• Journal of Korean Academy of Nursing Administration
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• v.17 no.1
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• pp.33-43
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• 2011

Purpose: This study was done to develop a web-based diagnostic evaluation program for nurses to prevent malpractice liability. Methods: A comprehensive review of the literature and 9 specialist interviews were used to search for learning goals and content for protection for nurses from malpractice. Data on needs for learning goals were collected from 56 hospital nurses who agreed to complete a self-report questionnaire. The diagnostic program was evaluated between September 2008 and August 2009 by 35 new hospital nurses using an application of the web-based program evaluation tools by Chung (2000). Results: A comprehensive review of the literature and interviews were used to search for learning goals and content. The evaluation program was composed of the 73 questions for diagnostic evaluation under 23 learning goals and 6 grand learning goals which included the principles of law, patient's rights, legal responsibility, patient's safety, regulation on nursing practice and patient's rights protection. Evaluation of the program showed that the mean for program evaluation was 3.43 (SD=.37). Conclusion: This diagnostic evaluation program could be an efficient method for teachers and learners to improve nurses' behavior in protecting the patient's rights and preventing malpractice claims.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1819-1826
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• 2009

To enhance the automatic performance of existing predicting and planning algorithms that require a predefined probability of the states' transition, this paper proposes a multiple sequence generation system. When interacting with unknown environments, a virtual agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. We describe a sequential behavior generation method motivated from the change in the agent's state in order to help the virtual agent learn how to adapt to unknown environments. In a sequence learning process, the sensed states are grouped by a set of proposed motivation filters in order to reduce the learning computation of the large state space. In order to accomplish a goal with a high payoff, the learning agent makes a decision based on the observation of states' transitions. The proposed multiple sequence behaviors generation system increases the complexity and heightens the automatic planning of the virtual agent for interacting with the dynamic unknown environment. This model was tested in a virtual library to elucidate the process of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3266-3285
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• 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.143-150
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• 2024

With the development of AI, multimedia tools in education offer personalized learning environments, which foster individual competencies. This study aims to examine the effectiveness of smart education as perceived by learners through a case study of university writing classes utilizing multimedia learning tools, and to explore potential applications. To achieve this, a writing course incorporating various multimedia tools to promote interaction was designed and implemented over the course of one semester, targeting 42 university students. Through the semester, student reactions and survey results were analyzed to investigate the effects and satisfaction levels regarding the use of multimedia learning tools in writing instruction as perceived by students. The analysis revealed that multimedia-assisted writing classes effectively fostered learners' autonomy by focusing on individual needs, while also promoting interaction and encouraging spontaneous participation. Students reported recognizing the presence of diverse perspectives by comparing and communicating about each other's writing, leading to an expansion of their own thinking. In using ChatGPT, it was found that students attempted to refine their questions until they obtained the desired answers. They reported that this process deepened their understanding of the essence of the questions. These benefits led to results of high levels of students' active class engagement and satisfaction. This study contributes foundational and empirical data regarding the effectiveness and potential applications of learner-centered smart education as part of fourth industrial revolution integration research.

• Journal of Engineering Education Research
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• v.19 no.1
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• pp.21-30
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• 2016

Communication is for making a passage to communicate with various modern narratives or various people. Therefore, it needs to be reorganized by these changes and demands. Especially in case of country students learning natural science and engineering are appreciating the necessity of speaking education. So a program which contains both speaking and writing should be organized. In this writing writer used flipped learning and strategic text to fulfill evaluation items that engineering authentication requires. Also writer suggested how to lecture and planned to make a integration textbook which can foster literacy and liberal arts knowledge.

• The Journal of Korean Academic Society of Nursing Education
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• v.19 no.3
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• pp.362-370
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• 2013

Purpose: The purpose of this study was to develop and evaluate effects of the integrative fidelity simulation curriculum. Methods: The integrative fidelity simulation curriculum was developed through meetings of experts based on nursing content. To test the application effect of simulation curriculum, a one group pre-post test design was applied. The simulation curriculum was applied with 149 nursing students who participated voluntarily. Results: In the application of satisfaction of the curriculum, learning interest in nursing and intrinsic motivation, nursing students had high scores in all evaluations. In addition, satisfaction of the curriculum had a significant positive correlation with learning interest in nursing and intrinsic motivation. Conclusion: The integrative fidelity simulation was an effective teaching tool for nursing students, and needs to develop more varied nursing simulation scenarios and curriculum.

• The Journal of Sustainable Design and Educational Environment Research
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• v.4 no.4
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• pp.25-38
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• 2004

Schools have focused so far on a student-oriented education. As the roles of schools, however, have been increasingly emphasized in the information society, community-centered functions are now additionally required. Beyond simply allowing communities to utilize selected facilities, schools can conduct re-education programs for community residents and actively use their facilities for such purposes. As explained above, schools must continuously evolve to meet current needs and demands, such as by offering special classes and utilizing learning facilities in the elementary levels to promote learning in ever-changing societies. This study analyzed the functions of school facilities to communities, as well as the educational functions involved in teaching-learning processes, in light of the advent of a knowledge and information society. Through analysis, the types of information facilities in elementary schools were derived. On the basis of such derived types, systematic and reasonable methods to estimate the scope were suggested.

• Korean Journal of Child Studies
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• v.22 no.1
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• pp.191-211
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• 2001

This in-depth study of portfolio method centered on efficient methods of application, including teacher education. The study was carried out in 2 public kindergartens in Taegu. The efficient portfolio assessment method was developed by revisions after successive applications, observations, and discussions with the teachers of both kindergartens. The resulting efficient portfolio method is composed of step 1: portfolio conference and planning; step 2: development of the portfolio in the process of teaching and learning; step 3: selection of the materials for the portfolio; step 4: analysis of the portfolio; and step 5: use of the portfolio method. The practical application of the portfolio assessment is included in the forms used for teachers' observations of children's play and educational interventions. Teachers' interventions include verbal interaction, presentation of materials, and participating as partners. This teaching-learning method consists of teaching and assessment by sensitive and instant responses to children's needs.

• The Journal of Information Technology and Database
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• v.8 no.1
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• pp.95-104
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• 2001

In the text classification domain, labeling the training documents is an expensive process because it requires human expertise and is a tedious, time-consuming task. Therefore, it is important to reduce the manual labeling of training documents while improving the text classifier. Selective sampling, a form of active learning, reduces the number of training documents that needs to be labeled by examining the unlabeled documents and selecting the most informative ones for manual labeling. We apply this methodology to Naive Bayes, a text classifier renowned as a successful method in text classification. One of the most important issues in selective sampling is to determine the criterion when selecting the training documents from the large pool of unlabeled documents. In this paper, we propose two measures that would determine this criterion : the Mean Absolute Deviation (MAD) and the entropy measure. The experimental results, using Renters 21578 corpus, show that this proposed learning method improves Naive Bayes text classifier more than the existing ones.