NDSL 174,803 Link page [] ư Ŭϼ.

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This study analyzed the science education activities for children aged 3 to 5 represented in the weekly teaching plans of early childhood education institutions. The distribution of content and trends in early childhood science education were analyzed and a direction for composing the content for early childhood science education activities was presented. To that end, the science education activities in the weekly plan and curriculum for 24 classes with children aged 3 to 5 at 8 child education institutions were analyzed for one year.The results show that understanding biological beings and the natural environment were most frequently represented and the most commonly planned for type of science education activity was free choice activity. In terms of timing, there were frequent plans for science education activities in April, July, October, November and December which were associated with the given season and while for children aged 3 to 5, regardless of their age, activities related to animals or plants were frequently planned, activities related to valuing life or learning about the convenience of tools and machines and their advantages and disadvantages had lower frequency in the plan.Continued training of the instructor would be required based on the identified overall distribution of content and their frequency in science education activity contents for children aged 3 to 5 that are currently planned for in the educational practice to promote better understanding of details and content plans.

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The purpose of this study was to evaluate the current 5th Youth Action Plan (2013-2017) to better promote the rights of children. This study analyzed in detail the 5 Priority Areas and relevant subsequent projects of the Action Plan. Five themes of analysis that were utilized were: Responsible authority, budget, project objectives, project content, performance indicators, and the target.The findings of this study revealed that the majority of the budget were dedicated to health related issues, failing to align the budget to the overall goal of the Youth Action Plan. Detailed findings are provided. In conclusion, the following major recommendations are provided. First improving the effectiveness of project through expansion of budget and review of the implementation of allocated budget are required. Second, construction of projects based on the CRC is needed. Third, through the use of relevant performance indicators, evidence-based policy should be ensured. Fourth, constructing an environment that allows for the promotion of the rights of youth needed. Fifth, consistency and alignment among projects established. Finally, good governance in implementation must be guaranteed.

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츮 ִ ȸ ް ȭ , â оߴ ϳ پ缺 ⼺ ǰ ִ. Ư dz ࿡ ̷ ϰ ִ ¿ ǹ̳ ؼ ǰ ִ. ̷ dz Ǵ ̷ 䱸ǰ ִ ̸ dz ࿡ Ȯϰ ùٸ νİ ̷ ϰ ϴµ д. dz ΰ ߽ Ͽ ΰ Ȱȯ ͸ ϱ ִ ̴. ̷ ǹ̿ dz ǥ ġ ǰ ġ dz ӿ ǥǾ ϴµ  ǥ ΰ ϴ ߿ ̴. ̷ ġ پȭ ô 䱸 ׷ ߽ â ذ ߽ ü ȭǰ ִ ̴. ׷Ƿ ̸ ذϱ dz Ȯ ذ 䱸ǰ, ڵ ո м ʿ䰡 ִ. dz ΰ屸 ̸ ϰ ִ ڵ ո мϿ ȿ ݿų ִ ü ȹ ϰ Ѵ. 켱, Ȯ dz ǹ̸ ľؾ ϹǷ ΰ dz ʿ ӿ dz Ȯ ϰ, о 輺 ã ġ 輺 ϰ, ⼭ ̷ dz ȹ  踦 ΰ Ͽ. ̷ ȹ 迡 Ű ؼ ڵ Ϲȭ ü 䱸 ǥǹ̸ Ȯ Ҽ ֵ ȹ ̷ ǥ üȭų ִ ǥ Ѱ輺 Ȯ Ͽ. ׸  ΰ ϴ ġ dz ȹ ʿ ߴµ, 迡 ȿ ݿų ִ ü ȹ Ǿ Ѵ.

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ô 310 ȹ ϰ , 34 ǰ 5⸶ Ÿ缺 θ ؾ Կ , ȹ Ͽ, ε å, ȯ ȭ ȭ ȹ ̴. Ŀ ȿ ֵ װ Ǿ ȹ ¤ ϰ Ǿ. 1忡 ϰ, Ͽ, 2忡 ȹ ȹ ذ ˾ƺ, ȹ Ȳ ľ Ҵ. 3忡 α ȹ Ϸ 8 Ͽ Ȳ Ư мϿ, ξϵ ȹ Ͽ. 4忡 ξϵ ȹ 泻 㰡 , , мϿ ľ Ʒ п Ͽ. ù°, ȹ Ȳ 캸 ñⰡ 86 51 ʾҴµ, 5⸶ Ÿ缺 θ Ͽ Ͽ Ѵٰ õǾ ֱ , 51 ιħ ȹ ġ 츦 ϰ ִ. ׷Ƿ ϴ Ͽ 5⸶ Ͽ Ͽ Ѵٴ ʿ䰡 ִ. °, ξϵ ȹ ȹ ü ϴ Ͽ, ؾ ϴ ȹҴ ȹ, 뵵ȹ, , , Ưȹ ߷ Ǿ, ĺٴ ʿ κи ִ ι ʿ䰡 ִ. °, ȹ ؿ ȿ Ͽ , ι(ȹҺ, ) ϰ , ִ ü ħ κ ϴ ǴܵǴµ, м ٿ ξϵ ȹ ó ι ʿ ټ ǴܵǹǷ, ȿ Ͽ Ŀ ü ʿ䰡 ִ.

