평생직업교육학원 시설의 분포 특성과 입지 요인 분석

• 영문명: The Spatial Characteristics of Life-long Vocational Education and Training Facilities and the Location of Determinants
• 발행기관: 한국지역개발학회
• 저자명: 박소현(Park, Sohyun) 이금숙(Lee, Keumsook)
• 간행물 정보: 『한국지역개발학회지』제32권 제3호, 123~146쪽, 전체 24쪽
• 주제분류: 사회과학 > 지역개발
• 파일형태: PDF
• 발행일자: 2020.09.30

영문 초록

In the new normal era, as the importance of life-long vocational competency development activities increased, the demand for life-long vocational education of adults, especially for vocational education and training, increased remarkably. Therefore, this study conducts an empirical analysis with the aim of estimating the socio-economic factors affecting the locational distribution and spatial characteristics by type of non-formal life-long vocational education and training facilities. At the results of spatial empirical analysis, due to the nature of market-oriented private institute services, the location of all life-long vocational education and training facilities are concentrated at major hotspots in Seoul city. In particular, occupational skill of life-long vocational education and training facilities are concentrated in major business-commercial-residence mixed areas have a significant relationship with highly educated professionals. The Gangnam area is analyzed as a place where private tutoring facilities are clustered for all age group participants. At the public level, for reducing the gap in geographical accessibility of participants in regional distribution differences in the life-long vocational education and training facilities, it is necessary to operate and provide various programs that reflect the age, education level, and job distribution of participants. It will be possible to establish a life-long vocational education system in closely related the region.

목차

1. 서 론

2. 이론적 고찰

3. 평생직업교육학원 분포 현황 및 입지 패턴

4. 평생직업교육학원 입지분포 요인

5. 결 론

참고문헌

키워드

비형식 평생교육기관 평생직업교육학원 커널밀도추정 최근린 지수 공간통계모형 Non-formal lifelong education institutes Life-long vocational education and training institute Kernel density estimation Nearest neighbor ratio Spatial regression models