3 research outputs found

    A Causal Relationship among Career Exploration-Behavior, Career Exploration Self-Efficacy, Parent's Attachment and Career Support From College of Seoul National University Undergraduates

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์‚ฐ์—…๊ต์œก๊ณผ, 2012. 2. ์ •์ฒ ์˜.์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ์˜ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™๊ณผ ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ, ๋ถ€๋ชจ์• ์ฐฉ, ํ•™๊ต์˜ ์ง„๋กœ์ง€์›์˜ ์ธ๊ณผ์  ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š”๋ฐ ์žˆ์—ˆ๋‹ค. ์—ฐ๊ตฌ๋ชฉ์ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๊ฐ€์„ค์„ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์„ค์ •ํ•˜์˜€๋‹ค. ์ฒซ์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ์˜ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™๊ณผ ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ, ๋ถ€๋ชจ์• ์ฐฉ, ํ•™๊ต์˜ ์ง„๋กœ์ง€์›์˜ ์ธ๊ณผ๋ชจํ˜•์€ ์‹ค์ฆ์  ์ž๋ฃŒ ์˜ˆ์ธก์— ์ ํ•ฉํ•  ๊ฒƒ์ด๋‹ค. ๋‘˜์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ์˜ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์— ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ, ๋ถ€๋ชจ์• ์ฐฉ, ํ•™๊ต์˜ ์ง„๋กœ์ง€์›์€ ์ง์ ‘์ ์œผ๋กœ ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋‹ค. ์…‹์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ์˜ ๋ถ€๋ชจ์• ์ฐฉ๊ณผ ํ•™๊ต์˜ ์ง„๋กœ์ง€์›์€ ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ์„ ๋งค๊ฐœ๋กœ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์— ๊ฐ„์ ‘์ ์œผ๋กœ ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋‹ค. ๋„ท์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ์˜ ๊ฐœ์ธ์  ํŠน์„ฑ์— ๋”ฐ๋ผ ์—ฐ๊ตฌ๋ชจํ˜•์ด ์ฐจ์ด๊ฐ€ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๋ชจ์ง‘๋‹จ์€ 2010๋…„ ์„œ์šธ๋Œ€ํ•™๊ต์— ์žฌํ•™ํ•˜๊ณ  ์žˆ๋Š” ์ „์ฒด ํ•™์ƒ์ด๋‹ค. 2010๋…„ ํ˜„์žฌ ์„œ์šธ๋Œ€ํ•™๊ต์— ์žฌํ•™ํ•˜๊ณ  ์žˆ๋Š” ํ•™์ƒ๋“ค์€ 16,325๋ช…์œผ๋กœ, ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๋ณ€์ธ์˜ ํŠน์„ฑ๊ณผ ๋ถˆ์„ฑ์‹ค ์‘๋‹ต ๋“ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ด 380๋ช…์„ ํ‘œ์ง‘ ํ•˜์˜€๋‹ค. ์ด๋Š” ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ์˜ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™๊ณผ ๊ด€๋ จ ์žˆ๋Š” ๊ธฐ์ค€ ๋ณ€์ˆ˜๋กœ ์ง‘๋‹จ์„ ์ธตํ™”ํ•˜์—ฌ ๋น„์œจ์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. ์ฃผ์š” ์ธตํ™” ๊ธฐ์ค€์ธ ์†Œ์†๋‹จ๊ณผ๋Œ€ํ•™์˜ ํŠน์„ฑ๊ณผ ์„ฑ๋ณ„์„ ๊ณ ๋ คํ•˜์—ฌ ํ• ๋‹นํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์ด๋ฃจ์–ด์กŒ๋‹ค. ์กฐ์‚ฌ ๋„๊ตฌ๋Š” ์ง„๋กœํƒ์ƒ‰ํ–‰๋™, ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ, ๋ถ€๋ชจ์• ์ฐฉ, ํ•™๊ต ์ง„๋กœ์ง€์›, ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ ๋ฐ ์ง„๋กœ ํŠน์„ฑ ์กฐ์‚ฌ๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑ๋œ ์„ค๋ฌธ์ง€๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ง„๋กœํƒ์ƒ‰ํ–‰๋™ ์ธก์ •๋„๊ตฌ๋Š” ์ตœ๋™์„ (2003)์ด ๊ฐœ๋ฐœํ•œ ๋„๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ๋‚˜์— ๋Œ€ํ•œ ํƒ์ƒ‰ 12๋ฌธํ•ญ, ์ง์—…์— ๋Œ€ํ•œ ํƒ์ƒ‰ 16๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๊ณ , ๋‚ด์  ์ผ์น˜๋„ ๊ณ„์ˆ˜๋Š” .854~912 ์ด์—ˆ๋‹ค. ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ ๋„๊ตฌ๋Š” Solberg(1994)๊ฐ€ ๊ฐœ๋ฐœํ•œ ๋„๊ตฌ๋ฅผ ์ตœ์˜ฅํ˜„(2007)์ด ๋ฒˆ์•ˆํ•œ ๋„๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ๋ฉด์ ‘ํšจ๋Šฅ๊ฐ 5๋ฌธํ•ญ, ๊ฐœ์ธ์ ํƒ์ƒ‰ํšจ๋Šฅ๊ฐ 5๋ฌธํ•ญ, ์ง์—…ํƒ์ƒ‰ํšจ๋Šฅ๊ฐ 6๋ฌธํ•ญ, ๊ด€๊ณ„๊ตฌ์ถ•ํšจ๋Šฅ๊ฐ 4๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ์œผ๋ฉฐ, ๋‚ด์  ์ผ์น˜๋„ ๊ณ„์ˆ˜๋Š” .841~.911 ์ด๋‹ค. ํ•™๊ต ์ง„๋กœ์ง€์› ๋„๊ตฌ๋Š” ์‚ฌํšŒ์  ์ง€์ง€์™€ ์ง„๋กœ์ง€์ง€์˜ ์ธก๋ฉด์—์„œ ๋ฌธํ—Œ๊ณ ์ฐฐ์„ ํ†ตํ•˜์—ฌ ๊ฒ€์‚ฌ์˜ ํ•˜์œ„์˜์—ญ์„ ๊ฒฐ์ •ํ•œ ๋’ค, ๊ธฐ์กด ์ธก์ •๋„๊ตฌ์˜ ๋ฌธํ•ญ๋ถ„์„๊ณผ ๊ฐœ๋ฐฉํ˜• ์งˆ๋ฌธ์ง€๋ฅผ ์ด์šฉํ•œ ์ƒˆ๋กœ์šด ๋ฌธํ•ญ ์ˆ˜์ง‘ ๋ฐ ์ง€์‹œ๋ฌธ๊ณผ ์‘๋‹ต์–‘์‹์„ ๊ฒฐ์ •ํ•˜์—ฌ ํ•™๊ต์˜ ์ง„๋กœ์ง€์› ๊ฒ€์‚ฌ(์ดˆ์•ˆ)๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ดํ›„ ๋ถ€์ ์ ˆํ•œ ๋ฌธํ•ญ์„ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ „๋ฌธ๊ฐ€ ๊ฒ€ํ† ์™€ ์˜ˆ๋น„์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜๋Š” ์ˆœ์œผ๋กœ ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ด ์ธก์ •๋„๊ตฌ๋Š” ์ •๋ณด์  ์ง€์› 7๋ฌธํ•ญ, ์‹ค์ œ์  ์ง€์› 8๋ฌธํ•ญ, ์ •์„œ์  ์ง€์› 10๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ์œผ๋ฉฐ, ๋‚ด์  ์ผ์น˜๋„ ๊ณ„์ˆ˜๋Š” .878~.889 ์ด๋‹ค. ์ž๋ฃŒ ์ˆ˜์ง‘์€ ์ž๋ฃŒ ์ˆ˜์ง‘์€ 2011๋…„ 10์›” 1์ผ ๋ถ€ํ„ฐ 10์›” 20์ผ๊นŒ์ง€ ์šฐํŽธ์กฐ์‚ฌ ๋ฐ ๋ฐฉ๋ฌธ์กฐ์‚ฌ๋ฅผ ํ†ตํ•˜์—ฌ ์‹ค์‹œ๋˜์—ˆ๋‹ค. ์ด์— 380๋ช…์˜ ์ž๋ฃŒ๊ฐ€ ํšŒ์ˆ˜๋˜์—ˆ์œผ๋ฉฐ(ํšŒ์ˆ˜์œจ 95.0%), ํšŒ์ˆ˜๋œ ์ž๋ฃŒ ๊ฐ€์šด๋ฐ ํ•œ ๋ฌธํ•ญ์ด๋ผ๋„ ์‘๋‹ตํ•˜์ง€ ์•Š์•˜๊ฑฐ๋‚˜, ์—ญ๋ฐฐ์ ๋˜๋Š” ๋ฌธํ•ญ์ด ์žˆ์—ˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๋ชจ๋“  ๋ฌธํ•ญ์— ํ•œ ๋ฒˆํ˜ธ๋กœ ์‘๋‹ตํ•œ 9๋ช…์„ ์ œ์™ธํ•œ 371(์œ ํšจ ์ž๋ฃŒ์œจ 92.8%)๋ช…์˜ ์ž๋ฃŒ๋ฅผ ์ตœ์ข… ๋ถ„์„์— ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋Š” SPSS 18.0 for Windows์™€ AMOS 18.0์„ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ๋ชจ๋“  ๋ถ„์„์— ์žˆ์–ด ํ†ต๊ณ„์  ํŒ๋‹จ์€ ์œ ์˜์ˆ˜์ค€ 5%์— ๋”ฐ๋ผ ์ด๋ฃจ์–ด์กŒ๋‹ค. ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด, ์ฒซ์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ์˜ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™๊ณผ ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ, ๋ถ€๋ชจ์• ์ฐฉ, ํ•™๊ต์˜ ์ง„๋กœ์ง€์› ๊ฐ„์˜ ๊ฐ€์„ค์  ์ธ๊ณผ๋ชจํ˜•์˜ ์ ํ•ฉ๋„๊ฐ€ ์–‘ํ˜ธํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜ ๋ณ€์ธ ๊ฐ„์˜ ์ธ๊ณผ๊ด€๊ณ„๊ฐ€ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋‘˜์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ์˜ ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ, ํ•™๊ต์˜ ์ง„๋กœ์ง€์›์€ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์— ์ง์ ‘์ ์œผ๋กœ ์ •์˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด์— ๋ฐ˜ํ•ด ๋ถ€๋ชจ์• ์ฐฉ์€ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์— ์ง์ ‘์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ•˜์˜€๋‹ค. ์…‹์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ์˜ ํ•™๊ต์ง„๋กœ์ง€์› ๋ฐ ๋ถ€๋ชจ์• ์ฐฉ๊ณผ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์˜ ๊ด€๊ณ„์—์„œ ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ์€ ์œ ์˜๋ฏธํ•œ ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฐ€์กŒ๋‹ค. ๋„ท์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ๋“ค์˜ ๋‹จ๊ณผ๋Œ€ํ•™ ํŠน์„ฑ์— ๋”ฐ๋ผ ์—ฐ๊ตฌ๋ชจํ˜•์ด ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๋ก ์„ ๋„์ถœํ•  ์ˆ˜ ์žˆ๋‹ค. ์ฒซ์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ๋“ค์˜ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์€ ์ƒ๋‹นํžˆ ๋‚ฎ์€ ์ˆ˜์ค€์ด๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ ๋‚˜ํƒ€๋‚œ ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ๋“ค์˜ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™ ์ˆ˜์ค€์€ ๋ณดํ†ต ์ดํ•˜์ด๋ฉฐ, ์ง์—…ํƒ์ƒ‰ ์˜์—ญ์ด ๋‚˜์— ๋Œ€ํ•œ ํƒ์ƒ‰ ์˜์—ญ๋ณด๋‹ค ์ƒ๋‹นํžˆ ๋‚ฎ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ์˜ ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ์ด ๋†’์„์ˆ˜๋ก ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์ด ์ด‰์ง„๋œ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ๋“ค์˜ ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ์€ ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์— ๊ฐ€์žฅ ํฐ ์˜ํ–ฅ๋ ฅ์„ ๊ฐ–๋Š” ๋ณ€์ธ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ํ•™๊ต ์ง„๋กœ์ง€์›์ด ๋†’์„์ˆ˜๋ก ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์ด ์ด‰์ง„๋œ๋‹ค. ์ฆ‰, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ๋“ค์€ ํ•™๊ต์—์„œ ์ธ์ง€๋˜๋Š” ์ง„๋กœ์ง€์›์˜ ์ˆ˜์ค€์ด ๋†’์„์ˆ˜๋ก ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์˜ ๋นˆ๋„๊ฐ€ ๋†’๋‹ค. ์ด๋Š” ์ง์ ‘์ ์ธ ๊ฒฝ๋กœ์™€ ๊ฐ„์ ‘์ ์ธ ๊ฒฝ๋กœ๋กœ ๋ชจ๋‘ ์„ค๋ช…๋œ๋‹ค. ๋„ท์งธ, ๋‹จ์ผํ•™๊ณผ๋กœ ๊ตฌ์„ฑ๋œ ๋‹จ๊ณผ๋Œ€ํ•™์— ๋น„ํ•ด ๋‹ค์ˆ˜ํ•™๊ณผ๋กœ ๊ตฌ์„ฑ๋œ ๋‹จ๊ณผ๋Œ€ํ•™์—์„œ ์ง„๋กœํƒ์ƒ‰ํšจ๋Šฅ๊ฐ์ด ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๋ ฅ์ด ๋” ์ปธ์œผ๋ฉฐ, ํ•™๊ต์ง„๋กœ์ง€์›์ด ์ง„๋กœํƒ์ƒ‰ํ–‰๋™์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ •๋„๋„ ๋” ์ปธ๋‹ค. ๋‹ค์„ฏ์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ๊ณ ํ•™๋…„ ํ•™์ƒ๋“ค์€ ์ €ํ•™๋…„ํ•™์ƒ๋“ค์— ๋น„ํ•ด ํ•™๊ต์˜ ์ง„๋กœ์ง€์› ์ˆ˜์ค€์„ ๋‚ฎ๊ฒŒ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ์ดˆ๋กœ ํ›„์† ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ œ์–ธ์„ ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ฒซ์งธ, ํ–ฅํ›„ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•™์ƒ๋“ค์˜ ์ธ์ง€๋œ ์ง„๋กœ์ง€์›์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฐœ์ธ์  ๋ณ€์ธ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค. ๋‘˜์งธ, ๋Œ€ํ•™์˜ ์ง„๋กœ์ง€์›์„œ๋น„์Šค์˜ ์งˆ์„ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋Š” ํ†ตํ•ฉ์ ์ธ ์ค€๊ฑฐ ๊ฐœ๋ฐœ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค. ์…‹์งธ, ๋ถ€๋ชจ์• ์ฐฉ๊ณผ ๋ถ€๋ชจ๋กœ๋ถ€ํ„ฐ์˜ ์‹ฌ๋ฆฌ์  ๋…๋ฆฝ์„ ๋™์‹œ์— ๊ณ ๋ คํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค. ๋„ท์งธ, ์„œ์šธ๋Œ€ํ•™๊ต ํ•™์ƒ๋“ค์˜ ์ง„๋กœํƒ์ƒ‰๊ณผ ์‹ค์ œ์ ์ธ ์ง„๋กœ์ง€์›์„ ๋„์šธ ์ˆ˜ ์žˆ๋Š” ์ง„๋กœ์ง€์›์ฒด๊ณ„๊ฐ€ ๊ตฌ์ถ•๋  ํ•„์š”์„ฑ์ด ์žˆ๋‹ค.The purpose of this study was to identify a causal relationship among career exploration-behavior, parent's attachment, career support from college, and career exploration self-efficacy of Seoul National University undergraduates. The specific objectives were to identify the effect of parent's attachment, career support from college, and career exploration self-efficacy on career exploration-behavior, to identify the moderate effect of career support from college and career exploration self-efficacy between parent's attachment and career exploration-behavior, and identify career exploration self-efficacy