The knowledge and human resources distribution system for university-industry cooperation (대학에서 창출하는 지적/인적자원에 대한 기업연계 플랫폼: 인문사회계열을 중심으로)
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- Journal of Intelligence and Information Systems
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- v.20 no.3
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- pp.133-149
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- 2014
One of the main purposes of universities is to create new intellectual resources that will increase social values. These intellectual resources include academic research papers, lecture notes, patents, and creative ideas produced by both professors and students. However, intellectual resources in universities are often not distributed to the actual users or companies; and moreover, they are not even systematically being managed inside of the universities. Therefore, it is almost impossible for companies to access the knowledge created by university students and professors to utilize them. Thus, the current level of knowledge sharing between universities and industries are very low. This causes a great extravagant with high-quality intellectual and human resources, and it leads to quite an amount of social loss in the modern society. In the 21st century, the creative ideas are the key growth powers for many industries. Many of the globally leading companies such as Fedex, Dell, and Facebook have established their business models based on the innovative ideas created by university students in undergraduate courses. This indicates that the unconventional ideas from young generations can create new growth power for companies and immensely increase social values. Therefore, this paper suggests of a new platform for intellectual properties distribution with university-industry cooperation. The suggested platform distributes intellectual resources of universities to industries. This platform has following characteristics. First, it distributes not only the intellectual resources, but also the human resources associated with the knowledge. Second, it diversifies the types of compensation for utilizing the intellectual properties, which are beneficial for both the university students and companies. For example, it extends the conventional monetary rewards to non-monetary rewards such as influencing on the participating internship programs or job interviews. Third, it suggests of a new knowledge map based on the relationships between key words, so that the various types of intellectual properties can be searched efficiently. In order to design the system platform, we surveyed 120 potential users to obtain the system requirements. First, 50 university students and 30 professors in humanities and social sciences departments were surveyed. We sent queries on what types of intellectual resources they produce per year, how many intellectual resources they produce, if they are willing to distribute their intellectual properties to the industries, and what types of compensations they expect in returns. Secondly, 40 entrepreneurs were surveyed, who are potential consumers of the intellectual properties of universities. We sent queries on what types of intellectual resources they want, what types of compensations they are willing to provide in returns, and what are the main factors they considered to be important when searching for the intellectual properties. The implications of this survey are as follows. First, entrepreneurs are willing to utilize intellectual properties created by both professors and students. They are more interested in creative ideas in universities rather than the academic papers or educational class materials. Second, non-monetary rewards, such as participating internship program or job interview, can be the appropriate types of compensations to replace monetary rewards. The results of the survey showed that majority of the university students were willing to provide their intellectual properties without any monetary rewards to earn the industrial networks with companies. Also, the entrepreneurs were willing to provide non-monetary compensation and hoped to have networks with university students for recruiting. Thus, the non-monetary rewards are mutually beneficial for both sides. Thirdly, classifying intellectual resources of universities based on the academic areas are inappropriate for efficient searching. Also, the various types of intellectual resources cannot be categorized into one standard. This paper suggests of a new platform for the distribution of intellectual materials and human resources, with university-industry cooperation based on these survey results. The suggested platform contains the four major components such as knowledge schema, knowledge map, system interface, and GUI (Graphic User Interface), and it presents the overall system architecture.
Currently, our country operates gifted education only as a special curriculum, which results in many problems, e.g., there are few beneficiaries of gifted education, considerable time and effort are required to gifted students, and gifted students' educational needs are ignored during the operation of regular curriculum. In order to solve these problems, the present study formulates the following research questions, finding it advisable to conduct gifted education in elementary regular classrooms within the scope of the regular curriculum. A. To devise a teaching plan for the gifted students on mathematics in the elementary school regular classroom. B. To develop a learning program for the gifted students in the elementary school regular classroom. C. To apply an in-depth learning program to gifted students in mathematics and analyze the effectiveness of the program. In order to answer these questions, a teaching plan was provided for the gifted students in mathematics using a differentiating instruction type. This type was developed by researching literature reviews. Primarily, those on characteristics of gifted students in mathematics and teaching-learning models for gifted education. In order to instruct the gifted students on mathematics in the regular classrooms, an in-depth learning program was developed. The gifted students were selected through teachers' recommendation and an advanced placement test. Furthermore, the effectiveness of the gifted education in mathematics and the possibility of the differentiating teaching type in the regular classrooms were determined. The analysis was applied through an in-depth learning program of selected gifted students in mathematics. To this end, an in-depth learning program developed in the present study was applied to 6 gifted students in mathematics in one first grade class of D Elementary School located in Nowon-gu, Seoul through a 10-period instruction. Thereafter, learning outputs, math diaries, teacher's checklist, interviews, video tape recordings the instruction were collected and analyzed. Based on instruction research and data analysis stated above, the following results were obtained. First, it was possible to implement the gifted education in mathematics using a differentiating instruction type in the regular classrooms, without incurring any significant difficulty to the teachers, the gifted students, and the non-gifted students. Specifically, this instruction was effective for the gifted students in mathematics. Since the gifted students have self-directed learning capability, the teacher can teach lessons to the gifted students