• Hwankyungkyoyuk
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• v.14 no.2
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• pp.95-105
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• 2001

Out-door learning activity in environmental education has been emphasized as an effective method in environmental education since the aims of environmental education emphasize students'value, attitude, actions as well as knowledge. In order to implement successfully out-door learning activity in environmental education classrooms, teachers'perceptions to environmental problems and experiences at fields are essential. An environmental education network among the metropolitan city and provincial office of education, nongovernmental organization of environmental movement and education and university was established and a teachers'in-service training program of out-door learning centered environmental education was implemented. The program was developed in order to 1) connect environmental education with the regional environmental situations, 2) provide teachers with opportunities to participate in an out-door learning program, and 3) train teachers to be environmental education leaders of out-door learning. For evaluation of the program, responses of participants to questionnaire were analyzed. Most of teachers responded that their perception of environment was changed positively after the participation in the program. This study suggested that a future planning of a teachers'in-service training program of out-door learning centered environmental education should be developed in considerations of arranging enough hours for out-door learning at regional environmental sites, applying performance assessment, providing teachers with multiple opportunities with programs in different levels including enriched programs, and establishing an environmental education network among nongovernmental organization of environment movement and education, university, and local offices and department of education.

• Korea Journal of Hospital Management
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• v.18 no.4
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• pp.54-77
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• 2013

This paper is a comparison study about leadership training transfer factors between physicians working in large scale hospitals and managers working in firms. To fulfill this purpose, this study conducted a regression analysis on 101 managers and 59 physicians who had attended similar leadership training programs more than 16 hours recently in order to identify the differences on the learning transfer factors. 6 factors such as Learner readiness, Performance self-efficacy, (so far as Trainee Characteristics group), Organization Culture, Supervisor's tangible incentives and Supervisor's intangible support, (so far as Work environment group), Content Validity & Transfer Design (so far Training Design group) were used as independent variables while the personal Managerial Capability Increase and Leadership Capability Increase were used as dependent variables. And also we used 5 factors as control variables ; Job style (Manager or Physician), Age, Gender, Working years and Organization size. Here are the summary of major findings ; first, there were statistically significant differences between the learning transfer factors in leadership training programs for managers and those of physicians. Second, there were also statistically significant differences among trainees' working years and their organization size factors while age and gender do not affect the learning transfer factors. Third, for the physician's leadership training the practitioners should focus on two factors ; Organization Culture and Learner readiness.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.831-838
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• 2022

Machine Learning is a process of using computer algorithms to extract information from raw data to solve complex problems in a data-rich environment. It has been used in the construction industry by both academics and practitioners for multiple applications to improve the construction process. The Construction Industry Institute, a leading construction research organization has twenty-five years of experience in benchmarking capital projects in the industry. The organization is at an advantage to develop useful machine learning applications because it possesses enormous real construction data. Its benchmarking programs have been actively used by owner and contractor companies today to assess their capital projects' performance. A credible benchmarking program requires statistically valid data without subjective interference in the program administration. In developing the next-generation benchmarking program, the Data Warehouse, the organization aims to use machine learning algorithms to minimize human effort and to enable rapid data ingestion from diverse sources with data validity and reliability. This research effort uses a focus group comprised of practitioners from the construction industry and data scientists from a variety of disciplines. The group collaborated to identify the machine learning requirements and potential applications in the program. Technical and domain experts worked to select appropriate algorithms to support the business objectives. This paper presents initial steps in a chain of what is expected to be numerous learning algorithms to support high-performance computing, a fully automated performance benchmarking system.

• Research in Mathematical Education
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• v.9 no.1 s.21
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• pp.69-96
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• 2005

Motivation for learning is important for positive learning outcomes as well as for measured achievement levels. When students come to our classes, they bring with them learning histories in which we as individual teachers, most likely, did not have an input. Our students do not only bring with them different levels of prerequisite leanings but also different levels of affect for what they will be learning. If we leave their final learning at the mercy of these entry characteristics, a test given the first day before the course will have almost isomorphic results with their achievement levels on the last day. The ones who had 'it' on the first day will be the ones who in the future will also have 'it', not too different from what the present situation is all over the world. These circumstances will tend to be the case ad infinitum, unless of course, we want to change the situation. This research clearly shows that effective instructional methodologies coupled with cooperative peer interactions not only have an impact on achievement but also on positive attitudes toward one's learning.

