• Journal of the Korean Society for Library and Information Science
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• v.57 no.4
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• pp.25-47
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• 2023

Library collaboration instruction (LCI) is a process in which a classroom teacher and librarian collaborate to co-planning, co-implementation, co-assessment instruction. LCI is being studied and modeled in various dimensions such as the level of collaboration, information activities, and time scheduling. However, there is no integrated model that comprehensively covers teacher and librarian collaboration. The purpose of this study is to propose a schematic integration model for LCI by comparing and analyzing various models in five dimensions (level of collaboration, information activities, collaborative approach, time scheduling, and technological integration). The main results of the integration model for LCI reflected in this study are as follows. First, in terms of the level of collaboration, TLC integration model reflected such as library-based teacher-led instruction, cross-curricular integrated curriculum. Second, in terms of information activities, LCI integration model reflected social and science subjects inquiry activities in addition to the information use process. Third, in terms of collaborative approach, LCI integration model is divided into such as lead-observation instruction and parallel station instruction. Fourth, in terms of time management, LCI integration model took into account the Korean national curriculum and scheduling methods. Fifth, in terms of technology integration, LCI integration model reflected the PICRAT model, modified from the perspective of library collaboration instruction.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.141-151
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• 2023

This study aimed to investigate the influence of learning flow, career decision-making self-efficacy, and major satisfaction on career preparation behaviors among nursing students. Data were collected by questionnaire from 15th September 2022 to 14th October 2022, from 208 nursing students at universities in provinces the G and J. And the data answered to the questionnaire were analyzed by descriptive analysis, t-test, One-way ANOVA, Pearson's correlation, and Stepwise Regression Analysis. As a result of this study, career preparation behaviors was analysed based on learning flow (r=.515, p<.001), career decision-making self-efficacy (r=.681, p<.001) and major satisfaction (r=.621, p<.001). The results of the multiple regression analysis showed that the influential factors on career preparation behaviour were career decision-making self-efficacy (𝛽=.446, p<.001), major satisfaction (𝛽=.285, p<.001), third grade (𝛽=.157, p=.001), learning flow (𝛽=.133, p=.018), and second grade (𝛽=.106, p=.038), and the explanatory power of the career preparation behavior was 57.0%. Therefore, there is a need to provide customised education through a career programme that takes into account the career path chosen by nursing students.

• Information Systems Review
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• v.23 no.1
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• pp.23-43
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• 2021

Recently, many studies have been conducted to increase the accuracy of stock price prediction by analyzing candlestick charts using artificial intelligence techniques. However, these studies failed to consider the time-series characteristics of candlestick charts and to take into account the emotional state of market participants in data learning for stock price prediction. In order to overcome these limitations, this study produced input data by combining volatility index and candlestick charts to consider the emotional state of market participants, and used the data as input for a new method proposed on the basis of combining variantion autoencoder (VAE) and attention mechanisms for considering the time-series characteristics of candlestick chart. Fifty firms were randomly selected from the S&P 500 index and their stock prices were predicted to evaluate the performance of the method compared with existing ones such as convolutional neural network (CNN) or long-short term memory (LSTM). The results indicated the method proposed in this study showed superior performance compared to the existing ones. This study implied that the accuracy of stock price prediction could be improved by considering the emotional state of market participants and the time-series characteristics of the candlestick chart.

• Journal of Industrial Convergence
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• v.22 no.5
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• pp.107-116
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• 2024

This study is a descriptive research study to determine the extent to which end-of-life care stress, death awareness, and prior decision-making attitudes of nurses at a hospice and palliative nursing institution have an impact on end-of-life care performance. The subjects of this study were 200 nurses working at a hospice and palliative nursing institution. Data collection for this study was conducted from August 9 to September 30, 2021, using two methods: written questionnaire and internet survey. The data analysis method used Pearson's correlation coefficient to analyze the relationship between the subjects' end-of-life care stress, death awareness, prior decision-making attitude, and end-of-life care performance. Hierarchical Regression was used to identify factors affecting the subject's end-of-life care performance. The results of this study showed a significant correlation between end-of-life care performance and death awareness (r=.22, p=.002), and end-of-life care performance and prior decision-making attitude (r=.20, p=.004). And prior decision-making attitude and death awareness had a significant impact on end-of-life care performance. As death awareness and prior decision-making attitudes increased, end-of-life care performance increased, and end-of-life care stress did not appear to be a statistically significant factor influencing end-of-life care performance. In order to improve hospice nurses' ability to provide end-of-life care, intervention that takes into account the influencing factors is required.

• Information Systems Review
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• v.22 no.4
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• pp.185-203
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• 2020

With the advent of 4th industrial revolution, the manufacturing industry is converging with ICT and changing into the era of smart manufacturing. In the smart factory, all machines and facilities are connected based on ICT, and thus security should be further strengthened as it is exposed to complex security threats that were not previously recognized. To reduce the risk of security incidents and successfully implement smart factories, it is necessary to identify key security factors to be applied, taking into account the characteristics of the industrial environment of smart factories utilizing ICT. In this study, we propose a 'hierarchical classification model of security factors in smart factory' that includes terminal, network, platform/service categories and analyze the importance of security factors to be applied when developing smart factories. We conducted an assessment of importance of security factors to the groups of smart factories and security experts. In this study, the relative importance of security factors of smart factory was derived by using AHP technique, and the priority among the security factors is presented. Based on the results of this research, it contributes to building the smart factory more securely and establishing information security required in the era of smart manufacturing.