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ءȹ 輳 ϴ ߴ 濡 Ƿ ߿ ϳ 蹰 ü ϴ ִ. ߽ Ͽ ո Ͽ Ͽ. ǥ ޼ϱ Ͽ ϰ, 츮 Ȳ мϿ 츮 θ 캸, 3 ̻ 30 ̸ 2,000 ֱ ż ǰų 2,300 忡 縦 ǽϿ Ȳ 뿡 縦 ǽϿ. غм ȯ(178) 2(356) ̻ ݼӰǰ (1,070), Ÿ (524), ڵ ƮϷ (449) 3 ̰ 1.5(267) ̻ 3 ܿ Ÿ (335), ݼӱǰ (276) ߰Ǿ 5 ̰ ̻ 9 Ÿ. ϸ 5 ̸ 85 90 % ݿ뷮 300 ̸̸, ٷ Ҽ ݿ뷮 300 ̻ . . ǹ 3 ǥ 1 ٷ 5 ̸ 忡 48 ʴ´١ ʿ ִ. , ٷ ϰ Ǿ Ǵܵȴ. , õ ݿ뷮 300 ϴ ϴ ġ ü ϱ ̵ ϰ ϵ ϱ⿡ ũٰ ִ Ÿ ϰ ִ ݿ뷮 ա̶  ⼳ Գ Ģ  ϰ ʾҴ. κ ȹ ۼϿ ɻ Ȯ Ȱ ʰ ־. ɻ Ȱ Ȱȭ 氨 ̹Ƿ ȹ ɻ ϰ Ȱ ִ ʿ䰡 ִ. . ̷ ȿ ϱ ؼ (1) ٷ Ͽ ءȹ ϵ ϰ (2) ȯ ȯ ̻ 9 Ȯϰ (3) ݿ뷮 ⼳뷮 հ Ǵ 뷮 ϸ (4) ɻ Ϸ ϰ Ȱ ִ ϴ ٶϴ.

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츮 3 ٴ̸, 13,509 ̸ ؾȼ ִ ̴. ؾ ޴ 3,220 ϸ α 1,274 α 27% ϰ ִ. ̷ 鿡 , 񵵽 ϴ ȹ п ߰ؾ ٸ ִ. ó ߿ ϰ кϰ ߵȴٸ Ư ° ȯ濡 ġ Ȯ ְ, , ڿطκ ϰ Ǿ ֹ ϰ ȴ. Ӱϰ ؾȱ ȸ ϱ ؼ ȯ , ȸȭ ׸ ϰ ̿ȹ ʿϴ. Ӱ ̿ȹ ΰ ֿ ʸ ̶ Ư ðȹ â Ͽ. ̿ȹ ǥ ̿ ݵǴ ° 븳 ؼϰ, ּ ߹ ãƳ ϴµ ִ. ̸ ؼ ϴ ̿뿡 ٴٸ ϴ Ȱ յǾ ϸ, µ ϰ ̷ Ѵ. ٴ ̿ȹ ǰ Ǿ ູ ϴ ̿ȹ ̴.

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This research suggested that the establishment of Korean National Comprehensive Plans for the Reduction of Damage from Storm and Flood can help the current status and enhance the role of natural hazard planning in South Korea. Recently, it is important to make a plan include not only structural strategies but also non-structural strategies to conserve the environment and make resilient community. Nevertheless, in South Korea, natural hazard planning has not paid attention to national plan for flood and storm, regional approach and land use planning method. Negotiations with experts and civic servants worked towards setting an agenda and we developed alternatives that reflected the agenda. The National Plan should improve its status, cooperate with related plans, and enhance connectivity with spatial plans. Therefore, the plan supports the establishment of the national vision-goal-strategy, the complementary procedure between top-down and bottom-up methods, and the regional mitigation strategies that consider urban planning.

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As local welfare is a complex of locality, residents and history, and the whole of the lives of local residents, we need to approach if from the holistic perspective. This study aims at reviewing and analyzing the contents of regional welfare planning, in particular, the 1st, 2nd planning on regional welfare in the cities of urban-rural mixed characteristics. It also has the aim of suggesting policy and practice implications for the future in social service design for regional welfare plans in Nam-won city.Research findings suggest several policy and practice implication. They are: manual containing national unity and regional characteristics, specific area plan and budget plan, systematization of needs research and planning, linkage of regional health planning and regional welfare plan, effective evaluation, deep concern of the mayor in the regional welfare plan.