individually or in a group, while teaching lessons to the non-gifted students. The teacher can take time to check the learning state of the gifted students and advise them, while the non-gifted students are solving their problems. Second, an in-depth learning program connected with the regular curriculum, was developed for the gifted students, and greatly effective to their development of mathematical thinking skills and creativity. The in-depth learning program held the interest of the gifted students and stimulated their mathematical thinking. It led to the creative learning results, and positively changed their attitude toward mathematics. Third, the gifted students with the most favorable results who took both teacher's recommendation and advanced placement test were more self-directed capable and task committed. They also showed favorable results of the in-depth learning program. Based on the foregoing study results, the conclusions are as follows: First, gifted education using a differentiating instruction type can be conducted for gifted students on mathematics in the elementary regular classrooms. This type of instruction conforms to the characteristics of the gifted students in mathematics and is greatly effective. Since the gifted students in mathematics have self-directed learning capabilities and task-commitment, their mathematical thinking skills and creativity were enhanced during individual exploration and learning through an in-depth learning program in a differentiating instruction. Second, when a differentiating instruction type is implemented, beneficiaries of gifted education will be enhanced. Gifted students and their parents' satisfaction with what their children are learning at school will increase. Teachers will have a better understanding of gifted education. Third, an in-depth learning program for gifted students on mathematics in the regular classrooms, should conform with an instructing and learning model for gifted education. This program should include various and creative contents by deepening the regular curriculum. Fourth, if an in-depth learning program is applied to the gifted students on mathematics in the regular classrooms, it can enhance their gifted abilities, change their attitude toward mathematics positively, and increase their creativity.
Two opposing views prevail regarding the economic impact of rural out-migration on the rural areas of origin. The optimistic neoclassical view argues that rapid rural out-migration is not detrimental to the income and welfare of the rural areas of origin, whereas Lipton (1980) argues the opposite. We developed our own alternative model for rural to urban migration, appropriate for rapidly developing economies such as Korea's. This model, which adopts international trade theories of nontraded goods and Dutch Disease to rural to urban migration issues, argues that rural to urban migration is caused mainly by two factors: first, the unprofitability of farming, and second, the decrease in demand for rural nontraded goods and the increase in demand for urban nontraded goods. The unprofitability of farming is caused by the increase in rural wages, which is induced by increasing urban wages in booming urban manufacturing sectors, and by the fact that the cost increases in farming cannot be shifted to consumers, because farm prices are fixed worldwide and because the income demand elasticity for farm products is very low. The demand for nontraded goods decreases in rural and increases in urban areas because population density and income in urban areas increase sharply, while those in rural areas decrease sharply, due to rapid rural to urban migration. Given that the market structure for nontraded goods-namely, service sectors including educational and health facilities-is mostly in monopolistically competitive, and that the demand for nontraded goods comes only from local sources, the urban service sector enjoys economies of scale, and can thus offer services at cheaper prices and in greater variety, whereas the rural service sector cannot enjoy the advantages offered by scale economies. Our view concerning the economic impact of rural to urban migration on rural areas of origin agrees with Lipton's pessimistic view that rural out-migration is detrimental to the income and welfare of rural areas. However, our reasons for the reduction of rural income are different from those in Lipton's model. Lipton argued that rural income and welfare deteriorate mainly because of a shortage of human capital, younger workers and talent resulting from selective rural out-migration. Instead, we believe that rural income declines, first, because a rapid rural-urban migration creates a further shortage of farm labor supplies and increases rural wages, and thus reduces further the profitability of farming and, second, because a rapid rural-urban migration causes a further decline of the rural service sectors. Empirical tests of our major hypotheses using Korean census data from 1966, 1970, 1975, 1980 and 1985 support our own model much more than the neoclassical or Lipton's models. A kun (county) with a large out-migration had a smaller proportion of younger working aged people in the population, and a smaller proportion of highly educated workers. But the productivity of farm workers, measured in terms of fall crops (rice) purchased by the government per farmer or per hectare of irrigated land, did not decline despite the loss of these youths and of human capital. The kun having had a large out-migration had a larger proportion of the population in the farm sector and a smaller proportion in the service sector. The kun having had a large out-migration also had a lower income measured in terms of the proportion of households receiving welfare payments or the amount of provincial taxes paid per household. The lower incomes of these kuns might explain why the kuns that experienced a large out-migration had difficulty in mechanizing farming. Our policy suggestions based on the tests of the currently prevailing hypotheses are as follows: 1) The main cause of farming difficulties is not a lack of human capital, but the increase in production costs due to rural wage increases combined with depressed farm output prices. Therefore, a more effective way of helping farm economies is by increasing farm output prices. However, we are not sure whether an increase in farm output prices is desirable in terms of efficiency. 2) It might be worthwhile to attempt to increase the size of farmland holdings per farm household so that the mechanization of farming can be achieved more easily. 3) A kun with large out-migration suffers a deterioration in income and welfare. Therefore, the government should provide a form of subsidization similar to the adjustment assistance provided for international trade. This assistance should not be related to the level of farm output. Otherwise, there is a possibility that we might encourage farm production which would not be profitable in the absence of subsidies. 4) Government intervention in agricultural research and its dissemination, and large-scale social overhead projects in rural areas, carried out by the Korean government, might be desirable from both efficiency and equity points of view. Government interventions in research are justified because of the problems associated with the appropriation of knowledge, and government actions on large-scale projects are justified because they required collective action.
