• The Journal of Korean Academic Society of Nursing Education
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• v.19 no.3
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• pp.413-422
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• 2013

Purpose: This study was designed to explore the influencing factors on communication skill of nursing students and to investigate the relationship between learning style, critical thinking disposition and communication skill, Methods: The study sample was composed of 559 nursing students. Data was collected from 1st to 30th, May 2012 used a questionnaire which included Kolb's learning style inventory, critical thinking disposition inventory, communication skill inventory. The SPSS 18.0 Window program was used for descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient, and stepwise multiple regression. Results: Learning styles of subjects were diverging 39.5%, accommodating 32.6%, assimilating 22.7%, and converging 5.2%. The total mean score of critical thinking disposition and communication skill was 3.58 and 3.39 respectively. The score of Critical thinking disposition(t=3.06, p=.002) and Communication skill(t=3.25, p=.002) significantly differed between clinical practice satisfaction. Communication skill was the most significant predictor and accounted for 41.3% of the variance in critical thinking disposition in nursing students. Conclusion: It is important for students to use all four learning styles rather than to rely on one style. There should be more emphasis placed on the development of positive critical thinking disposition and communication skill of nursing students.

• Journal of Korean Academy of Nursing Administration
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• v.22 no.1
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• pp.1-10
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• 2016

Purpose: A descriptive survey was used in this study to identify the relationship between nurses' critical thinking disposition, self-directed learning readiness and professional nursing competency and to examine factors that influence professional nursing competency. Methods: The data were collected from 188 nurses and analyzed using t-test, ANOVA, $Scheff{\acute{e}}$' test, Pearson correlation, and stepwise multiple regression analysis with the SPSS/WIN 20.0 PC program. Results: Professional nursing competency was found to have significant pure correlations with critical thinking disposition (r=.59, p<.001), and self-directed learning readiness (r=.54, p<.001). The most influential factor influencing nurses' professional nursing competency was critical thinking disposition, followed by self-directed learning readiness (${\beta}=.25$, p=.003), work department (${\beta}=.19$, p=.001), total clinical career (${\beta}=.19$, p=.003), and position (${\beta}=.12$, p=.040), and these factors explained 43.8% of the variance in professional nursing competency. Conclusion: The findings indicate the necessity of developing and applying strategies and educational programs to enhance individual nurse's critical thinking disposition and self-directed learning readiness. Furthermore, exploration is needed on ways to enhance professional nursing competency.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.21-29
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• 2018

The purpose of this study is to explore supporting strategies for teaching and learning based on students' effect recognition and continuous intention in college flipped learning. It was analyzed 426 data by multivariate analysis of variance (MANOVA) by examining student's effect recognition and continuous intention on 15 flipped learning classes of K-university in Chungnam. The characteristics of learners were male, senior students, students who knew flipped learning, students who did not have previous experience, and students who were learning video at anytime. As a teaching strategy, it was found that effect recognition and continuous intention were high in the supplementary deepening flipped learning class and natural science or engineering area. As a teaching and learning supporting strategies, First, the university should develop and operate flipped class learning strategy program for females and low-grade students. Second, it should support the development of good flipped learning design and operation model of instructor. Third, it should support the development of high quality online learning contents that students can learn from time to time. Fourth, it should support the strengthening of teaching competency to develop and operate flipped learning classes. This study can be used as basic data to support and spread the effective flipped learning classes of the university in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1252-1271
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• 2013

Automatic image annotation has become an increasingly important research topic owing to its key role in image retrieval. Simultaneously, it is highly challenging when facing to large-scale dataset with large variance. Practical approaches generally rely on similarity measures defined over images and multi-label prediction methods. More specifically, those approaches usually 1) leverage similarity measures predefined or learned by optimizing for ranking or annotation, which might be not adaptive enough to datasets; and 2) predict labels separately without taking the correlation of labels into account. In this paper, we propose a method for image annotation through collaborative similarity metric learning from dataset and modeling the label correlation of the dataset. The similarity metric is learned by simultaneously optimizing the 1) image ranking using structural SVM (SSVM), and 2) image annotation using correlated label propagation, with respect to the similarity metric. The learned similarity metric, fully exploiting the available information of datasets, would improve the two collaborative components, ranking and annotation, and sequentially the retrieval system itself. We evaluated the proposed method on Corel5k, Corel30k and EspGame databases. The results for annotation and retrieval show the competitive performance of the proposed method.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.227-237
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• 2020