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پ о븦 ϴ ũ 3 ǽϿ. , 1 2 (expertise) о߿ 븦 ϴ ߿ (Grit: ǥ޼ γ) ϰ ȹ (Deliberate practice) л о뿡  ġ 캸Ҵ. ̿ Բ, ִ ڱ ǰ ĥ Ƿ ݼ(contingencies of self-worth) ߿ ٷ. 1 ̿ о ( о, о ) ġ ܱб л ǽϿ(N = 99). ȸͺм , ° γ ɰ Ư(Big 5) ϰ о ϴ Ÿ. , о , 踦 ϰ ȹ ϴ Ÿ. 2 ݼ ߽ ذ ڱ ġ  ġ 캸Ҵ. ݼ о ȣۿ ȿ ߰ߵǾ, ڱ ġ ذ 迡 ۿ Ѵٴ ͵ . о õ ݼ ޴ ݼ ݴ Ÿ. ̷ ݼ ̷ ڱ ġ ̷ ϴ ̸, ڱ ġ ϴ ִٸ , ȣ ش. 3 Ư ɼ Ͽ л ȮϿ(N = 253). , о븦 ϰ ȹ н(ϰ ȹ Ư ݿ н) Ű ϴ Ÿ, ȥ ؼ ð ȿ . , ޸ǥ, ϰ ȹ ̾ ̰ پ о븦 ̲ äõǾ. л ܿ 뺯 غ񼺰 - ü ġ 캸 , ־. پ 븦 ؼ Ÿ ɻӸ ƴ϶ õ ߿ϴٴ ϸ, ġ Ÿ ɷ° () ϴ ѱ ǿ ° () ߿ ִ ŷ Ȱ ִ. , ϰ ȹ , ݼ θ Ұ Ư ν ̷ Ȯ ⿩ ̴.

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The purpose of this study was to examine the relationship between the big five personality traits (openness to experience, conscientiousness, extraversion, agreeableness and emotional stability) and five sub-scales of planned happenstance skills among undergraduate students in South Korea. Multivariate multiple regression analysis was used to examine the effects of the five personality traits on five sub-scales of planned happenstance skills. The results showed that openness to experience and extraversion positively related with all five sub-scales of planned happenstance skills and positively affected planned happenstance skills while conscientiousness and emotional stability not only positively related with but predicted a few sub-scales of planned happenstance skills. The results of the current study could be utilized in counseling services and career programs to figure out which sub-scales of planned happenstance skills can be developed based on the clients personality traits.

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This article examines how the Government-General of Joseon organized its agricultural technocrats, who oversaw 'Campaign to increase Rice Production' executed in the Colony Joseon during the 1920s, and the details of their tasks and achievements. Also, the establishment of local government officials, who were the supervisors at local level and paid with local tax revenue, will be discussed.The Bureau of Agricultural Administration at the Government- General of Joseon was the control tower of the colony's agricultural industry, and the department in charge of 'Campaign to increase Rice Production' expanded in the 1920s. The department thus became 'SiksanGook(ߧ) NongmuGwa(Τ)-TojigaeryangGwa(Τ) and eventually was promoted and became 'TojigaeryangBu(ݻ) in 1926. The advanced-level technocrats of this department were alumni of Tokyo Imperial University's College of Agriculture. In most cases, these technocrats had been working in Joseon since the 1910s, and even if they were newly appointed, they remained in Joseon for more than ten years. As the most renowned expert in Joseon agriculture and food industry, these technocrats played a key role in controlling the rice production and executing the increase campaign even after 1937. The number of agricultural technocrats increased in the 1920s.Most of the local government officials, paid with local tax revenue, were mostly technocrats rather than administrative management officials. For the Գ(Do) government, the number of these technocrat officials increased by tricefold, and for Gun government, a new positioned, titled Gisu() was installed. Most of these technocrats were Japanese, and there were only one or three officials of the Joseon origin for each Gun(). As the Joseon people's resistance to colonial control increased in the 1920s, the Government-General had to strategically soften up their control. To make their governance more effective, the technocrats with professional expertise and skills were assigned to deal directly with farmers and implement policies. Real-estate tax was increased, so that the local government can obtain greater tax revenue to hire more of these officials. This was an effort to more effectively govern the colony by employing the resources available within the colony in a more sophisticated manner.