Global challenges such as the corona pandemic, climate change and the war-on-tech ensure that the demand who the technologies of the future develops and monitors prominently for will be on the agenda. Development of, and applications in, agrifood, biotech, high-tech, medtech, quantum, AI and photonics are the basis of the future earning capacity of the Netherlands and contribute to solving societal challenges, close to home and worldwide. To be like the Netherlands and Europe a strategic position in the to obtain knowledge and innovation chain, and with it our autonomy in relation to from China and the United States insurance, clear choices are needed. Brainport Eindhoven: Building on Philips' knowledge base, there is create an innovative ecosystem where more than 7,000 companies in the High-tech Systems & Materials (HTSM) collaborate on new technologies, future earning potential and international value chains. Nearly 20,000 private R&D employees work in 5 regional high-end campuses and for companies such as ASML, NXP, DAF, Prodrive Technologies, Lightyear and many others. Brainport Eindhoven has a internationally leading position in the field of system engineering, semicon, micro and nanoelectronics, AI, integrated photonics and additive manufacturing. What is being developed in Brainport leads to the growth of the manufacturing industry far beyond the region thanks to chain cooperation between large companies and SMEs. South-Holland: The South Holland ecosystem includes companies as KPN, Shell, DSM and Janssen Pharmaceutical, large and innovative SMEs and leading educational and knowledge institutions that have more than Invest €3.3 billion in R&D. Bearing Cores are formed by the top campuses of Leiden and Delft, good for more than 40,000 innovative jobs, the port-industrial complex (logistics & energy), the manufacturing industry cluster on maritime and aerospace and the horticultural cluster in the Westland. South Holland trains thematically key technologies such as biotech, quantum technology and AI. Twente: The green, technological top region of Twente has a long tradition of collaboration in triple helix bandage. Technological innovations from Twente offer worldwide solutions for the large social issues. Work is in progress to key technologies such as AI, photonics, robotics and nanotechnology. New technology is applied in sectors such as medtech, the manufacturing industry, agriculture and circular value chains, such as textiles and construction. Being for Twente start-ups and SMEs of great importance to the jobs of tomorrow. Connect these companies technology from Twente with knowledge regions and OEMs, at home and abroad. Wageningen in FoodValley: Wageningen Campus is a global agri-food magnet for startups and corporates by the national accelerator StartLife and student incubator StartHub. FoodvalleyNL also connects with an ambitious 2030 programme, the versatile ecosystem regional, national and international - including through the WEF European food innovation hub. The campus offers guests and the 3,000 private R&D put in an interesting programming science, innovation and social dialogue around the challenges in agro production, food processing, biobased/circular, climate and biodiversity. The Netherlands succeeded in industrializing in logistics countries, but it is striving for sustainable growth by creating an innovative ecosystem through a regional industry-academic research model. In particular, the Brainport Cluster, centered on the high-tech industry, pursues regional innovation and is opening a new horizon for existing industry-academic models. Brainport is a state-of-the-art forward base that leads the innovation ecosystem of Dutch manufacturing. The history of ports in the Netherlands is transforming from a logistics-oriented port symbolized by Rotterdam into a "port of digital knowledge" centered on Brainport. On the basis of this, it can be seen that the industry-academic cluster model linking the central government's vision to create an innovative ecosystem and the specialized industry in the region serves as the biggest stepping stone. The Netherlands' innovation policy is expected to be more faithful to its role as Europe's "digital gateway" through regional development centered on the innovation cluster ecosystem and investment in job creation and new industries.