, trouble-shooting has mainly relied on the knowledge of skilful engineers. Thus, there is an important issue of how a grinding trouble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper, basic strategy to develop the grinding database of rule-based model, which is strongly depended upon experience and intuition , is described.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.492-497
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• 1999

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skillful engineers. Grinding operations include a large number of functional parameters, since there are several ways of coping with grinding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workshop, the other is the quantitative method which utilizes the experimental data obtained by sensor. But, they are all difficult to accomplish from the grinding trouble-shooting system. The reason is that grinding troubles are not easily controlled in the quantitative method, and therefore, trouble-shooting has mainly relied on the knowledge of skilful engineers. Thus, there is an important issue of how a grinding trouble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper, basic strategy to develop the grinding database of rule-based rule, which is strongly depended upon experience and intuition, is described.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.3
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• pp.33-50
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• 2020

Knowledge recipes are packages of knowledge which arise from the process of combining the knowledge assets in the organization in distinctive ways. This involves converting them into useful outputs which are the ideal core competitive advantage enablers for companies. The major objective of this study was to propose a knowledge recipe for financial-sector state corporations in Kenya. The study adopted a convergent parallel mixed methods research design. Quantitative and qualitative data were collected using questionnaires and key informant interviews. The target population of the study was 1574 respondents drawn from all financial state corporations. A multistage sampling technique was used for the study. The first phase involved purposive sampling of the organizations to be studied whereby the four state corporations namely: Capital Markets Authority, Competition Authority of Kenya, Kenya Investment Authority, and Kenya Revenue Authority were identified. The second phase entailed stratified sampling of the respondents in three strata namely senior management team, knowledge management team, and general staff. The authors used a census of all senior management team and knowledge management staff while a simple random sampling technique was used for the general staff. By use of the Krejcie and Morgan table, the actual sample size was 358 respondents from all the four organizations. Data were collected using questionnaires and interview schedules. The qualitative data were analyzed using content analysis while the quantitative data were analyzed by the use of Ms. Excel and VOSviewer and presented using pie charts, bar graphs, and tables. The response rate for this study was 257 (72%). The study revealed that while most employees in the financial sector organizations understand their knowledge needs, knowledge types, knowledge uses and knowledge gaps, they do not have a universal knowledge recipe to facilitate effective knowledge management in their organizations. Consequently, the authors propose a universal knowledge recipe for the state corporations in the financial sector in Kenya. The ingredients of the recipe are legal-knowledge (18%), financial knowledge (15%), administrative knowledge (11%), best practice (10%), lessons learnt (8%), human resource knowledge (8%), research and statistics knowledge (7%), product knowledge (6%), policy and procedure knowledge (5%), ICT knowledge (4%), investor knowledge (3%), markets knowledge (2%), general knowledge (2%) and regulatory framework knowledge (1%).