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ȭô븦 Ͽ ߾ õ å ġü ܼϰ ϴ ¿ , Ư 並 Ǵ ϰ ׿ ߾ α׷ ȹϰ ڿ ޡϿ ʿ伺 Ŀ ִ. 2003⿡ ȸ ȸ ߽ ȸõ ġ Ͽµ, ϳ ġü ȸü Ͽ ϴ ̸, ٸ ϳ 4 á á ȸȹ ̴. ȸ ǰ ִ ȸȹ ߱ϰ ׿ õ ν ȸȹ Ÿ缺 ϴµ () ִ ڷḦ ϴµ ִ. ȿ ޼ϱ (research questions) ´. ù°, á á ȸȹ ԵǾ ֿι ΰ? °, ȸ ȸȹ Ⱓ Ѱ? °, (bottom-up) 츮 ִ°? °, ȸȹ Կ ־ ʼ 䱸Ǵ ȸ 屸 ǽõǰ ִ°? ֿ á á ȸȹ ü迡 ̸ ִ. ù°, , 󳲵 22 á ִ. °, 鿡 󳲵 á Ͽų ߿ ִ ȸȹ ٷ ִ. °, ð 鿡 2006 ڷḦ м ϴ Ⱦܸ ̴. ׷ á ߿ ְų ̹ ȸȹ 2007 2010 4 ȹ ̴. ִ. ù°, ȸȹ ü ȸȹ ι Ӱ ֵ 緮 οϰ ִµ ̿ 塤 ΰǰ ִ. á ȸȹ á ȸȹ աϴ ۼǹǷ ι پ缺 ްȹ ۿ ִ. ߾γ ü ι ̵ ϴ Ǿ ̴. Ϻ á ȸȹ Ƿι ԽŰ ִµ, ̰ ǹ ǰȹ ߺ ߱ϰ Ƿ Ƿι ȸȹ ܵǾ ϴٰ . °, á Ÿ Ǿ Ѵ. 뿪 ȸȹ ϴ á , Ư åڰ á ȸȹ ϴ ü õ ̴. ׷ ش á ֹ 屸ذ ȯ Կ ұϰ, ȸȹ İ 鿡 ſ · ۼǾٴ ƴ . ȹ ȸȹ õ ħ , á ȹ , ޱ Բ Ÿ ־ ʿ Ǿ. °, 屸 ׸ ̵ ̰ ſ ũ ̸ ϰų мϱ ƴ. Ư á ȸȹ ϱ á 屸 Ͽ 屸 ľϿ ϴµ á 屸 ׸ ޶ 屸 ҰϿٴ ־. ϼ á ȸȹ Կ ־ 屸 ׸ Ȱ ʴ´ٴ Ǿ. ѹ á á ȸȹ ־ ϰ ϴ 뿡 μ 忡 ڷῡ ƴϴ. 2007 ȸȹ ϴ ȹ ִ õ а迡 ȸȹ ü踦 Ͽ ̴.

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α ȭǰ, ȯ ְ ϸ鼭 Կġᰡ ʿϰ ǰ Ŀ ȣ ʿϰ Ǿ. Կȯڵ ϴ ̰ ʿ ϼ Ѽ ȿ ϰ ߿ ΰϰ Ǿ. ȿ ȯںȣ 鿡 ȹ ߿伺 εǾ. 츮󿡼 ֱ Ƿῡ ü 屸 ǰ ǷẸ ǽ÷ Ƿ ̿ ϰ Ǹ鼭 Կȯڰ ϰ Ǿ. Ư ߺ üɻ ָ ԰ԵǴ ȴ. ȸڿ̳ ü ̺ 츮 ǿ ȯںȣ å ϹǷ η Բ ȸϴ 찡 ߻ϰ ϴ δ. Ⱓ ؼ ȿ Ϸ ̸ ȹ ̰ ִ. ׷ 츮󿡼 ȹ ϰ ִ 幰, õ ȯ 屸ľǵ ̺ ̴. ̿ Ǵ Ͻ ɻ ָ Ǵ ȯڸ , ϴ ȯڿ ִ ְ , ׸ ȣ ʿ 屸 ľμ ȹ ִ Ͽ. £ ϴ 7 պ Կ ȯڿ ڸ Ͽ. ȯ 30, 60 Ǿ. ڰ ޼ϱ ۼϿ ȯڿ 縦 ǽ߰, ڹ ׳̳ , Ͽ. ȯ Ϲ , Կ õ , ȯ Ƿ ¿ , , ȹ , غ ʿ Ǿ. ڷмδ 󵵿 Ͽ ȯڿ ְ Ͽ ƽ ȸͺм(Logistic Regression Model) Ͽ ִ Ҹ мϿ. м . ù°, ؿ ־ ȯڿ ̳ Ƿ Ͽ ñ⿡ Ͽ, ϼ 113ӿ ұϰ ټ ȯڿ ŭ Ȱġᰡ ̷ ʾҴٰ ϰų غ Ǿ ñⰡ ٰ ߴ. °, ɸ ־ ߿ Ҿ, ȣ δ㰨 µ Ư ȯ ȣ鿡 Ҿ . ̷ ȯڵ غ񿡼, ȯڸ ȣϴ 鿡 ް ִ . Ÿ. ɸ , ϼ, ü Ͽ. ϼ ü 쿡 ȣ߰, ü ɼ ϼ ª 쿡 Ÿ. °, ȹ ־ ΰ ߴµ ȯڵ κ ڽ Ͽ ڿ ֱ⸦ ٶ ־. ݸ ־ ٸ Ƿ ϱ⸦ ٶ鼭 Ҹ Ÿ° ο ־ Ȱ Բ ϱ⸦ ٶ Ÿ. ȯڴ ȹ ؼ ̳ Ҹ 찡 Ҵµ, ڿ ѵǾ ְ 鸸 ȯڸ ȣؾ ϴ Ȳ ִ κ̴. °, غ ʿϴٰ 񽺿 ؼ ȯڵ õ ̳ ϴ ݸ鿡 񽺳 ȯڸ ȣϱ ϴ Ÿ. ̴ ɸ ġϴ ̴µ, ü 쿡 Ÿ̳ ȳ ʿ ߰ 쿡 ȯڳ ϴ Ÿ ü ɸ, , ȯ ʿϴٰ Ͽ ϰ ϴ 屸 ִ. ټ°, 캸Ҵµ ȯ ǻݿ 谡 ִ Ÿ, ǻݿ̳ ü ü Ҹ Ÿ Ÿ. ü ɰ 谡 ִ Ÿµ ü , ׸ ª ϴ Ÿ. ̻ ȯڿ ̶ ȭ ϰ غϿ ñ⿡, ȣ Ȯ ̷ ֵ ȹ ؾ ̴. ̿ ȸ Ұ ļӿ 鿡 ϰ Ѵ. ù°, ȸ Կʱ ȯڿ ɸȸ 屸 ϰ õ Ͽ ȯڿ ϰ غ ֵ ; ̴. °, ȣ ʿ ִ ü踦 Ȯؾ Ѵ. ü踦 Ȯϰ ȸڿ ߱ ʿ ̴. ̿ ȸ ʿѵ ȯڻ¿ ˸´ ü , αٺ 簡 , ۼ , ڿ α׷ Ȱ ڿ ؾ ̴. °, غ ʿ α׷ ʿϴ. , ʿ ȯڳ ӵ ؼ ɸ ¿ ֵ ؾ ϰڴ. °, ϴ ȯڿ ǻ縦 ݿϿ ֵ ؾ ̴. Ƿ Բ ȸ Ͽ ȯڿ ҾȰ ȸ ʰ óس ִ ⸦ ؾ ̴. ̻ ȸ Ȱ Ǿ ̴. ǻ, ȣ, ġ ̷ ȯڿ ɸ ȸ, Ƿ 屸 ľϰ ȣ Ȯ ֵ ; ̴. ȯڰ ȸ ϱ ؼ ȯڵ ü Ȯ å ذå ʿϰڴ. ̷ ȹ ߿伺 εǰ ִ Ƿȸ Ȱ Ͽ ȹ ̷ٸ ȸ Կ ū ̴. ȯ 屸縦 ȹ , ȹ ȿ  򰡰 ̷ ̴.