This study was designed to examine whether the self-directed learning method could improve self-directed learning readiness and the effects of academic achievement level. Self-directed learning readiness was investigated among 63 first-year Medical Terminology undergraduates in the C area. A repeat measurement variance analysis of the general linear model was conducted to evaluate the effects of improving self-directed learning readiness according to the general characteristics and level of academic achievement, while a regression analysis was performed to identify the factors affecting self-directed learning readiness. Self-directed learning readiness increased from 177.3 to 180.8 for those under 18 years of age, and 192.9 to 196.5 for those over 19 years of age (p<0.05). After the team activity, the overall self-directed learning readiness was improved, and both high- and low-achieving groups showed statistically significant improvements (p<0.05). The environment surrounding learners was confirmed to have a positive effect on improving self-directed learning when given the right degree of self-directed learning and appropriate feedback. The study results are expected to form basic foundation material for professors and class designers who want to draw self-directed learning skills from memorizing subjects.

• Journal of Digital Policy
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• v.3 no.1
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• pp.1-10
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• 2024

The purpose of this study was to identify the predictors of self-regulated learning strategies among nursing university students. Data were collected from 212 nursing university students in G metropolitan city and K city. The SPSS WIN 23.0 version program was used for data analysis. The data were analyzed using Pearson's correlation coefficient and multiple regression. There were significant correlations between media multitasking and self-regulated learning strategies(r=.45, p<.001), learning flow and self-regulated learning strategies(r=.59, p<.001), and media multitasking and learning flow(r=.32, p<.001). Friendship satisfaction, media multitasking and learning flow explained 45% of the variance for self-regulated learning strategies. To increase the self-regulated learning strategies among nursing university students, it is necessary to develop multiple interventions that enhance friendship satisfaction, media multitasking and learning flow.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.159-165
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• 2012

In this paper, the qualification process to target trainees of 200 questionnaires collected 158 call was using the SPSS 18.0 statistical program was statistical results are as follows. First, the demographic characteristics of 158 people a total of 146 people men (92.4%) were 30 and 40 adults. Second, according to gender for women than men showed high satisfaction of sayibeogang. More than a college education group showed a high level of satisfaction. Depending on the marital satisfaction of married people was higher sayibeogang. In addition, cyber-lecture on satisfaction surveys women, highly educated, married, educated in the group were satisfied. 74.5% of total variance, variance, with 88.7 percent reliability was very high reliability. Thus, students' demographic variables, motivation, learning to fit in the field of instructional design and content to support the various efforts will be.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2895-2912
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• 2014

Content-based image retrieval has been the most important technique for managing huge amount of images. The fundamental yet highly challenging problem in this field is how to measure the content-level similarity based on the low-level image features. The primary difficulties lie in the great variance within images, e.g. background, illumination, viewpoint and pose. Intuitively, an ideal similarity measure should be able to adapt the data distribution, discover and highlight the content-level information, and be robust to those variances. Motivated by these observations, we in this paper propose a probabilistic similarity learning approach. We first model the distribution of low-level image features and derive the free energy kernel (FEK), i.e., similarity measure, based on the distribution. Then, we propose a learning approach for the derived kernel, under the criterion that the kernel outputs high similarity for those images sharing the same class labels and output low similarity for those without the same label. The advantages of the proposed approach, in comparison with previous approaches, are threefold. (1) With the ability inherited from probabilistic models, the similarity measure can well adapt to data distribution. (2) Benefitting from the content-level hidden variables within the probabilistic models, the similarity measure is able to capture content-level cues. (3) It fully exploits class label in the supervised learning procedure. The proposed approach is extensively evaluated on two well-known databases. It achieves highly competitive performance on most experiments, which validates its advantages.