• Journal of the Korean Chemical Society
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• v.46 no.1
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• pp.90-96
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• 2002

The STS contents, emphasized in the 6th curriculum, in the chemistry textbooks(II) were analyzed. The STS contents in textbooks showed average value of 2.7%. The chapter of ‘chemical bond and compound' were included 3.8% of STS contents. And the chapter of ‘atomic structure and periodic table', ‘state of material and solution', ‘science of material', and ‘chemical reaction' contained 3.2%, 2.2%, 1.9%, and 1.9% of STS contents, respectively. When the STS contents were analyzed by STS topics of Piel, the results are as follows; 33.7% on effect of technological developments, 27.5% on environmental quality and utilization of natural source, 19.6% on human engineering, 13.8% on energy, and 5.4% on sociality of science. However, there were no topics on population, space research and national defense. When the STS contents were analyzed by student activities of SATIS, most of the activities were research and case study. There were few field activities of practical investigation, problem solving and decision making, research design and stimulation. There were no activities of role play.

• Journal of the Korean Chemical Society
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• v.45 no.3
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• pp.256-263
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• 2001

The STS contents, emphasized in the 6th curriculum, in the chemistry parts of general science textbooks were analyzed. The STS contents of textbooks showed average of 24.4%. The chapter in "modern science and technology" were included 45.5% in STS contents, 38.7% in "environment", 29.1% in energy, and 14.0% in "materials". When the STS contents were analyzed by STS topics of Piel, the results are as follows; 38.3% on environmental quality and utilization of natural source, 29.6% on effect of technological developments, 7.9% on energy, and 0.6% on human engineering. However, there were no topics on population, space research and national defense. When the STS contents were analyzed by student activities of SATIS, most of the activities were research and case study. There were few field activities of role play, problem solving and decision making, and research design.

• 대한공업교육학회지
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• v.31 no.2
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• pp.233-256
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• 2006

The purpose of this study was to analyse between foreign curriculum and our high school industrial technology subject-matter curriculum, to review trend and stream of curriculum revision, and purpose and content system of subject-matter. This study was conducted through reviewing literature; research reference, journal, book, and Web materials. in this study, comparative target country was restricted to Japan, U. S. A., U. K., and N. Z., Australia that administer to similar subject with our industrial technology subject-matter. The major finding of this study were as follows: 1. A similar subject-matter with our industrial technology subject0matter was Japan' 'foundation of industrial technology' and 'project research', U. S. A.' 'technology' and etc, U. K.' 'design and technology', and N. Z.' 'technology', 'New South Wales in Australia' design and technology'. 2. The result of analysis to purpose and strength of subject-matter, our' industrial technology subject-matter was oriented to knowledge, understanding and career search in industrial area. but, the other was emphasized technological problem solving by process-based method with thinking and action. 3. In the curriculum content, our country was treat to content area of a broad industrial world. on the other hand, Japan; relationship between human and technology, environment, process technology and product technology, project research. U. S. A.; technology content standards by knowledge, process and context, U. K., N. Z., and Australia were focused 'design process'. Based on above results, the recommendation can be established as follows: 1. A study on the implementation of industrial technology curriculum. 2. A study on the perception and need assessment of expert and stakeholder about purpose and content system. of industrial technology subject-matter.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.690-698
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• 2023

Information sharing and decision making between airport stakeholders became possible after the introduction of airport cooperative making system (A-CDM). This also resulted in optimizing aircraft handling time and increased the efficiency of aircraft operations. Technological advances have recently led to the development of urban air mobility (UAM) which is a small aircraft taking off and landing vertically. It is emerging as a new air transportation system in the future due to its advantage of saving time and solving congestion problem in the urban area. This study aims to suggest how vertiport cooperative decision making system (V-CDM) should be managed for efficient operation of UAM. By establishing procedure for decision making system based on Vertiport ecosystem of UAM. By establishing procedure for decision making system based on Vertiport ecosystem and UAM aircraft, unnecessary flight delays or cancellations can be minimized and efficiency of UAM operation will be improved as well.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.41-49
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• 2024

The advancement of artificial intelligence on a global scale is significantly transforming life. In the field of education, there is a strong emphasis on actively utilizing AI and fostering creatively integrated talents with diverse knowledge. In alignment with this trend, there is a paradigm shift in AI education across primary, middle, high school, as well as university and graduate education. Leading AI schools and specialized high schools are dedicated to enhancing students' AI capabilities, while universities integrate AI into software courses or establish new AI departments to nurture talent. In AI-integrated education graduate programs, national efforts are underway to educate instructors from various disciplines on applying AI technology to the curriculum. In this context, specialized high schools are also restructuring their departments to cultivate technological talent in AI, tailored to students' characteristics and career paths. While the current education focuses primarily on the fundamental concepts and technologies of AI, there is a need to address the aspect of developing practical problem-solving skills. Therefore, this research aims to compare and analyze essential educational courses in AI-leading schools, AI-integrated high schools, AI high schools, university AI departments, and AI-integrated education graduate programs. The goal is to propose the necessary educational courses for AI education in specialized high schools, with the expectation that a more advanced curriculum in AI education can be established in specialized high schools through this effort.

• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.1089-1121
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• 2017

'Technological convergence' is the recent innovation trend which facilitates to solve social crux as well as to generate new industries. Korean government research institutes (GRIs) have taken a pivotal role for economic growth which capitalized on technology-oriented strategies. Recently, the policy interests on the transition of their role and mission towards multidisciplinary research organization is increasingly shed lights. This study regards the collaborative activities as one of the key success factors in the multidisciplinary research. In this sense, this study sets research purposes as follows: First, we intend to define a concept and to confine a scope of multidisciplinary research from the view point of R&D purposes and problem-solving process. Second, we categorize the collaboration and the relevant performances which reflect the characteristics of the multidisciplinary research. Third, we analyze the characteristics of collaborative activities and the effects of strength on the research performances. To this end, this study conducted a survey of 104 research project directors, which have experienced at least one of two types of multidisciplinary research projects through National R&D project or NST (National Research Council of Science & Technology) convergence research project. Then, we conducted regression analysis by utilizing the survey results in order to verify the relation between the collaborative activities and the performances. As results of analyses, first, the diversification of collaboration partners was a salient factor in the process of knowledge creation. Second, collective works among the researchers in similar area and domain enhanced mission-oriented technology development projects such as patent creation or technology transfer. Third, we verified that the diversity of created knowledge and the degree of relation continuity between researchers increased in the condition of guaranteeing individual researcher's independence and autonomy as well as sharing various technological capabilities. These results provide the future policy directions related to the methods to measure the collaboration and performance analysis for multidisciplinary research.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 1997.11a
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• pp.3-31
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• 1997

Water has always played a significant role in the lives of people. In urbanised Rome, with its million people. sophisticated supply systems developed and then fled with the empire. only to be rediscovered later But it was the industrial Revolution commencing in the eighteenth century that ushered in major paradigm shifts In use and altitudes towards water. Rapid and concentrated urbanisation brought problems of expanded demands for drinking supplies, waste management and disease. The strategy of using water from local streams, springs and village wells collapsed under the onslaughts of rising urban demands and pollution due to poor waste disposal practices. Expanding travel (railways. and steamships) aided the spread of disease. In England. public health crises peaks, related to water-borne typhoid and the three major cholera outbreaks occurred in the late eighteenth and early nineteenth century respectively. Technological, engineering and institutional responses were successful in solving the public health problem. it is generally accepted that the putting of water into pipe networks both for a clean drinking supply, as well as using it as a transport medium for removal of human and other wastes, played a significant role in towering death rates due to waterborne diseases such as cholera and typhoid towards the end of the nineteenth century. Today, similar principles apply. A recent World Bank report Indicates that there can be upto 76％ reduction in illness when major water and sanitation improvements occur in developing countries. Water management, technology and thinking in Australia were relatively stable in the twentieth century up to the mid to late 1970s. Groundwater sources were investigated and developed for towns and agriculture. Dams were built, and pipe networks extended both for supply and waste water management. The management paradigms in Australia were essentially extensions of European strategies with the minor adaptions due to climate and hydrogeology. During the 1970s and 1980s in Australia, it was realised increasingly that a knowledge of groundwater and hydrogeological processes were critical to pollution prevention, the development of sound waste management and the problems of salinity. Many millions of dollars have been both saved and generated as a consequence. This is especially in relation to domestic waste management and the disposal of aluminium refinery waste in New South Wales. Major institutional changes in public sector water management are occurring in Australia. Upheveals and change have now reached ail states in Australia with various approaches being followed. Market thinking, corporatisation, privatisation, internationalisation, downsizing and environmental pressures are all playing their role in this paradigm shift. One casualty of this turmoil is the progressive erosion of the public sector skillbase and this may become a serious issue should a public health crisis occur such as a water borne disease. Such crises have arisen over recent times. A complete rethink of the urban water cycle is going on right now in Australia both at the State and Federal level. We are on the threshold of significant change in how we use and manage water, both as a supply and a waste transporter in Urban environments especially. Substantial replacement of the pipe system will be needed in 25 to 30 years time and this will cost billions of dollars. The competition for water between imgation needs and environmental requirements in Australia and overseas will continue to be an issue in rural areas. This will be especially heightened by the rising demand for irrigation produced food as the world's population grows. Rapid urbanisation and industrialisation in the emerging S.E Asian countries are currently producing considerable demands for water management skills and Infrastructure development. This trend e expected to grow. There are also severe water shortages in the Middle East to such an extent that wars may be fought over water issues. Environmental public health crises and shortages will help drive the trends.