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⵵ ġü ȸȹ Ÿ ȸȹ ǹ̸ ľ , ϴ ̴. ְ ν ٶ ǹ̸ ȸȹ ǹü ǥü , ǹڿ ΰ ǹ, а Ͽ мϿ.
 ̸ ʿ ⵵ ü 31 ‧ м Ͽ, ÿ ֽø ʷ Ͽ. ڷ м ⵵ ÿ ֽ ȸȹ õ ڷ Ͽ, ȸȹ ǹü ǥü , ǹڿ ΰ ǹ, а ϴ ڷḦ ϰ мϿ. Ⱓ 2015 2 4 3 Ǿ, ü ǹü 2, ǥü 2, ǹ 2, ΰ ǹ 2, а 2 10 Ͽ.
 Ÿ ȸȹ ǹ ׸ ϸ .
 ù°, ȸü ǹü ǥü νϴ ȸȹ ֵ ΰ ߽ ȭ Ÿ. ʱ 1 2 ֱ 3 ȹ ־ ѷ Ư¡ Ÿ, ȹ ȸü Ǿ. ȸȹ Ϲ ֵ  ΰ ̾, ֹε ߿ νϰ ־.
 °, ǹڿ ΰ ǹڰ νϴ ȸȹ ΰ° ֹ ̲ Ÿ. ȸȹ ϼ ִ ǹ ŭ 1 2, 3 Ư¡ Ѱ迡 ؼ иϰ νϰ ־. ΰ ǹڵ ΰ° ֹ ߿伺 νϰ ־, ߾ ߿伺 Ͽ.
 °, ȸȹ а νϴ ȸȹ ü и Ư¡ ٺ ذؾ õǾ. ݱ ȸȹ Ư¡ ü , ΰ ǹ ְ νϴ Ÿ. ȹ ֹ Ͽ. ׷ ̿ ǹ ִ ұϰ ȹ Ѱ Ÿ. ̸ غϱ , 츮 ü Ȳ ȭ ο ȸȹ ʿ伺, ȸ , ߾ ͸ ʿ伺 Ǿ.
 
 ̻ ȸȹ Ÿ .
 ù°, ȸȹ , ü(⵵) ‧ иϰ ʿ䰡 ִ. °, ȹ ׿ ȸ Ŀ ӹ ִ ȸȹ ⼺ ȯ ؾ Ѵ. öϰ ߽ ȹ ٲ Ѵ. °, ȸȹ ߰ ΰ¿ ؾ Ѵ. °, ȸȹ ǹ ֹ Ǿ Ѵ.
 
 ̷ Ͽ.
 ù°, ֹ ̲ ֵ ȸȸ ȸü ȭؾ Ѵ. °, ȸȹ 򰡸 ü踦 ؾ Ѵ. °, ΰ ǹڵ ü ɰ ʿϴ. °, ֹ 屸 ڿ Ȳ ľϴ ȸ ٿȭؾ Ѵ. ټ°, ü åӼ ִ ȸ ü ؾ Ѵ. °, ⵵ ü д ü ü踦 ؾ Ѵ. ϰ°, ȸȹ ʿ伺 ߿伺 ü ν ־ Ѵ. °, ͷμ Ǻ иϰ Ǿ Ѵ. ȩ°, ȸü ȸü ȯ ü ȭ ͸ 䱸ȴ. °, ȸȹ ϵ Ǿ ľ ִ ǥ ߵǾ Ѵ.

