• Journal of Korean Home Economics Education Association
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• v.32 no.4
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• pp.19-30
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• 2020

Maker education started on the basis of the maker movement in which makers gathered in makerspace share their activities and experiences, and the educational value pursued in maker education is based on the constructivist paradigm. The purpose of this study is to present maker education components to be used in school education, focus on the characteristics and educational values of maker education, and explore ways to use them. To this end, this study explored the theoretical grounds to re-conceptualize maker education, drew statements based on in-depth interview data of teachers conducting maker education classes, and reviewed its validity through experts. Based on these statements, by deriving the components for the use of maker education, the direction of maker education in school education was set, and an example framework that could be used in subject class and creative experiential learning was proposed. Research shows that in maker education, makers cooperate to carry out activities, share ideas with others and try to improve them, and include self-direction such as learning, tinkering, design thinking, sharing and reflection. can see. In addition, maker education emphasizes experiential learning that can solve real problems that students face, rather than confining specific activities to student choices as needed. It emphasizes the learner's course of action rather than the outcome of the activity, tolerates the learner's failure, and emphasizes the role of the teacher as a facilitator to promote re-challenge. In the future, it can be used in various ways in each subject (curriculum expert, teaching/learning expert, elementary and middle school teachers, parents, local educators, etc.) and school activities, and it will contribute to setting future research directions as a basic research for school maker education.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.53-61
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• 2019

The risk of project failure has increased recently as ERP systems have been transformed into Web environments and task complexity has increased. Although low-code platform development tools are being used as a way to solve this problem, limitations exist as they are centered on UI. To overcome this, back-end development tools are required that can be developed quickly and easily, not only from the front development but also from a variety of development sources produced from the ERP development process, including back-end business services. In addition, the development tools included within existing ERP products require a lot of learning time from the perspective of beginner and intermediate developers due to high entry barriers. To address these shortcomings, this paper seeks to study ways to overcome the limitations of existing development tools within the ERP by providing customized development tool functions by enhancing the usability of ERP development tools suitable for each developer's skills and roles based on the requirements required by ERP development tools, such as reducing the time required for querying, automatic binding of data for testing for service-based units, and checking of source code quality.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.9
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• pp.263-272
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• 2023

Industrial Internet of Things (IIoT) is an important factor in increasing production efficiency in industrial sectors, along with data collection, exchange and analysis through large-scale connectivity. However, as traffic increases explosively due to the recent spread of IIoT, an allocation method that can efficiently process traffic is required. In this thesis, I propose a two-stage task offloading decision method to increase successful task throughput in an IIoT environment. In addition, I consider a hybrid offloading system that can offload compute-intensive tasks to a mobile edge computing server via a cellular link or to a nearby IIoT device via a Device to Device (D2D) link. The first stage is to design an incentive mechanism to prevent devices participating in task offloading from acting selfishly and giving difficulties in improving task throughput. Among the mechanism design, McAfee's mechanism is used to control the selfish behavior of the devices that process the task and to increase the overall system throughput. After that, in stage 2, I propose a multi-armed bandit (MAB)-based task offloading decision method in a non-stationary environment by considering the irregular movement of the IIoT device. Experimental results show that the proposed method can obtain better performance in terms of overall system throughput, communication failure rate and regret compared to other existing methods.

• Korean Journal of School Psychology
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• v.16 no.3
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• pp.231-263
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• 2019

The purpose of this study was to verify the effects of growth orientation, life satisfaction, perceived stress, positive psychological capital and interpersonal relationships in the group coaching program for the promotion of growth orientation for university students based on the model of the social-cognitive approach to motivation. The program consisted of eight topics: growth orientation, growth mindset and brain plasticity, self-directed goal setting, talent which is a product of ongoing effort, failure attitude and perspective change, positive emotion, thinking and behavior, value of growth orientation and self-coaching, respectively. The program comprised a total of eight sessions, 120 minutes each, and the final program was completed through a preliminary experiment with three university students. In order to verify the effectiveness of the program, 48 university students were divided into 16 in the experimental group, 16 in the comparative group, and 16 in the control group. The experimental group participated in the group coaching program to enhance the growth orientation based on the model of the social-cognitive approach to motivation developed in this study, the comparative group participated in a learning goal orientation improvement program based on an incremental implicit theory, and the control group did not carry out any program. Three groups were tested in pre, post, follow-up1(after 1 month) and follow-up2(after 3 months) in order to growth orientation, life satisfaction, perceived stress, positive psychological capital and interpersonal relationships. We performed analysis to confirm the homogeneity to the data of the three groups and to verify the interaction effects between times and groups. As a result, it was confirmed that the group coaching program to promote growth orientation, life satisfaction, perceived stress, positive psychological capital and interpersonal relationships had statistically significant effect and was more effective than the comparative program due to the larger effective size. Also, we confirmed that the coaching effect was sustained after the program was finished and more effectively maintained than the comparative program. Based on the results of this study, this study has academic implications because it verify the effectiveness of the group coaching for the promotion of the growth orientation by scient ic method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.5
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• pp.29-43
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• 2016

The Lean Startup, a methodology for minimizing failure rate of startups, has been receiving attention since its publication in 2011. Although it has been receiving enormous attention as an effective methodology of startups' growth and the emergence of unicorn companies, it is undeniable that the theoretical research and cases on this topic have not been fully accumulated in Korea. Progress of management theory has been made when combining the theory and case studies. In this paper, we thus excavated the 'Cardoc' case, which has applied the lean startup concept to the entire process of service and customer development from the inception of its product design. The following are the findings of the case. First, for the successful application of lean startup, it is essential that all team members to understand the lean startup concept and are willing to apply it thoroughly to the business management. Second, the prompt launching of MVP(Minimum Viable Product) is more important than table discussion. Third, it is crucial to select the appropriate key metrics and analytic tools for effective learning. Fourth, startup must scale up promptly as soon as it verifies the product-market fit through the BML(Build-Measure-Learn) iteration cycle. Fifth, all new business expansion should be lean. Cardoc is currently testing new MVPs in order to move onto the next scale-up process with huge investments in newly added segments. This study is meaningful in that it elaborates the representative case of a Korean startup that has applied the lean startup strategy under the circumstance of insufficient discussion of Korean startup cases in comparison with growing attention both in concept development and case accumulation abroad. We hope that this paper can be a stepping stone for future relevant research on the implementation of lean startup methodology in Korea.

• 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.