• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.591-601
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• 2021

This study examined the effect of branding capability on business performance moderated by learning capability. This study was conducted with small- and medium-sized enterprises (SMEs) of coffee cafes in the ex-Besuki region, East Java, Indonesia, covering four regencies located around coffee-producing areas with geographical indication (GI) certification. 150 managers of coffee cafe were sampled using the census technique. Data were collected by questionnaires distributed to the coffee cafe managers. The data were then analyzed by using simple regression analysis, Moderation Regression Analysis (MRA) and Moderated MultiGroup Analysis (MMA). The results showed that learning capability positively and significantly affect business performance, and learning capability moderated/enhanced the effect of branding capability on business performance. The findings of this study suggest that branding capability and learning capability play a crucial role in the performance of coffee cafe business especially in the dynamic environment. Coffee cafe managers need to take concrete steps to improve their branding capability and learning capability and they also need to improve their ability to interact with their environment and be committed in managing the coffee cafe. Therefore, it is imperative that the role of branding capability and learning capability be optimized in order to improve the business performance of the coffee cafe.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.77-82
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• 2022

The research study was conducted for development of the advanced image analysis service system based on deep learning. CNN(Convolutional Neural Network) is built in this system to extract learning data collected from Google and Instagram. The service gets a place image of Jeju as an input and provides relevant location information of it based on its own learning data. Accuracy improvement plans are applied throughout this study. In conclusion, the implemented system shows about 79.2 of prediction accuracy. When the system has plenty of learning data, it is expected to predict various places more accurately.

• International Journal of Contents
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• v.18 no.2
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• pp.58-67
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• 2022

As the Internet rapidly evolves, online learning has emerged as the third largest scenario in the field of education. Online education, different from the two traditional learning scenarios of the school and society, is characterized with broader learning types and higher freedom. In today's post-pandemic era, art education, which relies on face-to-face teaching, is of particular significance to expand online education methods. Based on the UTAUT model, this paper posits seven hypotheses about the willingness to continue learning in online art education. After collecting valid data through a questionnaire, a detailed empirical analysis was conducted via SPSS and AMOS. The results of empirical analysis show that less than half of the respondents had experienced the online art education, mirroring that this is a market worth developing. Based on the findings, learning habit does not significantly impact art learners' willingness to continue learning online. This result and other verified hypotheses are detailed in the discussion part of this paper. This study proves that UTAUT can better explain user behavior than the traditional information system model prior to the improvement, and also has strong explanatory power in the field of art education. The conclusion also posits some operational suggestions from the perspective of practitioners in this field, thereby providing a theoretical basis for art education practitioners.

• Computers and Concrete
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• v.32 no.3
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• pp.313-326
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• 2023

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.

• Journal of Distribution Science
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• v.21 no.4
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• pp.57-67
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• 2023

Purpose: This study aims to measuring the employee performance distributions of company in using relationship analysis between motivation, organization learning, compensation, and Organizational commitment. Research design and methodology: The study was conducted on 102 employees as a sample. Data were analyzed using Path Analysis in Structural Equation Modeling (SEM) with PLS. Results: the research result has shown that motivation and compensation have a positive significant effect on organizational commitment. While organizational learning has negative and insignificant effect on organizational commitment. Furthermore, motivation, organizational learning and motivation have no significant effect on employee performance distribution and organizational commitment has a positive significant effect on employee performance distribution. Results for mediating effect has obtained where organizational commitment mediates the effect of motivation and compensation on employee performance distribution, but cannot mediate the effect of organizational learning on employee performance distribution. Conclusion: Organizational commitment in this study can make employees feel comfortable and attached to the company so that employees can perform well to achieve company goals. Motivation and compensation are driving factors in improving employee performance distribution and will achieved if employees have good organizational commitment. In this study, organizational learning is not an important factor in improving employee performance distribution.