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ȹ ൿ̷(TPB) ġ ൿ ȹ ൿ ̷(TPB) Ͽ ġ ൿ ռ ϰ, ġ ൿ Ȯν ġ õ ڷ ϰ Ͽ. ó ġ ġ 320 ڷḦ Ͽ. SPSS 18.0 AMOS 18.0 Ͽ м, ȸͺм, κм Ͽ. ൿ κм AGFI κ (=18.824, df=2, GFI=0.975, AGFI=0.815, NFI=0.931, CFI=0.936, RMR=0.032) ռ Ͽ 򰡵Ǿ. ġ ൿ ǵ µ, ְ Թ, ൿ ϰ Ǿ, ൿ ൿ, ൿ ǵ Ǵ Ÿ. Ư ൿ ǵ ൿ ġ ľϰ TPB Բ Ư Ͽ ܰ ȸͺм ǽ , ൿ ǵ ġ µ ħ ġ ְ Թ ̾, ൿ ġ ൿ , ٹ, ٹ, ġ, ȥ, ൿ ǵ, ̼Ƚ ̾. ̻ , ġ ൿ ν TPB ռ Ǿ, ൿ ΰ ڰ ȮεǾǷ Ͽ ġ ൿ õ ؼ 켱 ൿ õؾ ϸ, µ ְ Թ Ŵν ൿ ǵ Ѿ Ѵ. ̸ ġ ̼ ذ ֿ ؾ ϰ, ġ ʿ ֱ ǽؾ ϸ, ġǷ  ü ʿϴ.

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³ȭ ȭʿ ̻ȭź å Ȯ, ȿ Ȱ λϰ ִ. 츮 δ 2008 8 ź 塱 ۷ι з Ͽ 忡 ǥϿ, о߿ 2025 ๰ ο ๰ ǹȭ Ѵٴ ħ̴. ̿, 2001 ǹ ȿ ϱ ǹȿ ǽϰ , 2013 2 23Ϻ ๰ ģȯ๰ üɵ Ͽ ̸, ģȯðǼ , ̴. Ư, ǹ ȿ ̱ ؼ ʱ ༳谡 ߿ϱ 㰡ܰ迡 500 ̻ ǹ ȹ ǹ ϵ Ͽ ǹ о߸ ߿ ҷ ٷ ִ. ȹ Ϲݻ, ǹ, ǥ伭(EPI) ׸ Ǿ ΰ๰ EPI 65 ̻, ๰ EPI 74 ̻ Ǿ߸ 㰡 㰡 ִ. ̷ ð ʿ ȮǾ, ܿ ȭǾ ȹ ׸ ǹ ࿡ 󸶳 ݿǰ ִ ڷᰡ ̴. ̿ ڷ ִ. 1 , 2 񱳡信 , 3 ⡯ Ͽ. ְźκ, ְ κ, ְ κ Ͽ ǥ伭(EPI) κ û ׸ 񱳡Ͽ. 1. ְźκ ๰ ְ κ ๰ EPI κ θ 񱳡 Ͽ ְſ ְ 뵵 ⺻ ũ ǹ ׸ 迡 ִ 1, 2, 3, 5׸ ๰ ߵ Ȯ ־. ̴ ⺻ ũ 㰡ǿ ִ 뿡 ϱ Ǵܵȴ. ְźκ ٰż ۼ ϰ ⺻ 13 ׸ Ҵ ݸ ְ ְźκ ܴܿ . 2. ְźκ ๰ ְ κ ๰ EPI κ θ 񱳡 Ͽ ְſ ְ 뵵 ⺻ ũ ǹ ׸ 迡 ִ 1, 2, 3, 5׸ ๰ ߵ Ȯ ־. ̴ ⺻ ũ 㰡ǿ ִ 뿡 ϱ Ǵܵȴ. ְźκ ٰż ۼ ϰ ⺻ 13 ׸ Ҵ ݸ ְ ְźκ ܴܿ . 3. ְ κ ๰ ְ κ ๰ EPI κ θ 񱳡 Ͽ ְ ְ Ը ⺻ ũ ǹ ׸ 迡 ִ 1, 2, 3, 4, 5׸ ๰ ߵ Ȯ ־. ̴ ⺻ ũ 㰡ǿ ִ 뿡 ϱ Ǵܵȴ. 4. ְšְ ְ ๰ EPI κ θ 񱳡 Ͽ ְſ ְ 뵵 ⺻ ũ ǹ ׸ 迡 ִ 1, 2, 3, 4, 5׸ ๰ ߵ Ȯ ־. ̴ ⺻ ũ 㰡ǿ ִ 뿡 ϱ Ǵܵȴ. ְźκ ٰż ۼ ϰ ⺻ 13 ׸ Ҵ ݸ ְ ְźκ ܴܿ . ְ ๰ ܺ ġ dzڷ Ǿ ִ 찡 ټ ־ ġdz ܿ ҿ 4 ܴܿ äá ׸ ־. ̷ ҿ Ȯ ִ ġdz ܸ󼼵 ÷εǾ ִ 쿡 Ͽ. ְźκ, ְ κ, ְ κ ๰ EPI κ θ

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ȯ ذȹ 뵿 . ׸ Ϻ׸񿡼 ߺǴ κе . ߺǴ κ з ϰ ̿ ð η ڰ ʿϴ. ߺ κ Ͽȭ Ѵٸ 濡 ̴. ذȹ ׸񺰷 ۼǾ ϰ ׸ ϴϷ Ͽ Ͽȭ ִ Ͽ.
 
