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    하이브리드 머신러닝 접근에 의한영유아교사의 포괄적 행복척도 단축형 개발 = Developing a short version of the Comprehensive Happiness Scale for early childhood teachers: A hybrid machine learning approach

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    https://www.riss.kr/link?id=A109264841

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    Objective: This study develops and validates a shortened version of the Comprehensive Happiness Scale for early childhood teachers using a hybrid machine learning approach.
    Methods: Secondary data on 212 early childhood teachers were used to develop and validate a comprehensive 53-item happiness scale. First, the full 53-item data were fed through unsupervised learning to stratify the latent population for item responses. Next, candidate shortened versions with different item counts, (3-25 items), were created to investigate concurrent validity. The shortened instruments’ validity was compared using Kappa coefficients and AUC values calculated by supervised learning.
    Results: First, after inputting early childhood teachers’ response data on all 53 items, the entire sample (N = 212, m = 3.59, sd = 0.40) was stratified into three latent groups through unsupervised learning. These groups were identified to have lower overall composite happiness, with particularly low levels of “satisfaction and engagement with the organization” and “leisure and physical well-being” (N = 52, m = 3.11, sd = 0.18), higher overall composite happiness with particularly high levels of “psychological well-being” (N = 50, m = 4.11, sd = 0.25); and an intermediate group (N = 110, m = 3.58, sd = 0.17) similar to the total population mean and distributed between the other two groups' composite happiness levels. Second, the shortened 17-item instrument was adequate to categorize the low (Kappa = 0.98, AUC = 1.00), high (Kappa = 0.98, AUC = 1.00), and intermediate (Kappa = 0.85, AUC = 0.98) groups in terms of comprehensive happiness.
    Conclusions: A shortened 17-item instrument was developed to measure early childhood teachers’ comprehensive happiness. This shortened instrument can be used to more easily and accurately measure the happiness level of infant and toddler teachers and support their comprehensive happiness.
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    Objective: This study develops and validates a shortened version of the Comprehensive Happiness Scale for early childhood teachers using a hybrid machine learning approach. Methods: Secondary data on 212 early childhood teachers were used to develop a...

    Objective: This study develops and validates a shortened version of the Comprehensive Happiness Scale for early childhood teachers using a hybrid machine learning approach.
    Methods: Secondary data on 212 early childhood teachers were used to develop and validate a comprehensive 53-item happiness scale. First, the full 53-item data were fed through unsupervised learning to stratify the latent population for item responses. Next, candidate shortened versions with different item counts, (3-25 items), were created to investigate concurrent validity. The shortened instruments’ validity was compared using Kappa coefficients and AUC values calculated by supervised learning.
    Results: First, after inputting early childhood teachers’ response data on all 53 items, the entire sample (N = 212, m = 3.59, sd = 0.40) was stratified into three latent groups through unsupervised learning. These groups were identified to have lower overall composite happiness, with particularly low levels of “satisfaction and engagement with the organization” and “leisure and physical well-being” (N = 52, m = 3.11, sd = 0.18), higher overall composite happiness with particularly high levels of “psychological well-being” (N = 50, m = 4.11, sd = 0.25); and an intermediate group (N = 110, m = 3.58, sd = 0.17) similar to the total population mean and distributed between the other two groups' composite happiness levels. Second, the shortened 17-item instrument was adequate to categorize the low (Kappa = 0.98, AUC = 1.00), high (Kappa = 0.98, AUC = 1.00), and intermediate (Kappa = 0.85, AUC = 0.98) groups in terms of comprehensive happiness.
    Conclusions: A shortened 17-item instrument was developed to measure early childhood teachers’ comprehensive happiness. This shortened instrument can be used to more easily and accurately measure the happiness level of infant and toddler teachers and support their comprehensive happiness.

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    참고문헌 (Reference)

    1 최재혁 ; 정목동, "혼합 기계 학습 기반소변 스펙트럼 분석 앙상블 모델" 23 (23): 1059-1065, 2020

    2 김흥길 ; 성연옥, "조직웰빙(OWB)의 개념화와 척도 개발에 관한 연구" 17 (17): 325-348, 2010

    3 도순남, "유치원 교사의 직무스트레스, 심리적 특성 및 인구통계학적 특성이 직무태도에 미치는 효과분석" 경상대학교대학원 1999

    4 김인선 ; 남효순 ; 이유미, "유아교사의 행복증진을 위한 교사교육 프로그램 개발" 8 (8): 123-142, 2017

    5 황해익 ; 탁정화, "유아교사의 자아존중감과 행복플로리시의 관계에서 향유신념의 조절효과" 13 (13): 153-178, 2014

    6 량양링 ; 김미숙, "유아교사의 심리적 웰빙이 교사-유아 상호작용에 미치는 영향 : 직업적 웰빙의 매개효과" 141 : 111-140, 2023

    7 임승렬 ; 김정림 ; 전방실, "영유아교사의 행복 측정척도 개발 및 타당화 연구" 18 (18): 405-431, 2014

    8 전효정 ; 고은경, "영유아교사의 포괄적행복 요인에 대한요구도 분석" 19 (19): 129-155, 2015

    9 전효정 ; 류미향 ; 고은경, "영유아교사 포괄적 행복척도 개발 연구" 15 (15): 517-545, 2015

    10 김윤지 ; 오선미 ; 이윤수 ; 이지영, "사립유치원 교사의 행복에 대한 인식 탐색 : 개념도 분석을 중심으로" 26 (26): 307-333, 2024