• Information Systems Review
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• v.20 no.4
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• pp.79-96
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• 2018

This study started by focusing on the internalization of the technology appraisal model into the credit rating model to increase the discriminative power of the credit rating model not only for SMEs but also for all companies, reflecting the items related to the financial stability of the enterprises among the technology appraisal items. Therefore, it is aimed to verify whether the technology appraisal model can be applied to identify high-stability SMEs in advance. We classified companies into industries (manufacturing vs. non-manufacturing) and the age of company (initial vs. non-initial), and defined as a high-stability company that has achieved an average debt ratio less than 1/2 of the group for three years. The C5.0 was applied to verify the discriminant power of the model. As a result of the analysis, there is a difference in importance according to the type of industry and the age of company at the sub-item level, but in the mid-item level the R&D capability was a key variable for discriminating high-stability SMEs. In the early stage of establishment, the funding capacity (diversification of funding methods, capital structure and capital cost which taking into account profitability) is an important variable in financial stability. However, we concluded that technology development infrastructure, which enables continuous performance as the age of company increase, becomes an important variable affecting financial stability. The classification accuracy of the model according to the age of company and industry is 71~91%, and it is confirmed that it is possible to identify high-stability SMEs by using technology appraisal items.

• Information Systems Review
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• v.21 no.4
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• pp.47-68
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• 2019

As cloud services and deployment models become diverse, there are a growing number of cloud computing selection options. Therefore, financial companies need a methodology to select the appropriated cloud for each financial computing system. This study adopted the Balanced Scorecard (BSC) framework to classify factors for the introduction of cloud computing in financial companies. Using Analytic Hierarchy Process (AHP), the evaluation items are layered into the performance perspective and the cloud consideration factor and a comprehensive decision model is proposed. To verify the proposed research model, a system of financial company is divided into three: account, information, and channel system, and the result of decision making by both financial business experts and technology experts from two financial companies were collected. The result shows that some common factors are important in all systems, but most of the factors considered are very different from system to system. We expect that our methodology contributes to the spread of cloud computing adoption.

• Journal of the Korean Applied Science and Technology
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• v.40 no.6
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• pp.1498-1505
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• 2023

This study attempted to investigate the difference in service orientation according to the individual characteristics of hair salon workers, and to identify the internal marketing factors of hair salon that influence service orientation. Questionnaires for empirical research were collected from hair salon workers in Gyeongnam, and the results of analyzing the collected questionnaires through IBM SPSS Statistics 26 are as follows. First, as a result of analyzing the difference in service orientation according to the individual characteristics of hair salon workers, the '40s or older' group and the 'working period of 10 years or longer' group showed statistically higher service orientation than other groups. Second, as a result of analyzing the causal relationship between internal marketing and service orientation, it was found that welfare, compensation system, education and training of internal marketings had the statistical effect on service orientation, and in particular, the compensation system had the strongest effect on service orientation. Therefore, service orientation for customers should be improved through internal marketing activities that take into account the individual characteristics of hair salon workers. The improvement of service orientation means the customer's intention to reuse, suggesting that ultimately the management performance of hair salon companies can be further improved.

• The Journal of Korean Society for School & Community Health Education
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• v.25 no.1
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• pp.1-15
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• 2024

Objectives: This study aims to examine the general characteristics and physical activity characteristics of young and middle-aged individuals with hypertension, with the goal of identifying key influencing factors and providing public health policy recommendations. Methods: Participants in this study used data from the 2021 Community Health Survey. The study participants include 5,511 individuals diagnosed with hypertension in the young and middle-aged group (aged 19 to 49). The collected data were analyzed using SPSS 26.0. Results: Model 1 is the influencing factors of young and middle-aged hypertensive patients according to general characteristics. The explanatory power is R²= .065. The influencing factors are as follows. Economic activity (𝛽= -.219, p<.001), breakfast per week (𝛽= .117, p<.001), gender (𝛽= .090, p<.001), subjective health status (𝛽= .073, p<.001), and education level (𝛽= .069, p<.001). Model 2 is the influencing factors of young and middle-aged hypertensive patients, including physical activity characteristics. The explanatory power is R²= .076. The influencing factors are as follows. Strength exercises (𝛽= -4.791, p<.001), the walking activity (𝛽= -2.694, p<.01), and the high-intensity physical activity (𝛽= -2.629, p<.01). Conclusion: The active management of young and middle-aged hypertension is essential to prevent progression to serious disease. To prevent hypertension in young and middle-aged people, health education is needed to develop and utilize health promotion programs that take into account general characteristics and physical activity characteristics.

• Journal of Christian Education in Korea
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• v.77
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• pp.225-249
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• 2024

Research Purpose : Based on artificial intelligence, this study considers learners' characteristics, learning content, and individual learning, and analyzes the collected learning data to develop a model that supports customized learning for individual learners. Research content and method : In order to achieve the research purpose, the literature was analyzed to investigate the structure of customized learning support, learning data analysis, and learning activities, and based on the investigated data, the area and detailed components of the customized learning support model were derived. did. A draft model was constructed through literature analysis, and the first expert Delphi survey was conducted on the draft model with five experts. The model was revised by reflecting the results of the first Delphi, and the validity of the revised model was verified through the second expert Delphi. The model was elaborated through expert Delphi, and the final model was constructed through this. Conclusion and Recommendation : Through research, customized learning support area, class management system area, and learning analysis data area were formed, and detailed elements were derived for each area. The results of this study provide basic data that can be used as a reference for constructing a customized learning support system based on artificial intelligence, taking into account the university's class environment.