 Ͽȭ ׸ .
 (1) ޽ü 䡯 ڷᡯ ڷ Ͽ, (2) ؼ ڷᡯ MSDS Ͽ, (3) ޽ü , ü Ȳ ڷᡯ Equipment Data sheet ۼϿ. (4) ޽ü  ס ڷᡯ, , 輺򰡡 ߺǴ κ Ͽ, (6) ȭл ․Ʒ ü ȹ ġ ȹ Ϻκ Ͽ Ͽȭ Ͽ.
 
 ó Ͽȭ ȴٸ ߺ ְ Ǿ ˱ 忡 ȯο 뵿 ɷ ų ˹޴ 忡 ۼ ȭ ȿ ų ִ. ̸ ð ̿ ϰ ż Ű ִ.

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λ̷ ȸ𵨿 ϴ Krumboltz(2009) ȹ ̷ ٰŷ ȹ 쿬(ȣ, γ, , 谨, ) ų ִ α׷ 츮 л λ ȿ ̸ ˾ƺ ̴. 쿬 ν ȿ ȥǾ Ÿ 쿬ν ȹ 쿬 κε 迡  ϴ ˾ƺ ̴. ϰ Ͽ.
 ù°, ȹ 쿬̷п ٰ λα׷ л ȹ 쿬 ĥ ̴. °, ȹ 쿬̷п ٰ λα׷ л 쿬ǿ ĥ ̴. °, ȹ 쿬̷п ٰ λα׷ л ĥ ̴. °, ȹ 쿬̷п ٰ λα׷ л ΰ ڱȿɰ ĥ ̴. ټ°, ȹ 쿬̷п ٰ λ α׷ л νƮ ĥ ̴. °, 쿬 ȹ 쿬 л , ΰ ڱȿɰ, νƮ 踦 Ű ̴.
 λ㿡 ϰ ִ ڱء 衯 ظ Krumboltz(2009) ȹ 쿬 4ܰ Բ α׷ 籸Ͽ.
 4 л Ǿ. α׷ ǽô 51, 21, ׸ 22 迡 Ͽ. α׷ 1ȸ 9 Ǿ ȸ 80о Ǿ. ̼ ð ˻縦 ǽϿ α׷ 1 ˻縦 ǽ Ͽ T- Űм(ũ μ) Űȿ ˾ƺҴ.
 ߵ α׷ . ߵ α׷ ȹ 쿬 ΰ ڱȿɰ ǹ ȿ ܰ ǹ ȭ . 쿬 ȹ 쿬 ΰ ڱȿɰ ǹ κиŰ ߴ. ü ȿ ˾ƺ, ȹ 쿬 γ, 谨, ǹ ȿ ־ ȿ . ˾ƺ м ǽϿ. ٸ ε ϰ Ÿ. ΰ ڱȿɰ ο ǥ, ȹ, ڱ򰡿 ǹ ȿ ־. ذῡ ȿ Ÿ. ȹ 쿬 ΰ ڱȿɰ ȿ ־. α׷ 쿬 ǿ ȭŰ ɷ° νƮ ̴ ȿ .
 α׷ α׷ ٷ ϴ 쿬 ٷ ó ȯ̳ κȭ ô뿡 α׷ ȿ ̴. л鿡 ߵ α׷ ϴ Ӹ ƴ϶ λ ִ پ ذ Ȯ ִ. Ư, ġ ʾҴ ٷ ֱ ذ ȣϴ ڵ鿡 ̴. 㿡 ٷ⿡ ϰų Ǵ ٷ ֱ 㿡 ȥǾ ִ. , ߽ ļӿ Ͽ