between parent's attachment and career exploration-behavior. The population of this study is the 16,325 students attending Seoul National University according to the 2010 record. The research carried out the proportional stratified sampling considering the ratio of the characteristics of college, grade and gender of the students who are attending university. Through this process it was sampled 400 students, 380 students from 11 different college. A survey questionnaire was conducted to measure variables of this study. It was consisted of career exploration-behavior scale, parent's attachment scale, career support from college scale, career exploration self-efficacy scale, and demographic items. In this study, the existing scales were used for career exploration-behavior, parent's attachment, and career support from college. career exploration-behavior was consisted of self-exploration and career-exploration. career exploration self-efficacy was consisted of career exploration self-efficacy, interview self-efficacy, relation self-efficacy, self exploration efficacy. parent's attachment was consisted of isolation, communication and trust. career support from college scale was developed to measure the levels of career support from college based on previous researches. The scale was consisted of informational support, practical support, and emotional support. Through pilot test and final survey, reliability of these scales were examined. The data were collected by mail and e-mail from October 4th to 12th 2011. A total of 400 out of 380 questionnaires were returned, of which 371 were used for analysis after data cleaning. Both descriptive and inferential statistics were employed for data analysis. To estimate parameters of proposed research model, covariance structure analysis were used. All data analysis was accomplished using the SPSS 18.0-Win statistics package, and AMOS 18.0 version. A alpha level of 5% was established prior for determining significance. The finding of the study were as follow: First, the fix indexed of causal model among career exploration-behavior, parent's attachment, career support from college, and career exploration self-efficacy were identified suitably. Second, factor loading of career exploration self-efficacy to career exploration-behavior was significant(ฮฒ=.579, p<0.01) also factor loading of career support from college was significant(ฮฒ=.196, p<0.01) however the factor loading of parent's attachment to career exploration-behavior was not significant. Third, in relationship between parent's attachment as well as career support from college and career exploration-behavior had moderating effect each as .152 and .142. Forth, the causal model has significant difference according to the characteristics of attending college. Based on the finding of the study, major conclusions of this study were as follows: First, the level of career exploration-behavior of Seoul National University undergraduates was under the average. Second, as the level of career exploration self-efficiency was higher, the career exploration behavior