• Journal of Korean Academy of Fundamentals of Nursing
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• v.16 no.4
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• pp.459-471
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• 2009

Introduction: Critical thinking involves identifying problem(s), assessing resources, and generating possible solutions and allows clinical nurses to decide which solution is the most reasonable under the given circumstances, taking into consideration the "hat ifs" and how they will affect the end result. This research was conducted to further understanding and identification of subjective factors in critical thinking in clinical nurses. Methods: The research design was a Q-Methodological Approach. Q-population was formulated from a non-structured questionnaire and interviews from 17 experienced clinical nurses. Thirty selected Q-statements were sorted by 30 experienced clinical nurses. Results: Four factors for critical thinking were identified: (1) Deductive reasoning based on causal relation, (2) Construction of an effective model based on patients' responses, (3) Formulating categories based on priorities for effective interventions, and (4) Judging validity of the situational significance on clinical performances. Conclusion: Critical thinking is an attitude and reasoning process. From this study, the frame of reference for clinical nurses in formulating critical thinking within the context of clinical settings is identified and indicates the way nurses utilize thinking skills when they care for patients and areas that need further exploration as nurses and faculty develop education systems to advance clinical performance competency.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.114-122
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• 2003

Dynamic and fragmented characteristics are two of the most significant factors that distinguish the construction industry from other industries. Previous forecasting techniques have failed to solve the problems derived from the above characteristics, and do not provide considerable support This paper deals with providing a more precise forecasting by applying Case-based Reasoning (CBR). The newly developed model in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting, the choice of the numbers of referring projects was investigated. It is concluded that selecting similar projects at $5{\~}6{\%}$ out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

• Architectural research
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• v.8 no.1
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• pp.77-84
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• 2006

In spite of enormous increase in data generation, its practical usage in the construction sector has not been prevalent enough compared to those of other industries. The author would explore the obstacles against efficient data application in the arena of expenditure forecasting, and suggest a forecasting method by applying Case-based Reasoning (CBR). The newly suggested method in the research, enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. Among 99 projects collected, the cost data from 88 projects were processed to establish a new forecasting model. The remaining 10 projects were utilized for the validation of the model. From the comprehensive study, the choice of the numbers of referring projects was investigated in detail. It is concluded that selecting similar projects at 12~19 % out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

• Journal of the Korean earth science society
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• v.33 no.5
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• pp.459-466
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• 2012

This study aimed to investigate the characteristics of pre-service elementary teachers' understanding about scientific inquiry in terms of designing exploration and reasoning that is used to formulate explanations based on evidence. The research context was an open inquiry with using the Science Writing Heuristic (SWH) template in which participant students were not provided with inquiry questions. As data, lab. 39 pre-service elementary teachers participated in this study while taking their science methods course. Analyses of the reports were framed by the cognitive processes of inquiry (Chinn and Malhotra, 2002) and each report was coded and analyzed by the framework of inquiry (Tytler and Peterson, 2004). Results showed that groups' works that utilized the SWH template encouraged the participants to interact each other about scientific inquiry. They came up with more relevant and testable questions for their scientific inquiry. It implicates that children will be able to have chances of testing their own questions more properly by using the SWH template in science classes just as the participants did in this study. The use of the SWH template would help pre-service teachers to teach appropriately how to test inquiry questions to their students in the future. Discussion was made to figure out the characteristics or Korean pre-service elementary teachers' understanding about scientific inquiry.

• The Mathematical Education
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• v.54 no.3
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• pp.207-222
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• 2015

Mathematics preservice teachers' Mathematical Content Knowledge ("MCK") includes not only knowledge for mathematics, but also academic knowledge for school mathematics and mathematical process knowledge. We can consider the items in PISA 2012 as suitable tools to assess process knowledge as well as mathematical content knowledge because these items are developed by competent international educational experts. Therefore, the responses to items with the low percentage of correct answers in conjunction with the mathematical contents were analyzed with focus on FMC. The results showed the reasoning competency in responses using the conditions of the problem and of understanding the conditions after reading the complex problems within the context (i.e. the reasoning and argumentation competency, and communication competency) requires improvements. Furthermore the results indicated the errors due to a lack of ability of devising strategies for problem solving. Based on the foregoing results, the implications towards the directions of the education for the preservice mathematics teachers have been derived.