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ô ð Žð ѷϰ ù ȭ ѷ ̴. 1970뿡 ߿ ϴ ð ߽ ް ð Ǿ, 1990  д Žð ߵǾ. ֱٿ ѱ Ǿ ִ DZ DZŵð ߵǾ. ̷ ô ü ü ð ƴϱ ٸ Ư ѷϰ ִ. ̷ ð ̿, α, ȸ , ñݽü Ȳ, ȸ ߻  ߱ϰ ִ. ̷ ذϰ ִ ϵ ϴ ʵб ϰ Ѵ. ʵб ðȹüν Ȱ ϰ ִ. Ȱ ϴµ ־ ٸֱ ̷п ߽ Ǵ ü̴. ׷Ƿ ʵб ġ ִ Ȱ ֵ Ӹ ƴ϶ ȸ ü ߽ɿ ν ùε Ǹ Ű Ѵ. Ȱǰ ٸֱ ̷п ̷ м Ͽ. 켱 αƯ, , Ȳ, Ȳ Ȳ Ͽ ʵб ġ Ȳ Ͽ мϿ. ̸ GIS Ʈũ м ʵб 500m, 1000m Ͽ. ̴ б ִ ִ Ÿ 500m 1000ͷ ϰ Ÿ Ͽ ǿ ̴. ȿ б ǿ 4θ ʴ Ͽ Ͽ. ̻ ȯ 1000m б Ͽ Ͽ. б ø м , αе ʵб Ǿ ϴٰ ǴܵǴ ð мϿ. αе ӿ ұϰ ʵб ġǾ ʾ ʵб 䱸Ǵ , Ȳ ʾ ʵб б ϰ Ǿ Ͽ ϰ ̿ ذ ϵ Ͽ. ð ȹ ߵ ʾ ʵб ϰ ġ ؼ 켱 Ȯ ñ ̴. ð κ 簳 Ǿ ִ ŭ Ȯ ߰ ʿϴ. Ȳ ä ġ ʵб б Ը иϿ ұԸ б ġϵ Ͽ Ѵ. 4 θ ʴ бǰ ʵб бǿ Ե ֵ ұԸ б ϴ Ǿ Ѵ. Ȱ ⺻ ߽ Ǵ ʵб ո ġǾ ִ ÷ ϱ ؼ ñ⺻ȹ б 뿡 ȹ Ǿ бü ġȹ ̷ ֵ Ͽ Ѵ. ðȹ üġ  ħ ϰ ȹ ܰ迡 ȿ Ͽ ȿ ֵ Ͽ Ѵ.

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ȹ ൿ̷(TPB) ʵл ĩ ൿ ġ ȹ ൿ̷(TPB) ⺻ ̱ ڱȿɰ ߰Ͽ , ʵл ĩൿ ġ Ȯϰ Ͽ. 2012 10 18Ϻ 11 30ϱ õÿ ġ ʵб 4, 5, 6г 443 ڱԽ 縦 ǽϿ. SPSS21.0 ̿Ͽ м, ȸͺм, ܰ ȸͺм Ͽ. TPB Ͽ ȸͺм ǽ ְ Թ ĩ ǵ Կ ־ ߿ , ڱȿɰ, ൿ, ĩ µ ̾. TPB ܿ ڱȿɰ ߰Ͽ ϰ Ͽ. ĩ ൿ ĩ ǵ ڱȿɰ Ǿ, ൿ ܵǾ. TPB ܿ ڱȿɰ ߰Ͽ ϰ Ͽ. Ư ĩ ǵ ൿ ġ ľϰ TPB Բ Ư Ͽ ܰ ȸͺм ǽ , ĩ ǵ ġ ְԹ, ڱȿɰ, ൿ, ĩ µ, Ÿ ܿ ġ ȮεǾ. ĩ ൿ ġ ĩ ǵ ڱȿɰ, Ӵ ȮεǾ. ̻ , Ƶ ùٸ ĩ Ű ؼ Ƶ ִ θ ĩ ֵ ϰ, Ƶ ĩ õ ִ ȯ ν ĩ ǵ Ű, DZ Ƶ ĩ ִٴ ڽŰ ɾ ʿ䰡 ִٰ ȴ.

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ȹ ̷ ȹ ̷ ֿ , ׸ ų ų ¿ м 幰. ̿ ȹ ̷(Theory of Planned Behavior) ٰ ݿ ǵ ݿ ġ ֿ Ρų ΰ 踦 ľϰ, ƿ﷯ ݿ ǵ Ư ΰ м ȿ ݿ ϰ Ͽ. 纴 34 2000 9 25Ϻ 10 7 ̿ 縦 ǽϿ, ǥ ν ŷڵ ϰ Cronbach's coefficientм Ͽ. ǥ ŷڵ Ÿ. Ͽ 1 2000 11 2Ϻ 11 28ϱ ǽϿ, 2 2001 2 4Ϻ 2 28 ̿ ǽϿ. ( ) 216 1 ȸ 216, 2 ȸ 204̾, 1 翡 信 ϴٰ Ǵܵ 26ǰ Ȯ ʾ 5 Ͽ, 2 ο 12 Ͽ. 12 185 м Ǿ. ڷ м SAS(statistical analysis system) Ͽ м ƽ ȸͺм, ANOVA, t-test м Ͽ. TPB ֿ ΰ ų м , 谡 ־ TPB ֿ ݿ ǵ ȸͺм ſ Ͽ. ǵ ̾ٴ TPB ̷п ƽ ȸ м ǽϿ ʾҴ. ݿ ǵ ġ δ δ ݿ ǽ , δ , ݿ ̾ , Դ Ǵ ź Ⱓ ݿ , δ , ݿ ݿ ǵ ġ ʾҴ. ȿ鿡 , ְ ȭ ̾, ɸ鿡 , ̾. ݿ Ͽ ź 뿡 ݿ ݿ , ̴ ݿ ǵʹ Ͽ, δ ݿ ǽ ݿ ǵ ü ִ Ͻ̰ ݿ ٴ ݿ ȿ ־. ǽõǰ ִ ݿ ̰ ü ư ٶ Ǹ, 鿡 ΰ ݿ ִ ȿ Ǿ ȴ.

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