• Journal of Digital Convergence
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• v.13 no.4
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• pp.147-161
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• 2015

The purpose of study was to test the effects of CEO' transformational leadership and learning organization on innovative behavior in the Knowledge-based Service Organization showing the characteristics of convergence service and the moderating role of learning organization between transformational leadership and innovative behavior. For this study, the data were collected from 348 Knowledge-based Service industrial employees in metropolitan area by using structured questionnaires. Collected data were analyzed by hierarchical regression technique. The results showed that both of CEO' transformational leadership and seven learning organization building factors had a positive effect on employees' innovative behavior. And also found out the only four out of the seven learning organization building factors, namely 'Create continuous learning opportunities', 'Promote inquiry and dialogue', 'Encourage collaboration and team learning', 'Provide strategic leadership for learning' had the moderate roles between CEO's transformational leadership and employees' innovative behavior. The theoretical and practical implications of the findings were discussed and the directions for future research were presented.

• Journal of Korean society of Dental Hygiene
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• v.22 no.5
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• pp.451-459
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• 2022

Objectives: This study aimed to provide useful basic data for improving the quality of problem-based learning (PBL) to improve integrated thinking and problem-solving skills in clinical dental hygiene through in-depth exploration of the experiences of dental hygiene students trained in PBL modules. Methods: A total of nine participants were selected based on the grade distribution of clinical dental hygiene. Three participants each were from the upper, middle, and lower groups. A focus group interview (FGI) was conducted using a developed questionnaire. All contents of the recorded FGI were used to draw the main results while maintaining the core contents Results: The themes derived through the FGI were confirmed by 'advantages of PBL', 'competencies developed through PBL', 'teamwork experienced in PBL', 'required competencies for PBL team activities', 'differences in contribution among team members', 'satisfaction with PBL', 'improvements to PBL', and 'trial and error experienced in PBL'. Conclusions: The PBL was a useful for improving the integrated thinking and problem-solving skills of dental hygiene students. Moreover, this study provides useful basic data for the qualitative improvement of the PBL.

• Journal of The Korean Association of Information Education
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• v.24 no.4
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• pp.401-412
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• 2020

Team-based programming methods are widely applied to solve various difficulties that pre-service teachers experience in the programming lessons. To prepare effective collaboration strategies necessary for them, it is necessary to analyze various collaborative factors that affect learners' programming competencies. Therefore, in this article, a questionnaire survey was conducted by dividing learners' collaboration factors into individual and team competencies, and the relationship between learners' programming competencies was analyzed. As a result of the verification, the program design competency showed significant results in all elements of the learner's personal competency, team techniques such as data sharing skills necessary for collaboration, and team collaboration. It was analyzed that an individual's understanding of learning and team collaboration influenced the program implementation competency. In addition, the group with relatively high team technique showed significant differences in programming competence, interest, and satisfaction. Accordingly, by linking meaningful factors related to individual and team competencies according to the programming process, a collaborative strategy practically necessary for pre-service teachers was suggested.

• Asia pacific journal of information systems
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• v.26 no.1
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• pp.66-79
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• 2016

The availability of detailed data on customers' online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the 'entropy' concept from information theory, while 'random forests' method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers' information search behaviors differ significantly across product categories.

• Human Ecology Research
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• v.60 no.2
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• pp.187-209
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• 2022

The objective of this study was to conduct a 'Theory of Home Economics Education' class using online problem-based learning(PBL) for prospective home economics(HE) teachers. The aim was to enable teachers to analyze the learning experience in the classroom, and to prepare operational strategies for online PBL on this basis. In order to achieve this, online PBL was applied to 31 students participating in the 'Theory of Home Economics Education' at the Department of HE in a university in Seoul, and the results were collected from the learning process. This also involved a reflective journal, a survey on the learning experience and the impacts was conducted. Moreover, analysis was undertaken on the learning activities, learning difficulties, and improvements. The main research results are as follows. Firstly, students accessed Webex, an online video conferencing program, and performed two PBL tasks: 'Making Home Economics Promotion Materials' and 'Presenting Teaching Strategies to Improve Learner's Immersion in Online Classes'. Secondly, learners established their own identity of HE learned about the HE class plans themselves. They also encountered realistic experience as HE teachers and learned communication and collaboration skills. Furthermore, they acquired creative problem-solving and self-directed learning ability, community consciousness, as well as the attitude of consideration and respect. Thirdly, students lacked knowledge of learning content and encountered difficulty in solving data research, analysis processes, and unstructured problems. They were affected by a lack of time and encountered problem in communicating with other team members in an online environment. As an improvement in online class operation, it was considrered necessary to reduce the learning burden by securing time and reducing the number of assignments, as well as to explain active interaction with instructors and PBL.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.2
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• pp.31-40
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• 2017

This study validate effectiveness of Online Entrepreneurship Education based on PBL(Problem-based Learning) and flipped learning. This study reveals online education of entrepreneurship based on PBL and flipped learning method has positive effect on personal entrepreneurship, will to be an entrepreneur, and problem-solving skills. First, the results show that entrepreneurship education based on PBL and flipped learning can improve entrepreneurship more than a previous learning method. Second, PBL and flipped learning based online education affects will to be an entrepreneur in positive way. Experimental group who experienced problem solving activity and flipped learning has more will to be entrepreneur than control group who takes previous learning method. Lastly, PBL and flipped learning method based entrepreneurship education also has positive effect on personal problem-solving techniques. This results show that online entrepreneurship education based on PBL and flipped learning has positive impacts on entrepreneurship, will to be an entrepreneur, and improving problem-solving skills significantly.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Engineering Education Research
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• v.16 no.2
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• pp.78-85
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• 2013

An 'Introduction to Creative Engineering Design' course at College of Engineering at Chungnam National University is required for all freshmen. The objective of this course is to educate the freshmen with basic engineering design concepts and experiences in creative problem-solving approaches. It provides the students learning opportunities in solving engineering design problems through team efforts and creative approaches. Thus, this course emphasizes creative ideas and thinking, engineering design experiences to students over the course. This study presents the syllabus, the examples of PBL (problem based learning)-related activities as a team, and the results of the course evaluation and outcomes. Based on the results of this study, we can conclude that overall this course using PBL method had significant positive effects on the course outcomes and the creativity of the engineering freshmen in the department of environmental engineering at Chungnam National University. However, there are still efforts to be needed to improve the PBL-related activities in the course, including students' workload, financial supports, and team work.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.37-53
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• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.6
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• pp.117-128
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• 2014

This is the case study of SATREC INITIATIVE company which is the unique domestic production of commercial satellites. We examined the path and pattern for accumulation of technological capability and technology learning process. This case study show that the process of technological innovation and their influencing factors. First, the technological learning of the satellite industry follows the stage of technological acquisition, absorption, improvement and is embodied by the technological capability. Second, accumulated technological capability of the satellite industry influences the technology innovation. Third, the top management team(TMT) affects the technological learning and technological capability. Fourth, TMT has a moderating role between the technological capability and the performance of technological innovation. Finally, technological innovations in the small and venture business would be the source of technological capability and technological learning. The implications of this study are as follows. TMT has the very important role for the technological innovation and affect the technology development and the production. Also technology-based companies must gain a competitiveness advantage through technological learning and technological innovations for sustainable growth.