was promoted. third career support from college of Seoul National University undergraduates has direct and indirect effect on career exploration-behavior. Fourth, the characteristics of attending college has effect of causal relationships among career exploration-behavior, career exploration self-efficacy and career support from college. fifth, the higher grade undergraduates perceived the level of support from college less sufficient. Some recommendations for future researches were suggested: First, further research needs to investigate personal variables which have effects on the consciousness of career support from college. Second, integrative criteria for evaluating the quality of career support from college should develop. Third, future research should consider both attachment and psychological independence from parents. Fourth, the career support system which can assist undergraduates to explore career practically should be constructed.Maste

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2013. 8. ์ด๋™ํ˜ธ.๋ ˆ์ผ ์œ„๋ฅผ ์ฃผํ–‰ํ•˜๋Š” ๊ณ ์†์—ด์ฐจ์˜ ๊ฒฝ์šฐ ๊ฐ™์€ ํ˜•์ƒ์˜ ๋™๋ ฅ์ฐจ๊ฐ€ ๋™์‹œ์— ์•ž๊ณผ๋’ค ์ฐจ๋Ÿ‰์œผ๋กœ ๋ฐฉํ–ฅ๋งŒ ๋ฐ”๋€Œ์–ด์„œ ์‚ฌ์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ฒซ๋ฒˆ์งธ ์ฐจ๋Ÿ‰์˜ ์œ„์น˜์—๋งŒ ์ ํ•ฉํ•˜๊ฒŒ ์„ค๊ณ„๋œ ์ด์ „์˜ ์ตœ์  ์ „๋‘๋ถ€ ํ˜•์ƒ์€ ๋งˆ์ง€๋ง‰ ์ฐจ๋Ÿ‰์˜ ์œ„์น˜์— ์žˆ์„ ๋•Œ ์ „์ฒด ๊ณต๊ธฐ์ €ํ•ญ์˜ ๊ด€์ ์—์„œ ์ ํ•ฉํ•˜์ง€ ์•Š๋‹ค. ๋˜ํ•œ 3์ฐจ์› ํ˜•์ƒ ์ตœ์ ์„ค๊ณ„์— ์žˆ์–ด ์ •ํ™•ํ•œ ํ›„๋ฅ˜ ๋ชจ์‚ฌ๋Š” ๋งค์šฐ ์ค‘์š”ํ•œ ๋ถ€๋ถ„์ด์ง€๋งŒ, ์ด์ „ ์—ฐ๊ตฌ๋“ค์˜ ์—ด์ฐจ ํ›„๋ฅ˜ ๋ชจ์‚ฌ๋Š” ์—ด์ฐจ ํ˜•์ƒ๊ณผ ๊ฒฝ๊ณ„์กฐ๊ฑด์œผ๋กœ ์ธํ•ด ์ •ํ™•ํ•˜์ง€ ์•Š์•˜๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ „์ฒด ๊ณต๊ธฐ์ €ํ•ญ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ๊ด€์ ์—์„œ์˜ 3์ฐจ์› ์ „๋‘๋ถ€ ํ˜•์ƒ ์ตœ์ ์„ค๊ณ„๋Š” ์ „์ฒด ์ฐจ๋Ÿ‰ ํ˜•์ƒ์„ ๋ชจ๋‘ ๊ณ ๋ คํ•ด์•ผ ํ•˜๋ฉฐ, ํŠนํžˆ ํ›„๋ฏธ๋ถ€์—์„œ์˜ ํ›„๋ฅ˜ ์˜์—ญ์„ ์ œ๋Œ€๋กœ ๋ชจ์‚ฌํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ „์ฒด ๊ณต๊ธฐ์ €ํ•ญ์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ์ „ํ›„๋Œ€์นญ์—ด์ฐจ์˜ ์ „๋‘๋ถ€ 3์ฐจ์› ํ˜•์ƒ ์ตœ์ ์„ค๊ณ„๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ „์ฒด ๊ณต๊ธฐ์ €ํ•ญ์„ ์ค„์ด๋Š” ๋น„์ œ์•ฝ ์ตœ์ ์„ค๊ณ„์™€ ์ „์ฒด๊ณต๊ธฐ์ €ํ•ญ๊ณผ ๋ฏธ๊ธฐ์••ํŒŒ๋ฅผ ๋ชจ๋‘ ์ €๊ฐํ•˜๊ธฐ ์œ„ํ•ด ๋ฏธ๊ธฐ์••ํŒŒ๋ฅผ ์ตœ์†Œํ™”ํ•˜๋Š” ์ „๋‘๋ถ€ ๋‹จ๋ฉด์  ๋ถ„ํฌ๋ฅผ ์ œ์•ฝ์กฐ๊ฑด์œผ๋กœ ๊ฐ€์ง€๋ฉด์„œ ์ „์ฒด๊ณต๊ธฐ์ €ํ•ญ์„ ์ €๊ฐํ•˜๋Š” ์ œ์•ฝ๋ชจ๋ธ ์ตœ์ ์„ค๊ณ„๋ฅผ ๊ฐ๊ฐ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. Vehicle Modeling Function์„ ์ด์šฉํ•˜์—ฌ 3์ฐจ์› ์—ด์ฐจ ํ˜•์ƒ์„ ๊ตฌ์„ฑํ•˜์˜€๊ณ , Navier-Stokes ๋ฐฉ์ •์‹๊ณผ ๋น„์ •๋ ฌ๊ฒฉ์ž๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ทธ ๊ณต๊ธฐ์ €ํ•ญ์„ ์˜ˆ์ธกํ•˜์˜€๋‹ค. ์ „์ฒด๊ณต๊ธฐ์ €ํ•ญ์„ ๊ณ ๋ คํ•œ ๊ฒฝ์šฐ์™€ ์ฒซ๋ฒˆ์งธ ์ฐจ๋Ÿ‰์˜ ๊ณต๊ธฐ์ €ํ•ญ๋งŒ์„ ๊ณ ๋ คํ•œ ๊ฒฝ์šฐ์˜ ์ตœ์ ์„ค๊ณ„๋ฅผ ๋น„์ œ์•ฝ ์ตœ์ ์„ค๊ณ„์™€ ์ œ์•ฝ๋ชจ๋ธ ์ตœ์ ์„ค๊ณ„ ๋ชจ๋‘์˜ ๊ฒฝ์šฐ์— ๊ฐ๊ฐ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. Maxi-min Latin Hypercube Sampling ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์ถ”์ถœํ•œ ์‹คํ—˜์ ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ธ๊ณต์‹ ๊ฒฝ๋ง์„ ๊ตฌ์„ฑํ•˜์—ฌ ์ตœ์ ์„ค๊ณ„์— ์ด์šฉํ•˜์˜€๋‹ค. ๋น„์ œ์•ฝ ์ตœ์ ์„ค๊ณ„์˜ ๊ฒฝ์šฐ, ๋ฒ ์ด์Šคํ˜•์ƒ๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ, ์ „์ฒด๊ณต๊ธฐ์ €ํ•ญ์„ ๊ณ ๋ คํ•œ ์ „๋‘๋ถ€ ํ˜•์ƒ์„ ์ ์šฉํ•œ ์—ด์ฐจ๋ชจ๋ธ์€ ์ „์ฒด๊ณต๊ธฐ์ €ํ•ญ์ด ์•ฝ 5.8% ๊ฐ์†Œํ•˜์˜€๊ณ , ์ฒซ๋ฒˆ์งธ ์ฐจ๋Ÿ‰์˜ ๊ณต๊ธฐ์ €ํ•ญ๋งŒ ๊ณ ๋ คํ•œ ์ „๋‘๋ถ€ ํ˜•์ƒ์„ ์ ์šฉํ•œ ์—ด์ฐจ ๋ชจ๋ธ์€ ์ „์ฒด๊ณต๊ธฐ์ €ํ•ญ์˜ ๊ฐ์†Œ๊ฐ€ ๋ฏธ๋ฏธํ•˜์˜€๋‹ค. ์ œ์•ฝ๋ชจ๋ธ ์ตœ์ ์„ค๊ณ„์˜ ๊ฒฝ์šฐ, ๋ฒ ์ด์Šคํ˜•์ƒ๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ, ์ „์ฒด๊ณต๊ธฐ์ €ํ•ญ์„ ๊ณ ๋ คํ•œ ์ „๋‘๋ถ€ ํ˜•์ƒ์„ ์ ์šฉํ•œ ์—ด์ฐจ๋ชจ๋ธ์€ ์ „์ฒด๊ณต๊ธฐ์ €ํ•ญ์ด ์•ฝ 15.3% ๊ฐ์†Œํ•˜์˜€๊ณ , ์ฒซ๋ฒˆ์งธ ์ฐจ๋Ÿ‰์˜ ๊ณต๊ธฐ์ €ํ•ญ๋งŒ ๊ณ ๋ คํ•œ ์ „๋‘๋ถ€ ํ˜•์ƒ์„ ์ ์šฉํ•œ ์—ด์ฐจ ๋ชจ๋ธ์€ ์ „์ฒด๊ณต๊ธฐ์ €ํ•ญ์ด ์˜คํžˆ๋ ค 9.7% ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๋น„์ œ์•ฝ์„ค๊ณ„ ์ตœ์  ์ „๋‘๋ถ€ ํ˜•์ƒ์˜ ๋‚ฎ๊ฒŒ ๊น”๋ฆฌ๋ฉด์„œ ์„ธ๋กœ๋กœ ๊ธด ํ˜•์ƒํŠน์ง•์€ ์—ด์ฐจ ๋’ค์ชฝ ์ „๋‘๋ถ€ ๋๋‹จ ๊ทผ์ฒ˜์˜ ํšŒ์ „์œ ๋™์„ ์•ฝํ™”์‹œํ‚จ๋‹ค. ๋ฐ˜๋ฉด์— ์ œ์•ฝ๋ชจ๋ธ๊ธฐ๋ฐ˜ ์ตœ์  ์ „๋‘๋ถ€ ํ˜•์ƒ์˜ ๋‚ฎ๊ฒŒ ๊น”๋ฆฌ๋ฉด์„œ ๊ฐ€๋กœ๋กœ ๊ธด ํ˜•์ƒ ํŠน์ง•์€ ์—ด์ฐจ ๋’ค์ชฝ ์ „๋‘๋ถ€ ์•„๋žซ๋ฉด์—์„œ ์˜ฌ๋ผ์˜ค๋Š” ์œ ๋™๊ณผ ๊ทธ๋กœ ์ธํ•ด ํ˜•์„ฑ๋˜๋Š” ์™€๋ฅ˜๋ฅผ ์•ฝํ™”์‹œํ‚จ๋‹ค. ์ด๋Ÿฐ ๋‘ ์ตœ์ ํ˜•์ƒ์˜ ํ˜•์ƒํŠน์ง•๋“ค์ด ์ „์ฒด ๊ณต๊ธฐ์ €ํ•ญ์„ ๊ฐ์†Œ์‹œํ‚จ๋‹ค. ์‹ค์ œ ์ „ํ›„๋Œ€์นญ์—ด์ฐจ์˜ ์ตœ์ ์„ค๊ณ„๋ฅผ ์œ„ํ•ด์„œ ํ›„๋ฅ˜ ์˜์—ญ์ด ์ •ํ™•ํ•˜๊ฒŒ ๋ชจ์‚ฌ๋˜์–ด์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— 3์ฐจ์› ํ˜•์ƒ ๋ชจ๋ธ๋ง์€ ํ•„์ˆ˜์ ์ด๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ Vehicle Modeling Function์€ 3์ฐจ์›ํ˜•์ƒ์„ ํ•จ์ˆ˜ํ™”ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ํ˜•์ƒ์„ ํ‘œํ˜„ํ•˜๋Š”๋ฐ ์ œ์•ฝ์ด ์—†๊ธฐ ๋•Œ๋ฌธ์— ํ˜•์ƒ์ œ์•ฝ์กฐ๊ฑด์˜ ์œ ๋ฌด์— ๊ด€๊ณ„์—†์ด ์„ฑ๊ณต์ ์ธ 3์ฐจ์› ํ˜•์ƒ ์ตœ์ ์„ค๊ณ„๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ๊ฐ€์น˜์žˆ๋Š” ๋„๊ตฌ์ด๋‹ค. ๋˜ํ•œ ์ „์ฒด ๊ณต๊ธฐ์ €ํ•ญ์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ํšจ๊ณผ์ ์ธ ์ตœ์ ์„ค๊ณ„๋ฅผ ์œ„ํ•ด์„œ๋Š” ์•ž๋’ค์—์„œ ๋ฐฉํ–ฅ๋งŒ ๋‹ค๋ฅธ ๊ฐ™์€ ์ „๋‘๋ถ€ ํ˜•์ƒ์˜ ์–‘๋ฐฉํ–ฅ ์ฃผํ–‰์„ ๋ชจ๋‘ ๊ณ ๋ คํ•˜๋Š” ๊ฒƒ์ด ๊ผญ ํ•„์š”ํ•˜๋‹ค.A high-speed train uses two symmetrically corresponding shaped power cars at both ends. Consequently, the same nose shape plays a role as a leading part and a role as a trailing part in one train at the same time. Thus the existing model of the optimized first car nose shape which does not consider the entire train is not sound in terms of the aerodynamic drag. Also, while accurate simulation of the wake area behind the train is very significant for the design optimization of the three-dimensional shape, accuracy of previous studies has been limited by their train shapes and boundary conditions. Therefore, it is necessary to consider the entire train including the first car nose and the last car nose and especially accurate simulation of the wake area for the optimization of the shape design of a three-dimensional symmetric train in order to reduce the total aerodynamic drag. In this dissertation, two nose shape optimizations of the front-rear symmetric train are performed with no constraint for the reduction of the total aerodynamic drag and with the constraint of the optimized cross-sectional area distribution for the