• Journal of the Korea Society of Computer and Information
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• v.23 no.6
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• pp.9-14
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• 2018

This paper focuses on how the latest progress in digital media and technology affects the writing education environment. In the contemporary era, collecting numerous pices of information online, and arranging them to create new knowledge is important. There is also a need to seek new methods for writing education to stay in tune with the times. To that end, this paper suggests an open writing model using hypertexts. This writing model consists of a total of five stages, which are use of information, compilation of information, open mutual discussion, search of additional information and writing to recreate knowledge. The final outcome of such writing is writing using footnotes. By describing the gist of the keyword and adding numerous footnotes, such writing opens up an infinite possibility of re-creating information into new knowledge. This method can help university students who are accustomed to the digital society to proactively use information and improve multi-disciplinary communication skills required today. This author applied such a model to university writing education and found that more than 82% of the students were satisfied. Through the process of collaboration and recreation of knowledge in writing, learners found distinct benefits and noted their horizons had broadened. Given this effect, the open writing model using hypertexts is meaningful in that it forms a learning community that goes beyond a one-way feedback from instructor to student and instead nudges students to realize collective intellect. Moreover, it is meaningful in that it moves away from a top-down approach of the instructors passing down knowledge about writing and its rules, and towards a more proactive involvement by students in creating knowledge.

• Journal of Korean Biological Nursing Science
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• v.15 no.3
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• pp.139-146
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• 2013

Purpose: The purpose of this study was to identify knowledge and the Need of pathophysiology, perceived connectivity between knowledge of pathophysiology and clinical practice, and confidence and satisfaction with clinical performance by applying the knowledge to clinical practice, and to analyze the correlation between the knowledge, perceived connectivity, and confidence and satisfaction of clinical nurses. Methods: Subjects consisted of 149 clinical nurses who work at 7 general hospitals and 6 university hospitals located in Seoul, Incheon, and Gyeonggi province in Korea. Those who learned pathophysiology during their undergraduate program were recruited by using a convenient sampling. The data were collected between April 23 and May 24 in 2012. Participants responded to self-administered questionnaires about knowledge and the need of pathophysiology, and the perceived connectivity between knowledge and clinical practice, and confidence and satisfaction with clinical performance by applying knowledge to clinical practice. Results: Mean scores of knowledge and the need of pathophysiology were $2.88{\pm}0.34$ and $2.33{\pm}0.39$ each. Scores of knowledge and the need of 3 units such as fluid and electrolyte imbalance, acid-base imbalance, and alterations in the respiratory system were high among 23 units while that of adolescent disorders was the lowest. The mean score of perceived connectivity between knowledge and clinical practice was $7.32{\pm}1.57$, and those of confidence and satisfaction with clinical performance by applying the knowledge to clinical practice were $7.66{\pm}1.66$ and $7.42{\pm}1.67$ respectively. Knowledge of pathophysiology was positively correlated with the perceived connectivity between knowledge and clinical practice, and confidence, and satisfaction with clinical performance. Conclusion: The results indicate that knowledge and the need of pathophysiology in clinical nurses is above average, and that knowledge of pathophysiology is important in promoting perceived connectivity with clinical practice and increasing confidence and satisfaction with clinical performance by applying knowledge to clinical practice.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1309-1314
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• 2003

If an agent has a learning ability using previous knowledge, then it is expected that the agent can speed up learning by interacting with environment. In this paper, we present an improved reinforcement learning algorithm using domain knowledge which can be represented by problem-independent features and their classifiers. Here, neural networks are employed as knowledge classifiers. To show the validity of our proposed algorithm, computer simulations are illustrated, where navigation problem of a mobile robot and a micro aerial vehicle(MAV) are considered.

• Journal of Families and Better Life
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• v.21 no.1
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• pp.91-102
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• 2003

The main purpose of this research was to examine the variables that influence environment-friendly management behaviors of family resources. For this study, environment-friendly management behaviors were classified into four categories: buying, using, recycling and disposal. Another purpose of this study was to find out the levels of environmental education, environmental knowledge, and environmental consciousness among household managers. In order to determine the levels, a structured survey questionnaire was administered and data were collected from 391 housewives who live in Seoul and Jeonju. The results show that environmental consciousness and environmental knowledge affected buying and disposal behavior. There was a significant difference in using behavior by the degree of environmental consciousness. The variables that affected recycling behavior were environmental education and environmental consciousness. The level of environmental awareness was highest in ! environmental consciousness, second highest in environmental knowledge, and lowest in environmental education.