• Education of Primary School Mathematics
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• v.18 no.1
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• pp.45-59
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• 2015

This study focused on the classification of triangles by angles in elementary school mathematics. We examined Korean national mathematics curriculum from the past to the present. We also examined foreign textbooks and the Euclid's . As a result, it showed that the classification is not indispensable from the mathematical and the perceptual viewpoint. It is rather useful for students to know the names of triangles when studying upper level mathematics in middle and high schools. This study also suggested that the classification be introduced in elementary school mathematics in the context of reasoning and inquiring as shown foreign textbooks, and example topics for the reasoning and inquiring.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.979-986
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• 2021

Online food delivery and food delivery apps have continued to grow exponentially in Malaysia. Fundamental aspects in entrepreneurship of the food and beverage industry, such as knowledge and attitudes towards online food delivery services being outsourced, have yet to be extensively recognized. The present study intends to explore this area of subject matter within the Malaysian context by using behavioral reasoning theory. The actual interview for this study took place in May 2020, and 14 interviews had been carried out. All interviews were audio-recorded with the consent from the respondents for reference purposes and subsequently transcribed verbatim. The transcripts were then checked against audio records. Content analysis was used to analyze the transcripts by focusing on n frequency counts and coding of themes. A qualitative method has been adopted by employing an interview to elicit the perspectives of entrepreneurs from Sarawak on the determinants and barriers in outsourcing online food delivery services. Results indicate that high potential in revenue, broad exposure to reach customers, convenience, and provision of job opportunities are the four factors that determine to outsource. In contrast, food quality maintenance, trustworthiness, high cost incurred, and consumer technology resistance are four factors that serve as barriers towards outsourcing third-party online delivery.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.319-330
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• 2021

Recently, in the field of open domain natural language question answering, multi-task, multi-hop question answering has been studied extensively. In this paper, we propose a novel deep neural network model using hierarchical graphs to answer effectively such multi-task, multi-hop questions. The proposed model extracts different levels of contextual information from multiple paragraphs using hierarchical graphs and graph neural networks, and then utilize them to predict answer type, supporting sentences and answer spans simultaneously. Conducting experiments with the HotpotQA benchmark dataset, we show high performance and positive effects of the proposed model.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.2
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• pp.100-109
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• 2007

Object recognition of service robots is very important for most of services such as delivery, and errand. Conventional methods are based on the geometric models in static industrial environments, but they have limitations in indoor environments where the condition is changable and the movement of service robots occur because the interesting object can be occluded or small in the image according to their location. For solving these uncertain situations, in this paper, we propose the method that exploits observed objects as context information for predicting interesting one. For this, we propose the method for modeling domain knowledge in probabilistic frame by adopting Bayesian networks and ontology together, and creating knowledge model dynamically to extend reasoning models. We verify the performance of our method through the experiments and show the merit of inductive reasoning in the probabilistic model

• KIPS Transactions on Software and Data Engineering
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• v.9 no.8
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• pp.243-250
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• 2020

Knowledge graph-based question answering not only requires deep understanding of the given natural language questions, but it also needs effective reasoning to find the correct answers on a large knowledge graph. In this paper, we propose a deep neural network model for effective reasoning on a knowledge graph, which can find correct answers to complex questions requiring multi-hop inference. The proposed model makes use of highly expressive bilinear graph neural network (BGNN), which can utilize context information between a pair of neighboring nodes, as well as allows bidirectional feature propagation between each entity node and one of its neighboring nodes on a knowledge graph. Performing experiments with an open-domain knowledge base (Freebase) and two natural-language question answering benchmark datasets(WebQuestionsSP and MetaQA), we demonstrate the effectiveness and performance of the proposed model.