reduction of the total aerodynamic drag and the micro-pressure wave respectively. The three-dimensional train nose shape is constructed through Vehicle Modeling Function and a viscous compressible flow solver is adopted with unstructured meshes to predict the aerodynamic drag. The two optimizations are respectively performed under consideration of two cases โ€“ for the total aerodynamic drag of the entire train and for the aerodynamic drag of the first car only by the previous method for the reduction of design time. Also, an Artificial Neural Network is constructed with the experimental points extracted by Maxi-min Latin Hypercube Sampling method. In the unconstrained optimization, it was found that the total aerodynamic drag of the entire train with the optimized shape for the entire train was reduced by 5.8% when compared to the unconstrained base model, whereas that with the optimized shape for only the first car is changed little. On the other hand, in the constrained optimization, the total aerodynamic drag of the entire train with the optimized shape for the entire train was effectively reduced by 15.3 % when compared to that of the constrained base model while that with the optimized shape for only the first car is increased by 9.7% on the contrary. The low-risen and long vertical nose shape of the unconstrained optimum weakens the whirled flow around the nose tip. On the other hand, the low-risen and wide horizontal nose shape of the constrained optimum weakens the up-wash flow and vortices behind the blunt nose. Both shape characteristics reduce the overall aerodynamic drag of each base model. Therefore, the three dimensional modeling is very necessary for design optimization of the actual front-rear symmetric train in that the wake area behind the train must be simulated as accurately as possible. In doing so, Vehicle Modeling Function is a valuable tool in successful three-dimensional shape optimization since it has no modeling constraint to functionalize three-dimensional shape thus efficiently enables the various models of the three-dimensional train shape. Also, it is required to design symmetrically identical both noses in order to reduce the total aerodynamic drag.Abstract I Nomenclature V List of Tables VI List of Figures VII Chapter 1. Introduction 1 1.1 Aerodynamics of a High-Speed Train 1 1.2 Effect of External Shapes on Train Aerodynamics 3 1.3 Previous Research for Train Nose Shapes 4 1.4 Dissertation Objectives and Outlines 6 Chapter 2. Methodology 12 2.1 Numerical Method 12 2.1.1 Grid Generation for CFD Analysis 12 2.1.2 Methodology for CFD Analysis 12 2.1.3 Validation of the CFD Method 14 2.2 Shape Modeling 15 2.2.1 Train Model 15 2.2.2 Vehicle Modeling Function 15 2.3 Design Optimization Method 23 2.3.1 Design of Experiments (DOE) โ€“ Maximin Latin Hypercube Sampling 23 2.3.2 Design Space Approximation - Artificial Neural Network 23 Chapter 3. Nose Optimization with Unconstrained Train Model 35 3.1 Design Problem Formulation 35 3.2 Different Aerodynamic Effects of One Same Nose on the First Car and on the Last Car 38 3.3 