• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.721-728
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• 2023

This study was conducted with the aim of exploring the essence of the achievements experienced by nursing students while enhancing their problem-solving abilities through simulation practical training. The study participants included 13 fourth-year nursing students, and data were collected through individual interviews conducted after the simulation practical training. Data analysis followed the qualitative research method of content analysis, involving coding, categorization, and thematization of the data. The results of this study revealed that nursing students' achievement experiences through simulation practical training included the following processes: "confirming confidence through improvement," "acknowledging change," "experiencing nursing self-efficacy," and "getting closer to the goal of clinical practice." Furthermore, it is suggested that efforts should be made to implement efficient operation and evaluation tools through multifaceted and meticulous design to promote integrated learning through simulation practical training and to confirm the process of internalizing knowledge through reflection by nursing students.

• Asia-Pacific Journal of Business
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• v.14 no.2
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• pp.85-100
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• 2023

Purpose - The purpose of this study is to identify the factors of shocking events in the career aspect experienced by Korean workers in the context of the Covid-19 pandemic, and to find out whether these career shocks affect individual perceptions of the importance of subjective career success. Design/methodology/approach - In the survey of 146 respondents, the career shock events experienced in the context of the Covid-19 pandemic were largely divided into three categories; 'work change', 'employment anxiety', and 'life anxiety'. For the subjective career success, seven dimensions - 'financial security', 'financial achievement', 'entrepreneurship', 'positive relationship', 'positive impact', 'learning and development', 'work-life balance' - were used. Findings - As a result, there was no difference in the perception of subjective career success due to the experience of 'work change' during the Covid-19 period. However, the respondents who experienced 'employment anxiety' came to recognize that 'financial security' and 'financial achievement' were more increasing in terms of the degree of difference of importance. And respondents who experienced 'lifetime anxiety' perceived that the degree of difference of importance was increasing in the six dimensions except for 'social influence'. Particularly, the increase in the importance of 'work-life balance' and 'positive relationship' was found to be the greatest among the career success dimensions. Research implications or Originality - Finally, it was concluded that changes in the external environment such as Covid-19 pandemic influence as a career shock and affect the level of importance in subjective career success perception. Based on the results, the theoretical implication on current career study and some practical implications for organizational career management were suggested.

• Information Systems Review
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• v.23 no.4
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• pp.69-86
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• 2021

As artificial intelligence technology advances, personalized recommendation systems using big data have attracted huge attention. In the case of beauty products, product preferences are clearly divided depending on customers' skin types and sensitivity along with individual tastes, so it is necessary to provide customized recommendation services based on accumulated customer data. Therefore, by employing deep learning methods, this study proposes a neural network-based recommendation model utilizing both product search history and context information such as gender, skin types and skin worries of customers. The results show that our model with context information outperforms collaborative filtering-based recommender system models using customer search history.

• Journal of The Korean Association For Science Education
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• v.28 no.6
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• pp.507-518
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• 2008

The purpose of this study was to find out the difficulties of teaching the chapter on 'ight', experience of learning, teaching methods, and thinking types of 10 science teachers of the master's course in chemistry education. Discussion course for abduction thinking was carried out during 12 hours after the interview. Data were collected from individual interviews of 4 teachers among the 10 subjects and from the reports of the science teachers after the discussion course. From the data, it was found that most of the science teachers had suffered difficulty in teaching the chapter on light before the discussion course. Most of them had tried to teach drawing the path of light, but there was little teaching effect. Their teaching methods were similar to the method of what they had learned. During the course, the teachers recognized they could not see the path of light directly, and it needed inferring from image. From the abduction thinking, the teachers recognized the meaning of image and gained concrete methods in teaching students.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.95-103
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• 2024

The purpose of this study was to examine the perceptions and beliefs of physical education teachers regarding learner-centered physical education and to qualitatively explore the stories of physical education teachers that appear in the field of practicing physical education curriculum. The research method was qualitative research, and data were collected and recorded through semi-structured questionnaires, individual interviews, group interviews, and metaphor records, and the data were analyzed through domain analysis and classification analysis. The study was able to derive results by dividing them into 'learner focus', 'overall development', and learning evaluation' in relation to physical education teachers' meaning construction of learner-centered physical education. And the practice of learner-centered physical education and its limitations were presented. In conclusion, the holistic development of learner-centered physical education includes addressing physical, cognitive, social, and emotional aspects. It is believed that this approach will not only measure student progress but also actively contribute to their development as individuals.