• Journal of Information Science Theory and Practice
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• v.1 no.4
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• pp.12-37
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• 2013

Organizational climate and organization culture can be some of the leading factors in hindering knowledge sharing within the organization. It is generally accepted that successful knowledge management practice, including knowledge sharing, comes as a result of a conducive and knowledge sharing friendly environment. Organizations that promote and reward collective work generate a trustful and a more collaborative learning culture. The perception of fairness in an organization has been considered an important indicator of employee behavior, attitude, and motivation. This study investigates organizational justice perception and its impact on knowledge sharing practices among forensic experts in the Turkish National Police. The study findings revealed that senior officers, who are experts in the field, have the strongest organizational justice perception. Meanwhile, noncommissioned officers or technicians bear positive but comparatively weaker feelings about the existence of justice within the organization. The study argues that those who satisfy their career expectations tend to have a higher organizational justice perception.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.81-90
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• 2012

Open Source Softwares(OSS) are increasingly deployed in several domains, many educational organization have tried to deploy the OSS LMS(Learning Management System). For deploying OSS LMS, customization for specific environment is critical requirement. In this paper, we implemented the supporting environment to integrate the Moodle and school information system for user and course management. It is the most important customization requirement for introducing the Moodle to school. Also, we implemented supporting environment for the most important requirement so that we verified the usability of our guideline. We applied our environment to verify the efficiency of customization process.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.387-393
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• 2022

The article reflects short-term perspectives on the use of information and communication technologies in the training of teachers for higher education. Education is characterized by conservatism, so aspects of systematic development of the industry are relevant to this cluster of social activity. Therefore, forecasting the introduction of innovative elements of ICT training is in demand for the educational environment. Forecasting educational trends are most relevant exactly in the issues of training future teachers of higher education because these specialists are actually the first to implement the acquired professional skills in pedagogical activities. The article aims to consider the existing potential of ICT-based learning, its implementation in the coming years, and promising innovative educational elements that may become relevant for the educational space in the future. The tasks of scientific exploration are to show the optimal formats of synergy between traditional and innovative models of learning. Based on already existing experience, extrapolation of conditions of educational process organization with modeling realities of using information and communication technologies in various learning dimensions should be carried out. Educational trends for the next 3 years are a rather tentative forecast because, as demonstrated by the events associated with the COVID-19 pandemic, the socio-cultural space is very changeable. Consequently, the dynamism of the educational environment dictates the need for a value-based awareness of the information society and the practical use of technological advances. Thus, information and communication technologies are a manifestation of innovative educational strategies of today and become an important component along with traditional aspects of educational process organization. Future higher education teachers should develop a training strategy taking into account the expediency of the ICT component.

• Journal of the Korean Institute of Educational Facilities
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• v.18 no.5
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• pp.43-50
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• 2011

There are emerging issues to update the educational environment for schools in terms of information and communication technology in order to provide customized programs to students as well as all participants relating to learning and teaching. The past year has been turbulent as the education facilities has changed and new procurement processes such as BTL have emerged. In this study, the feasibility analysis of ICT for the public educational environment is to analyse the current primary schools by means of collecting parent's opinion. In the web-site questionnaires, it was designed with 70 items such as teaching method, class organization, aptitude drill and educational community. As results, the statistical analysis is to propose the list of priority and orientation covering social agenda in the issue of ICT for education, the benefits schools can achieve by smart environment is to have the advanced learning services and solutions that represents parental engagement with identical local aims of interactive interface between their students and qualified teachers at a school. Both the national curriculum as well as the after-school program initiatives from the ministry of education, science and technology may reduce negative effects of private education so that the program has to be carefully developed for balanced education society revitalizing mutual communication within regional learning participants such as students, teachers and educational experts.

• 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.

• Proceedings of the Korean Society for Environmental Edudation Conference
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• 2001.11a
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• pp.47-49
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• 2001