• Educational Technology International
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• v.11 no.1
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• pp.47-68
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• 2010

The purpose of this study was to analyze majors trends and issues of e-Learning curriculum for human resource development in the corporate context. The e-Learning curriculum was chosen as the subject of research consists of 2,710 lectures that were given from 2007 to July 2009 for the recent three years by providing at Ministry of Labor and Korea Research Institute for Vocational Education & Training. In order to investigate trends and issues, it was employed theme analysis which is one of the types of document analysis that approach a qualitative research methodology. As a result of this research, 7 major trends and issues in e-Learning curriculum for HRD in the field of corporate education were drawn; ① Strengthening expertise through learning of job related professional knowledge, ② Cultivation of common & essential knowledge for a job to increase work performance efficiency ③ Organizational management strategy for improving performance, ④ Organizational management and operational strategy for actively responding to environmental changes, ⑤ Leadership as a strategy for cultivating core personnel and field-centered practical leadership. ⑥ Creating a happy workplace through the work-life balance, ⑦ Strengthening global communication skill. Based on these analysis, practicals and theoretical implications of e-Learning professionals and HR researchers for HRD were suggested.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.242-248
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• 2024

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

• The Journal of Korean Association of Computer Education
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• v.20 no.6
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• pp.1-15
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• 2017

The purpose of this study is to design a LMS(Learning Management System) dashboard for instructor feedback support based on learning analytics and to apply a LMS dashboard incorporating such taxonomy which allows an instructor to give a student personalized feedback according to the class content and a student's traits. In the dashboard design phase, usable instructional data were selected from LMS based on feedback taxonomy in terms of learning analytics. Two validity tests were conducted with 8 instructional technologists over 8 years of experience, and were revised accordingly. The final dashboard screen has three parts: A comprehensive analysis screen to provide appropriate feedback based on instructor feedback taxonomy analysis, a summary screen for learner analysis, and a recommended feedback guide screen. Detailed analysis information are provided through other dashboards that are displayed in eight screens: login analysis, learning information confirmation analysis, teaching materials learning analysis, assignment/tests, and posts analysis. All of these dashboards were represented by analysis information and data based on learner analytics through visualization methods including graphs and tables. The implications of educational utilization of the dashboard for instructor feedback support based on learning analytics and the future researches were suggested based on these results.

• The Journal of Korean Association of Computer Education
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• v.15 no.5
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• pp.11-22
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• 2012

This study aimed to analyze applying TAM model the process that flow factors such as 'harmony of challenge and technology' exert effects on learners' attitudes of keeping learning in STEAM class employing robots. For the study, the 'Energy and Tools' chapter of the science textbook for the 6th grade's second semester was re-arranged, and applied for 189 students, and among them, only the 174 usable data were used for the analysis. As a result of analysis, students' learning immersion factor(factor of harmony of challenge and technology) had deeper effects on the factor of ease of learning than usefulness of learning and this in turn, had an effect on their intention to keep learning ultimately through the factor of value of learning as the study found. As a result of research, it was found that for indications identified, in order to use robots in STEAM class, for the students' intention to keep learning, it's essential for learners to have proper and active attitudes towards learning and basic knowledge of robots, and aspects of values should be considered that based on this, robot can assist in learning and affect results of learning in STEAM class. On the other hand, the factors of ease of learning and the combination of the challenge and technology do not gives direct (+) effect on the intention to continue learning and the value for learning, respectively. However, each of the two factor has indirect influence on each of the dependent variable within the significant range, which is the reason the author includes the result of the analysis.

• Journal of Engineering Education Research
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• v.11 no.3
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• pp.12-23
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• 2008

The purpose of this study is to explore teaching and learning strategies through analyzing cases of foreign engineering education. With the analysis criteria composed of engineering education model, teaching and learning method, evaluation strategy, and technology supporting strategy, 10 foreign colleges of engineering in 5 countries were examined and analyzed. Teaching and learning strategies deduced from analysis state as follows. First of all, it need to develop engineering education models that reform should be made in systematic approach to teaching and learning, workplaces and laboratories, evaluation, technology support, etc. Secondly, the strategy for teaching and learning recommends supporting student directed learning, active learning participation, and collaboration learning by inductive learning strategies such as problem based learning, inquiry learning, project based learning, studio based learning, and blended learning. Thirdly, the evaluation strategy suggests that evaluation should be made to reflect students' learning and facilitate continuous learning based current learning results while it is necessary to build up a whole evaluation system. Finally, it is the educational technology approach for systematic engineering education that is required considering that many foreign colleges of engineering have reformed engineering education through technology supporting systems and are maximizing research and education in connection with other universities. This study is expected to contribute as preliminary data in developing further teaching and learning models and strategies for nurturing engineering students.