Comparison of the Optimized Model for Entire Train and the Previously Optimized Model 40 Chapter 4. Nose Optimization with the Constrained Train Model 55 4.1 Design Problem Formulation 55 4.2 Different Aerodynamic Effects of One Same Nose on the First Car and on the Last Car 59 4.3 Comparison of the Optimized Model for Entire Train and the Previously Optimized Model 62 4.4 Comparison of the Unconstrained Optimum Model and the Constrained Optimum Model 66 Chapter 5. Conclusion 93 References 95 ์ดˆ ๋ก โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 103Docto

    Analysis of Students Open-Ended Course Evaluation Using Topic Modeling

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    ์ด ์—ฐ๊ตฌ๋Š” ํ† ํ”ฝ ๋ชจ๋ธ๋ง(topic modeling)์˜ ์ผ์ข…์ธ ์ž ์žฌ ๋””๋ฆฌํด๋ ˆ ํ• ๋‹น(latent Dirichlet allocation, ์ดํ•˜ LDA)์„ ํ™œ์šฉํ•˜์—ฌ S๋Œ€ํ•™๊ต์˜ ํ•™์ƒ๋“ค์ด ์ž‘์„ฑํ•œ ๊ฐ•์˜ํ‰๊ฐ€ ์‘๋‹ต์„ ๋ถ„์„ํ•จ์œผ๋กœ์จ ํ•™์ƒ๋“ค์ด ๊ฐ–๊ณ  ์žˆ๋Š” ๊ฐ•์˜์— ๋Œ€ํ•œ ์ƒ๊ฐ์„ ๋ณด๋‹ค ์ง์ ‘์ ์œผ๋กœ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด 2015๋…„ 1ํ•™๊ธฐ์— ๊ฐœ์„ค๋œ ์•ฝ 1,500๊ฐœ ๊ฐ•์˜์— ๋Œ€ํ•ด ํ•™์ƒ๋“ค์ด ๊ฐ•์˜์—์„œ ๊ฐœ์„  ๋˜์–ด์•ผ ํ•  ์ ๊ณผ ๊ฐ•์˜์—์„œ ์ข‹์•˜๋˜ ์ ์— ๋Œ€ํ•ด ์„œ์ˆ ํ•œ ์•ฝ 47,000๊ฐœ์˜ ์‘๋‹ต ๋‚ด์šฉ์„ LDA๋ฅผ ํ™œ์šฉํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ์•„์šธ๋Ÿฌ, 6๊ฐœ์˜ ๋‹จ๊ณผ๋Œ€ํ•™(๊ณต๊ณผ๋Œ€ํ•™, ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™, ์‚ฌ๋ฒ”๋Œ€ํ•™, ์ธ๋ฌธ๋Œ€ํ•™, ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™, ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™) ๊ฐ•์˜์˜ ๊ฐœ์„ ๋˜์–ด์•ผํ•  ์ , ์ข‹์•˜๋˜ ์ ์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ์ฒซ์งธ, ๊ฐ•์˜์—์„œ ๊ฐœ์„ ๋˜์–ด์•ผ ํ•  ์ ๊ณผ ๊ฐ•์˜์—์„œ ์ข‹์•˜๋˜ ์  ๋ชจ๋‘ 3๊ฐœ ์ฃผ์ œ ๋ชจํ˜•์ด ๊ฐ€์žฅ ์ ํ•ฉํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋จผ์ €, ๊ฐ•์˜์—์„œ ๊ฐœ์„ ๋˜์–ด์•ผ ํ•  ์ ์€ 1) ๊ณผ์ œยท์‹คํ—˜ยท์‹ค์Šต์— ๋Œ€ํ•œ ๊ฐœ์„ ์‚ฌํ•ญ, 2) ๋ฐœํ‘œยทํ† ๋ก ์— ๋Œ€ํ•œ ๊ฐœ์„ ์‚ฌํ•ญ, 3) ์‹œํ—˜ยท์ง„๋„ยท์ˆ˜์—…๋‚ด์šฉ์— ๋Œ€ํ•œ ๊ฐœ์„ ์‚ฌํ•ญ์˜ ์„ธ ๊ฐ€์ง€ ์ฃผ์ œ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ๊ฐ•์˜์—์„œ ์ข‹์•˜๋˜ ์ ์€ 1) ๊ต์ˆ˜์žยท๊ต์ˆ˜ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ๊ธ์ •์  ํ”ผ๋“œ๋ฐฑ, 2) ์ง์ ‘์  ๊ฒฝํ—˜ยท์‹ค์Šต์— ๋Œ€ํ•œ ๊ธ์ •์  ํ”ผ๋“œ๋ฐฑ, 3) ๊ฐ•์˜๋‚ด์šฉ์— ๋Œ€ํ•œ ๊ธ์ •์  ํ”ผ๋“œ๋ฐฑ์˜ ์„ธ ๊ฐ€์ง€ ์ฃผ์ œ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ๋‹จ๊ณผ๋Œ€ํ•™๋ณ„ ๋ถ„์„ ๊ฒฐ๊ณผ, ๋‹จ๊ณผ๋Œ€ํ•™๋ณ„๋กœ ๋‚˜ํƒ€๋‚œ ์ฃผ์ œ์˜ ์˜๋ฏธ๋Š” ๋Œ€์ฒด์ ์œผ๋กœ ์ „์ฒด ๋Œ€ํ•™ ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ–ˆ์„ ๋•Œ์™€ ๋น„์Šทํ–ˆ์œผ๋‚˜, ํ•˜๋‚˜์˜ ์ฃผ์ œ ์ •๋„๊ฐ€ ๋‹จ๊ณผ๋Œ€ํ•™์˜ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ฐ•์˜ํ‰๊ฐ€์˜ ์„ ํƒํ˜• ๋ฌธํ•ญ ๋ถ„์„์— ์น˜์ค‘ํ•˜์˜€๋˜ ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ๋‹ฌ๋ฆฌ, ํ† ํ”ฝ ๋ชจ๋ธ๋ง์„ ํ™œ์šฉํ•จ์œผ๋กœ์จ ๋Œ€๋Ÿ‰์˜ ์„œ์ˆ ํ˜• ๊ฐ•์˜ํ‰๊ฐ€ ์ž๋ฃŒ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์š”์•ฝํ•˜์˜€์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๊ฐ•์˜ ์ „๋ฐ˜์— ๋Œ€ํ•œ ํ•™์ƒ๋“ค์˜ ์ธ์‹์„ ๋ณด๋‹ค ์ง์ ‘์ ์ด๊ณ  ์ข…ํ•ฉ์  ์œผ๋กœ ์‚ดํŽด๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค๋Š” ์˜์˜๋ฅผ ๊ฐ–๋Š”๋‹ค
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