• Educational Technology International
• /
• v.13 no.1
• /
• pp.207-231
• /
• 2012

The purpose of this study is to analyze the effects of Critical Thinking Strategy supporting argumentation activities between learners. The research question is whether the form of Critical Thinking Strategy offered to support meaningful interaction of collaborative argumentation between learners influences the knowledge acquisition, learning outcome, and student satisfaction. For this, the collaboration outcome of the group, the level of individual knowledge acquisition, the level of students satisfaction were measured as outcome of argumentation activity and their differences analyzed. This study concludes the following: A comparison of the group that was provided with Critical Thinking Strategy (test group) and the group provided with general argumentation scaffolds (compared group) showed there wasn't statistically significant differences in the quality of the learning outcome of collaboration between the groups and in students satisfaction. But there was significant difference in the degree of individual acquisition depending on the offering of scaffolding for Critical Thinking. Therefore, as premised in this study, supporting meaningful mutual interaction between learners during collaborative argumentation using Critical Thinking Strategy has a positive influence on the individual acquisition of domain knowledge. The group provided with scaffolding for Critical Thinking gained higher effect in the degree of knowledge sharing and individual acquisition of domain knowledge compared to the group provided with general argumentation scaffolding.

• Journal of KIISE:Software and Applications
• /
• v.26 no.8
• /
• pp.1010-1017
• /
• 1999

불완전한 계획 영역 이론은 오류 영역(noisy domain)에서 하나의 상태에 상반된 연산자들이 적용되는 불일치성 문제를 야기할 수 있다. 이 문제를 해결하기 위해서 본 논문은 상태를 기술하기 위해 다치 논리를 도입하여 제어지식으로서의 부정적 선행조건을 학습하는 새로운 방법을 제안한다. 기계에는 알려지지 않은 이러한 제어지식이 인간에게는 반대개념으로 잠재적으로 사용되고 있다. 이러한 잠재된 개념을 학습하기 위해 본 논문은 반대 연산자들로 구성된 사이클을 영역이론으로부터 기계적으로 생성하고, 이 연산자들에 대한 실험을 통해 반대 리터럴(literal)들을 추출한다. 학습된 규칙은 불일치성을 방지하면서 동시에 중복된 선행조건을 제거하여 연산자를 단순화시킬 수 있다.Abstract An incomplete planning domain theory can cause an inconsistency problem in a noisy domain, allowing two opposite operators to be applied to a state. To solve the problem, we present a novel method to learn a negative precondition as control knowledge by introducing a three-valued logic for state description. However, even though the control knowledge is unknown to a machine, it is implicitly known as opposite concept to a human. To learn the implicit concept, we mechanically generate a cycle composed of opposite operators from a domain theory and extract opposite literals through experimenting the operators. A learned rule can simplify the operator by removing a redundant precondition while preventing inconsistency.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.1
• /
• pp.77-92
• /
• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

• Journal of Korean Academic Society of Home Health Care Nursing
• /
• v.29 no.1
• /
• pp.18-30
• /
• 2022

Purpose: Purpose: This study aimed to identify whether infection control practice would correlate significantly with the knowledge and attitude of infection control in the pre-, mid-, and postvisiting rounds among community-visiting nurses. Methods: A descriptive study was conducted based on the knowledge, attitude, and practice (KAP) model by administrating questionnaires during September-October 2020. A total of 65 nurses working for 15 community health centers in Seoul, South Korea were included. The questionnaires were developed based on the epidemiologic triangle model and comprised of 28 items on practice, 18 items on knowledge, and 10 items on attitude. Results: The infection control practice showed a mean of 88.9 (range, 0-100). The infection control knowledge had 89.2% on the host domain, 80.0% on the environment domain, and 74.8% on the agent domain (range, 0-100). The infection control attitude showed a mean of 39.5 (range, 0-50). Higher scores on the infection control practice are significantly correlated with the higher scores on the infection control knowledge about the host domain (p= .004) at the pre-, mid-, and post-visiting rounds. Higher scores on the infection control practice are significantly correlated with the higher scores on the infection control attitude at the mid- (p= .018) and postvisiting rounds (p= .028). Conclusions: The infection control practice by community-visiting nurses may be enhanced with increased knowledge and attitude levels of infection control at the mid- and post-visiting rounds. The enhancement should be included in the on-the-job education for community-visiting nurses.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1993.10a
• /
• pp.142-148
• /
• 1993