• Quality Improvement in Health Care
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• v.29 no.2
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• pp.2-14
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• 2023

Purpose: This study aimed to identify strategies to enhance the competencies of patient safety coordinators in Korea. Methods: Fourteen participants from nine hospitals were interviewed between May and November 2022. Qualitative content analysis was used to analyze the data. Results: As for the strategies to enhance patient safety management competency, 3 themes and 11 sub-themes were derived. The first theme was 'Having individual competence as a patient safety coordinator', and the sub-themes were 'Communication skills with members', 'Flexible thinking from multiple perspectives', and 'Preparing for administrative work competencies that they had not experienced as a nurse.' The second theme was 'Responding strategically to promote improvement activities', and the sub-themes for it were 'Multi-angle approach to the problem', 'A careful approach so as not to be taken as criticism in the field', 'Increasing the possibility of improvement activities through awareness', 'Activating the network between patient safety coordinators', and 'Expanding learning opportunities through patient safety case analysis.' The third theme was 'Obtaining support to facilitate patient safety activities', and the sub-themes for this were 'Improving staff awareness of patient safety', 'Providing a training course for nurse professional of patient safety', and 'Expanding the manpower allocation standard of patient safety coordinators.' Conclusion: This study explored personal competencies such as document writing and computer utilization capabilities, focused on ways to improve the field of patient safety management, and emphasized the need for organizational and political support.

• The Research Journal of the Costume Culture
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• v.32 no.2
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• pp.199-214
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• 2024

Until now, research on consumers' purchasing behavior has primarily focused on psychological aspects or depended on consumer surveys. However, there may be a gap between consumers' self-reported perceptions and their observable actions. In response, this study aimed to investigate consumer purchasing behavior utilizing a big data approach. To this end, this study investigated the purchasing patterns of fashion items, both online and in retail stores, from a data-driven perspective. We also investigated whether individual consumers switched between online websites and retail establishments for making purchases. Data on 516,474 purchases were obtained from fashion companies. We used association rule analysis and K-means clustering to identify purchase patterns that were influenced by customer loyalty. Furthermore, sequential pattern analysis was applied to investigate the usage patterns of online and offline channels by consumers. The results showed that high-loyalty consumers mainly purchased infrequently bought items in the brand line, as well as high-priced items, and that these purchase patterns were similar both online and in stores. In contrast, the low-loyalty group showed different purchasing behaviors for online versus in-store purchases. In physical environments, the low-loyalty consumers tended to purchase less popular or more expensive items from the brand line, whereas in online environments, their purchases centered around items with relatively high sales volumes. Finally, we found that both high and low loyalty groups exclusively used a single preferred channel, either online or in-store. The findings help companies better understand consumer purchase patterns and build future marketing strategies around items with high brand centrality.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.565-577
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• 2024

This discussion looked at how emotional adjectives are connected in the format [[emotional adjective + '-ko(and)'] + emotional adjective]. As a result, it was confirmed that there are quite a few cases in which two or more emotional adjectives are used to express emotions in Korean. This can help Korean learners understand and express the individual lexical meanings of emotional adjectives more clearly by identifying emotional adjectives that are used together with the corresponding configuration. It was believed that it could help Korean language learners express complex emotions or create rich emotional expressions when expressing their emotions in Korean. It is hoped that the examples and frequency of [[emotional adjective+'-ko(and)'+emotional adjective] shown in this discussion will be of some help in teaching and learning Korean emotional vocabulary.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.