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    가상현실 기반 건설안전교육에서 개인특성이 학습성과에 미치는 영향 - 머신러닝과 SHAP을 활용하여 - = Impact of personal characteristics on learning performance in virtual reality-based construction safety training - Using machine learning and SHAP -

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    https://www.riss.kr/link?id=A108846595

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    To address the high accident rate in the construction industry, there is a growing interest in implementing virtual reality (VR)-based construction safety training. However, existing training approaches often failed to consider learners' individual characteristics, resulting in inadequate training for some individuals. This study aimed to investigate the impact of personal characteristics on learning performance in VR-based construction safety training using machine learning and SHAP (SHAPley Additional exPlanations). This study revealed that age exerted the greatest influence on learning performance, while work experience had the least impact. Furthermore, age exhibited a negative relationship with learning performance, indicating that the introduction of VR-based construction safety training can be effective for younger individuals. On the other hand, academic degree, qualifications, and work experience exhibited a positive relationship. To enhance learning performance for individuals with lower academic degree, it is necessary to provide content that is easier to understand. The lower qualifications and work experience have minimal impact on learning performance, so it is important to consider other learners' characteristics so as to provide appropriate educational content. This study confirmed that personal characteristics can significantly affect learning performance in VR-based construction safety training, highlighting the potential for leveraging these findings to provide effective safety training for construction workers.
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    To address the high accident rate in the construction industry, there is a growing interest in implementing virtual reality (VR)-based construction safety training. However, existing training approaches often failed to consider learners' individual ch...

    To address the high accident rate in the construction industry, there is a growing interest in implementing virtual reality (VR)-based construction safety training. However, existing training approaches often failed to consider learners' individual characteristics, resulting in inadequate training for some individuals. This study aimed to investigate the impact of personal characteristics on learning performance in VR-based construction safety training using machine learning and SHAP (SHAPley Additional exPlanations). This study revealed that age exerted the greatest influence on learning performance, while work experience had the least impact. Furthermore, age exhibited a negative relationship with learning performance, indicating that the introduction of VR-based construction safety training can be effective for younger individuals. On the other hand, academic degree, qualifications, and work experience exhibited a positive relationship. To enhance learning performance for individuals with lower academic degree, it is necessary to provide content that is easier to understand. The lower qualifications and work experience have minimal impact on learning performance, so it is important to consider other learners' characteristics so as to provide appropriate educational content. This study confirmed that personal characteristics can significantly affect learning performance in VR-based construction safety training, highlighting the potential for leveraging these findings to provide effective safety training for construction workers.

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    참고문헌 (Reference)

    1 신기남 ; 주선우 ; 양상현, "건설업 기초안전보건교육 현황 및 개선에 관한 연구 : 교육 내용 및 방법을 중심으로" 한국산학기술학회 16 (16): 3484-3490, 2015

    2 박현수 ; 구충완, "가상현실 기반 건설안전교육이 건설근로자의 학습효과에 미치는 영향 - CAMIL 이론을 활용하여 -" 한국건설관리학회 23 (23): 104-115, 2022

    3 Chen, T., "Xgboost: A scalable tree boosting system" 785-794, 2016

    4 Cortes, C., "Support-vector networks" 20 : 273-297, 1995

    5 Park, S.W., "Suggestions on Remedies for Basic Safety and Health Education and New Hire Education in the Construction Industry" Kyung Hee University 2017

    6 Loosemore, M., "Safety training and positive safety attitude formation in the Australian construction industry" 113 : 233-243, 2019

    7 Feng, Y., "Risk compensation behaviours in construction workers’ activities" 22 (22): 40-47, 2015

    8 Liu, H., "Risk Perception and Coping Behavior of Construction Workers on Occupational Health Risks—A Case Study of Nanjing, China" 18 (18): 7040-, 2021

    9 Hoerl, A. E., "Ridge regression: applications to nonorthogonal problems" 12 (12): 69-82, 1970

    10 Thevaraja, M., "Recent developments in data science: Comparing linear, ridge and lasso regressions techniques using wine data" 1 : 1-6, 2019

    1 신기남 ; 주선우 ; 양상현, "건설업 기초안전보건교육 현황 및 개선에 관한 연구 : 교육 내용 및 방법을 중심으로" 한국산학기술학회 16 (16): 3484-3490, 2015

    2 박현수 ; 구충완, "가상현실 기반 건설안전교육이 건설근로자의 학습효과에 미치는 영향 - CAMIL 이론을 활용하여 -" 한국건설관리학회 23 (23): 104-115, 2022

    3 Chen, T., "Xgboost: A scalable tree boosting system" 785-794, 2016

    4 Cortes, C., "Support-vector networks" 20 : 273-297, 1995

    5 Park, S.W., "Suggestions on Remedies for Basic Safety and Health Education and New Hire Education in the Construction Industry" Kyung Hee University 2017

    6 Loosemore, M., "Safety training and positive safety attitude formation in the Australian construction industry" 113 : 233-243, 2019

    7 Feng, Y., "Risk compensation behaviours in construction workers’ activities" 22 (22): 40-47, 2015

    8 Liu, H., "Risk Perception and Coping Behavior of Construction Workers on Occupational Health Risks—A Case Study of Nanjing, China" 18 (18): 7040-, 2021

    9 Hoerl, A. E., "Ridge regression: applications to nonorthogonal problems" 12 (12): 69-82, 1970

    10 Thevaraja, M., "Recent developments in data science: Comparing linear, ridge and lasso regressions techniques using wine data" 1 : 1-6, 2019

    11 Breiman, L., "Random forests" 45 : 5-32, 2001

    12 Korea Occupational Safety and Health Agency, "Occurrence of Industrial Accidents in 2022"

    13 Ministry of Employment and Labor, "Occupational Safety Accident Status"

    14 Murtagh, F., "Multilayer perceptrons for classification and regression" 2 (2): 183-197, 1991

    15 Bzdok, D., "Machine learning: a primer" 14 (14): 1119-1120, 2017

    16 Segal, M. R., "Machine learning benchmarks and random forest regression" UCSF: Center for Bioinformatics and Molecular Biostatistics 1-14, 2004

    17 Heinrich, H.W., "Industrial Accident Prevention. A Scientific Approach" 1941

    18 McCabe, B., "Individual safety and health outcomes in the construction industry" 35 (35): 1455-1467, 2008

    19 Dobrowolski, P., "Immersive virtual reality and complex skill learning: transfer effects after training in younger and older adults" 1 : 604008-, 2021

    20 Lee, K. Y., "Enhancement of Safety and Health Education towards Improved Awareness of Construction workers" Hanyang University 2020

    21 Yu, W. D., "Empirical Comparison of Learning Effectiveness of Immersive Virtual Reality–Based Safety Training for Novice and Experienced Construction Workers" 148 (148): 04022078-, 2022

    22 Meng, X., "Demographic influences on safety consciousness and safety citizenship behavior of construction workers" 129 : 104835-, 2020

    23 Haslam, R. A., "Contributing factors in construction accidents" 36 (36): 401-415, 2005

    24 Shapley, Lloyd S., "A value for n-person games" 307-317, 1953

    25 Lundberg, S. M., "A unified approach to interpreting model predictions" 30 : 2017

    26 Smola, A. J., "A tutorial on support vector regression" 14 : 199-222, 2004

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