    1 최재혁 ; 정목동, "혼합 기계 학습 기반소변 스펙트럼 분석 앙상블 모델" 23 (23): 1059-1065, 2020

    2 김흥길 ; 성연옥, "조직웰빙(OWB)의 개념화와 척도 개발에 관한 연구" 17 (17): 325-348, 2010

    3 도순남, "유치원 교사의 직무스트레스, 심리적 특성 및 인구통계학적 특성이 직무태도에 미치는 효과분석" 경상대학교대학원 1999

    4 김인선 ; 남효순 ; 이유미, "유아교사의 행복증진을 위한 교사교육 프로그램 개발" 8 (8): 123-142, 2017

    5 황해익 ; 탁정화, "유아교사의 자아존중감과 행복플로리시의 관계에서 향유신념의 조절효과" 13 (13): 153-178, 2014

    6 량양링 ; 김미숙, "유아교사의 심리적 웰빙이 교사-유아 상호작용에 미치는 영향 : 직업적 웰빙의 매개효과" 141 : 111-140, 2023

    7 임승렬 ; 김정림 ; 전방실, "영유아교사의 행복 측정척도 개발 및 타당화 연구" 18 (18): 405-431, 2014

    8 전효정 ; 고은경, "영유아교사의 포괄적행복 요인에 대한요구도 분석" 19 (19): 129-155, 2015

    9 전효정 ; 류미향 ; 고은경, "영유아교사 포괄적 행복척도 개발 연구" 15 (15): 517-545, 2015

    10 김윤지 ; 오선미 ; 이윤수 ; 이지영, "사립유치원 교사의 행복에 대한 인식 탐색 : 개념도 분석을 중심으로" 26 (26): 307-333, 2024

    11 황종귀, "보육교사의 행복감, 자기효능감과 교사-유아의 상호작용 간의 관계" 21 (21): 7-25, 2017

    12 구은미, "보육교사의 신체적·정신적 건강상태와 소진에 대한 연구" 15 (15): 117-139, 2011

    13 김혜금, "보육교사의 건강상태, 건강행동과 보육의 질" 7 (7): 149-166, 2011

    14 최영옥, "교사의 다차원적 완벽주의 성향에 따른 사회적 문제해결능력, 직무스트레스 및 행복 간의 관계분석" 경성대학교 대학원 2011

    15 공경애, "검사법 평가 : 검사법 비교와 신뢰도 평가" 40 (40): 9-16, 2017

    16 김일옥 ; 정구철, "가정보육시설 보육교사의 건강상태가 이직의도에 미치는 영향-자기 효능감의 매개효과를 중심으로-" 68 : 147-169, 2011

    17 Fritz, C., "Work and sleep: Research insights for the workplace" Oxford University Press 55-76, 2016

    18 Van Katwyk, P. T., "Using the Job-related Affective Well-being Scale(JAWS)to investigate affective responses to work stressors" 5 : 219-230, 2000

    19 Sahdra, B. K., "Using genetic algorithms in a large nationally representative American sample to abbreviate the Multidimensional Experiential Avoidance Questionnaire" 7 : 189-, 2016

    20 Wall, D. P., "Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism" 7 (7): e43855-, 2012

    21 Riff, C. D., "The structure of psychologic well-being revisited" 69 (69): 719-727, 1995

    22 Sonnentag, S., "The recovery paradox : Portraying the complex interplay between job stressors, lack of recovery, and poor well-being" 38 : 169-185, 2018

    23 Diener, E., "The Satisfaction with Life Scale" 49 (49): 71-75, 1985

    24 Sonnentag, S., "The Recovery Experience Questionnaire : Development and validation of a measure for assessing recuperation and unwinding from work" 12 (12): 204-221, 2007

    25 Hills, P., "The Oxford Happiness Questionnaire : A compact scale for the measurement of psychological well-being" 33 (33): 1071-1082, 2002

    26 Morgado, F. F., "Scale development : ten main limitations and recommendations to improve future research practices" 30 (30): 3-, 2017

    27 Abbas, H., "Machine learning approach for early detection of autism by combining questionnaire and home video screening" 25 (25): 1000-1007, 2018

    28 Alshmemri, M., "Herzberg’s two-factor theory" 14 (14): 12-16, 2017

    29 Deci, E. L., "Facilitating optimal motivation and psychological well-being across life's domains" 49 (49): 14-23, 2008

    30 Simundic, A. M., "Diagnostic accuracy-part 1 : Basic concepts sensitivity and specificity, ROC Analysis, STARD Statement" 11 (11): 6-8, 2012

    31 Khan, A., "Development of a three tiered cognitive hybrid machine learning algorithm for effective diagnosis of Alzheimer’s disease" 34 (34): 8000-8018, 2022

    32 Wang, I., "Development of a Berg Balance Scale short-form using a machine learning approach in patients with stroke" 47 (47): 44-51, 2023

    33 Thabtah, F., "A new computational intelligence approach to detect autistic features for autism screening" 117 : 112-124, 2018

    34 Lin, S., "A hybrid machine learning model of depression estimation in home-based older adults : a 7-year follow-up study" 22 (22): 1-13, 2022

    35 Momenzadeh, N., "A hybrid machine learning approach for predicting survival of patients with prostate cancer : A SEER-based population study" 27 (27): 100763-, 2021

    36 Cohen, J., "A coefficient of agreement for nominal scales" 20 (20): 37-46, 1960

    37 Allen, N. J., "A Three-Component Conceptualization of Organizational Commitment" 1 (1): 61-89, 1991

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