• Korean Journal of Social Welfare
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• v.52
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• pp.171-200
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• 2003

Social workers' behaviors in their organizations are governed not only by explicit rules but by implicit rules. This study aimed to measure the extent that the implicit rules exist in social work agencies, and to assess the impact of the implicit rules on the degree of social workers' devotion, burnout, and satisfaction in their own work places. This study also endeavored to search any sort of sanctions that agencies apply against workers who refused to follow implicit rules in their organizations, along with any harms and/or benefits which organizations may experience according to the extent that the implicit rules exist. The results showed that the implicit rules do not exist so much in social work agencies in Korea. Not so many sanctions in the organizations were not found against workers who violated the implicit rules. The amount of implicit rules, however, affected the degree of damages that organizations sustained. The more implicit rules exist in the organizations, the higher was the level of social workers' devotion, burnout, and dissatisfaction in the work places. The impact of implicit rules was powerful in a few areas of work. That is, social workers were required to do whatever things at hand rather than carrying out professional tasks only. Social workers were also asked to stay way beyond the closing hour. The explicit rules were dominant in the area concerned with social work ethics. Some strategies to substantiate social work practice were suggested on the basis of careful examination of the powerful implicit rules.

• Korean Journal of Social Welfare
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• v.46
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• pp.236-262
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• 2001

This discovery-oriented study explored 31 social workers' perceptions of discrepancies between explicit and implicit rules in their work places that are supposed to affect the quality of social work services, and identified eight categories of dilemmas: (a) confused accountability or purpose, (b) ambiguous principle, (c) improper authority, (d) confused role of social workers, (e) conflict between ideal and reality, (f) confused work ethics, (g) confused boundary of workers' rights, and (h) binds. These eight categories revealed the real philosophy and purposes of social work agencies, work ethics and values prevalent among social work agencies, agencies' orientation toward clients, and the conditions of social support from the society in large. Instead of searching for discrete variables as separately responsible for inefficient social work services, this approach probed malfunctioning implicit rules in a holistic context to see if inefficient or ineffective provision of social work services is a logical response to a much larger and deeper nexus. Insight into discrepant rules does not solely ensure the improvement of social work practice in the field, particularly if their identification is simply used as another opportunity to blame and avoid self-responsibility. However, such discrepancies between implicit and explicit rules are real enough to the staff workers and agency administrators that they may want to begin the dialogue of contradictory rules as a way of sanctioning discussion of previously forbidden topics. This study provided the ground-work for the dialogue.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.247-250
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• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• Journal of Korean language education
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• v.25 no.1
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• pp.115-144
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• 2014

The purpose of this paper is to compare the two different instruction methods for Korean learners of academic purposes in learning request expression. Participants were divided into two groups, explicit and implicit group. Both groups viewed several scenes from the drama that involved native speakers interacting in different situations. The instructional treatment for the explicit group included metapragmatic information while the treatment for the implicit group did not. On the other hand, the treatment for the implicit group followed implicit techniques, which were repetition of the video presentation and a script reading activity. This study was made up of a pre-test, a post-test, and a delayed-test. The pre-test was conducted prior to the instructional treatment. The post-test was administered a day after the last instruction and the delayed-test was conducted five weeks after the treatments. Two types of tests, speaking and writing, were used in this study to examine subjects' knowledge of Korean request. The result of this research reveals that implicit treatment was more effective than explicit treatment in Korean learners' request acquisition. This results might have been due to the operationalization of the implicit condition in this study. Implicit instruction may help language learners make rules by themselves through tasks.

• The Journal of Information Technology
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• v.9 no.3
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• pp.27-32
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• 2006

If is interested and create rule between it in item that association rules buys, by negative association rules is interested to item that do not buy, it is attempt to do data Maining more effectively. It is difficult that existent methods to find negative association rules find one part of rule, or negative association rules because use more complicated algorithm than algorithm that find association rules. Therefore, this paper presents method to create negative association rules by simpler process using Boolean Analyzer that use dependency between items. And as Boolean Analyzer through an experiment, show that can find negative association rules and more various rule through comparison with other algorithm.

• Korean Journal of Child Studies
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• v.25 no.5
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• pp.95-110
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• 2004

This research employs the grounded theory approach among various qualitative methodologies in order to reach a deep understanding of both the experiential process that children undergo in block play and the essential meaning of it. The objects of this study are 22 children(female 7, male 15) in a 5-year-old class of K kindergarten at Guro district, Seoul. The result of this research shows that first, children take pleasure in block play because of the delight and sense of accomplishment in building, the joy in demolishing, and the happiness of embracing the world through dramatic play with building structures. Second, the characteristics of children's block play are popular subject of the play, decision of the subject, impromptu transformation and elaboration of building structures, and flow of the play according to friend/non-friend relationship. Third, the implicit rules shared by children have more significant influences upon the block play than the agreed rules at the beginning of semester.

• Korean Journal of Child Studies
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• v.19 no.2
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• pp.147-157
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• 1998

The purpose of this study was to investigate children's ideas about self-regulation in such situational variables as the presence of explicit rules and the domains of social rules. The subjects were 6-, 9-, and 12-year-old children (344). To assess children's ideas about self-regulation, a procedure was devised in which children were presented with stories portraying a protagonist in a conflict between an implicit or an explicit rule and a personal desire. The children were asked to make a choice for the protagonist and to give reasons for their choice. Major findings as follows: (1) There was significant difference in children's self-regulation depending on the presence of explicit rules. (2) There was significant difference in children's self-regulation depending on the domains of social rules. In situations that involved no explicit rules for behaviors, there were significant differences depending on the domains. In situations that involved explicit rules for behaviors, for 6- and 9-year-old children, there was no significant difference depending on the domains. (3) Children's use of justifications for their choice of action varied as a function of the characteristics of the social rules.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.179-182
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• 2000

A Classifier System processes a discrete coded information from the environment. When the system codes the information to discontinuous data, it loses excessively the information of the environment. The Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit Bucket Brigade Algorithms. Also the FCS employs the Genetic Algorithms(GAs) to make new rules and modify rules when performance of the system needs to be improved. We will verify the effectiveness of the proposed FCS by applying it to AMR avoiding the obstacle.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.