Recectly, development of expert system utilizing the domain specific knowledge focuses upon the machining operations. This paper describes an expert system for selecting the optimum grinding wheel based on the Analytic Hierarchy Process and Fuzzy Logic. Knowledge-base, in this system, for selecting of grinding wheel is designed to appling the knowhow and experience knowledge of skilled hands. In this paper, firstly determination method of fuzzy membership function utilizing the qualitative knowledge, and then selection of the optimum wheel from among the available components according to Saaty's priority rule are described. Lastly,some implementation results are suggested.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.20 no.3
• /
• pp.77-104
• /
• 1995

Intelligent Decision System support the decision analysis process in the managerial problems with decision analytic knowledge as well as domain specific knowledge. Influence Diagram has been one of the major knowledge representation in the intelligent decision system. In the development of intelligent decision system, knowledge acquisition is also known to be difficult. This paper suggests a developing tool using an influence diagram and Verbal Protocol Analysis which facilitates knowledge acquision for intelligent decision system. An ennvironmental decision making problem is used as an illustrative example and validation of the suggested developing tool is discussed. The suggested tool is very flexible to be expanded or applied to similar problems.

• Knowledge Management Research
• /
• v.19 no.3
• /
• pp.47-61
• /
• 2018

A knowledge map, a taxonomy of knowledge repositories, must have capabilities supporting and enhancing knowledge user's activity to search and select proper knowledge for problem-solving. Conventional knowledge maps, however, have been hierarchically categorized, and could not support such activity that must coincide with the user's cognitive process for knowledge utilization. This paper, therefore, aims to verify and develop a methodology to build a networked knowledge map that can support user's activity to search and retrieve proper knowledge based on the referential navigation between content-relevant knowledge. This paper deploys keywords as the semantic information between knowledge, because they can represent the overall contents of a given document, and because they can play the role of semantic information on the link between related documents. By aggregating links between documents, a document network can be formulated: a keyword-based networked knowledge map can be finally built. Domain expert-based validation test was also conducted on a networked knowledge map of 50 research papers, which confirmed the performance of the proposed methodology to be outstanding with respect to the precision and recall.

• Proceedings of the Korean Information Science Society Conference
• /
• 1999.10b
• /
• pp.101-103
• /
• 1999

Decentralized 정보는 여러 도메인에 대한 heterogeneous한 독립적인 정보가 자율적으로 존재하며 이들 정보간의 관계성의 고려한 전체에 대한 global view가 존재하지 않기 때문에 inter-domain에 대한 마이닝을 수행하는데 어려움이 있다. 본 연구에서는 intra-domain knowledge discovery, intra 및 inter-domain problem solving method라는 접근방법으로, decentralized 데이터 환경에서 문제 해결에 필요한 정보 추출을 위한 데이터 tailoring과 분산 데이터에 대한 목표-지향 데이터마이닝(goal-oriented data-mining)을 통해 문제 해결을 위해 필요한 지식을 생성하고 이들 간의 관련 정보를 탐색하여 문제를 해결하는 프레임워크를 제안한다. 특히, 생성된 지식간의 협동 문제 처리를 멀티 에이전트 패러다임을 이용하기로 한다. 제안 프레임워크는 산재되어 있는 데이터들로부터 문제 해결에 유용한 지식 차원의 정보를 추출해내고 생성된 지식을 바탕으로 각 도메인 정보에 대한 개별적인 사용뿐 만 아니라 서로 cooperation을 통한 문제 해결을 지원함으로써, 개방된 분산 환경하에 decentralized 되어 있는 여러 도메인 정보를 보다 효율적으로 활용할 수 있는 새로운 형태의 문제 해결 방법이라고 할 수 있다.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.345-348
• /
• 2005

Because the current web search engine estimates the similarity of documents, using the frequency of words, many documents irrespective of the user query are provided. To solve these kinds of problems, the semantic web is appearing as a future web. It is possible to provide the service based on the semantic web through ontology which specifies the knowledge in a special domain and defines the concepts of knowledge and the relationships between concepts. In this paper to search the information of potential customers for home-delivery marketing, we model the specific domain for generating the ontology. And we research how to retrieve the information, using the ontology. Therefore, in this paper, we generate the ontology to define the domain about potential customers and develop the search robot which collects the information of customers.

• Journal of Korea Multimedia Society
• /
• v.4 no.3
• /
• pp.280-285
• /
• 2001

Since KBANN (knowledge-based artificial neural network) combing the inductive learning algorithm and the analytical learning algorithm was proposed, several methods such as TopGen, TR-KBANN, THRE-KBANN which modify KBANN have been proposed. But these methods can be applied when there is a domain theory. The algorithm representing the problem into KBANN based on only the instances without domain theory is proposed in this paper. Domain theory represented into KBANN can be refined by THRE-KBANN. The performance of this algorithm is more efficient than the C4.5 in the experiment for some problem domains of inductive learning.