• Knowledge Management Research
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• v.9 no.3
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• pp.141-158
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• 2008

This study introduces the construct of group efficacy and combines the construct with the Adaptive Structuration Theory(AST) and user satisfaction framework. Job efficacy and IT efficacy identified as the dimensions of group efficacy are hypothesized to influence faithfulness of appropriation and consensus on appropriation. and then the user satisfaction of knowledge management systems(KMS). Data were collected through a survey of respondents who have the experience of using KMS, and analyzed by using structural equations modelling. The major findings of this study are as follows: First, job efficacy was found to have a positive influence on consensus on appropriation and user satisfaction. Second, IT efficacy was found to have a positive effect on faithfulness of appropriation and consensus on appropriation. Third, faithfulness of appropriation was found to have a positive influence on consensus on appropriation and user satisfaction. Implications of these findings are discussed for researchers and practitioners.

• Research in Mathematical Education
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• v.8 no.2
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• pp.81-93
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• 2004

This study investigated three preservice teachers' mathematical problem solving among hand-in-write-ups and final projects for each subject. All participants' activities and computer explorations were observed and video taped. If it was possible, an open-ended individual interview was performed before, during, and after each exploration. The method of data collection was observation, interviewing, field notes, students' written assignments, computer works, and audio and videotapes of preservice teachers' mathematical problem solving activities. At the beginning of the mathematical problem solving activities, all participants did not have strong procedural and conceptual knowledge of the graph, making a model by using data, and general concept of a sine function, but they built strong procedural and conceptual knowledge and connected them appropriately through mathematical problem solving activities by using the computer technology.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.115-130
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• 2004

This paper proposes how to build an expert profile database in KMS, which provides the information of expertise that each expert possesses in the organization. To manage tacit knowledge in a knowledge management system, recent researches in this field have shown that it is more applicable in many ways to provide expert search mechanisms in KMS to pinpoint experts in the organizations with searched expertise so that users can contact them for help. In this paper, we develop a framework to automate expert classification using a text categorization technique called Vector Space Model, through which an expert database composed of all the compiled profile information is built. This approach minimizes the maintenance cost of manual expert profiling while eliminating the possibility of incorrectness and obsolescence resulted from subjective manual processing. Also, we define the structure of expertise so that we can implement the expert classification framework to build an expert database in KMS. The developed prototype system, "Knowledge Portal for Researchers in Science and Technology," is introduced to show the applicability of the proposed framework.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.983-987
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• 2001

This paper deals with an automated computer-aided process planning system by which designer can determine operation sequences even if they have little experience in process planning of wheel bolt products by a multi-stage former. The approach to the system is based on knowledge-based rules and a process knowledge base consisting of design rules is built. Knowledge for the system is formulated from plasticity theories, empirical results and the empirical knowledge of field experts. Programs for the system have been written in AutoLISP for the AutoCAD using a personal computer and are composed of two main modules. An attempt is made to link programs incorporationg a number of expert design rules to form a useful package. Results obtained using the modules enable the designer and manufacturer of wheel bolt product to be more efficient in this field.

• The Korean Journal of Rehabilitation Nursing
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• v.16 no.1
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• pp.63-70
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• 2013

Purpose: The purpose of this study was to investigate blue color workers' knowledge and attitudes about cardiocerebrovascular disease, and these influence on their preventive health behavior. Methods: Data were collected from 235 blue color workers using questionnaire in 'H' city. Data were analyzed using hierarchical multiple regression analysis with SPSS/WIN 17.0 version. Results: Health behaviors for preventing cardiocerebrovascular disease were correlated positively with knowledge about cardiocerebrovascular disease. In addition, preventive behaviors for cardiocerebrovascular disease was influenced by marital status, monthly income, past history of cardiocerebrovascular disease, and knowledge about cardiovascular disease. Conclusion: Knowledge about cardiocerebrovascular disease has an effect on preventive behaviors regarding cardiocerebrovascular disease. For preventing cardiocerebrovascular disease, national campaign and health education program in each industry need to be built.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.113-124
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• 2022

News in the form of web data generates increasingly large amounts of information as unstructured text. The capability of understanding the meaning of news is limited to humans; thus, it causes information overload. This hinders the effective use of embedded knowledge in such texts. Therefore, Automatic Knowledge Extraction (AKE) has now become an integral part of Semantic web and Natural Language Processing (NLP). Although recent literature shows that AKE has progressed, the results are still behind the expectations. This study proposes a method to auto-extract surface knowledge from English news into a machine-interpretable semantic format (triple). The proposed technique was designed using the grammatical structure of the sentence, and 11 original rules were discovered. The initial experiment extracted triples from the Sri Lankan news corpus, of which 83.5% were meaningful. The experiment was extended to the British Broadcasting Corporation (BBC) news dataset to prove its generic nature. This demonstrated a higher meaningful triple extraction rate of 92.6%. These results were validated using the inter-rater agreement method, which guaranteed the high reliability.