Application of Gradient Boosting Regression to Evaluate the Impact of Family Tourism on Children's Experiential Learning in Nakhon Ratchasima Province
Keywords:
Family Tourism, Machine Learning, Children Learning, Boosting Regression, Experiential Learning TheoryAbstract
Although family tourism is increasingly popular, the research has explored how specific tourism activities drive children’s developmental outcomes beyond general family bonding. This study aims to (1) measure the impact of tourism activities in Nakhon Ratchasima Province on children’s development; (2) develop a predictive Gradient Boosting Regression (GBR) model using activity patterns, family characteristics, and engagement levels. Through a quantitative approach with random sampling, 921 families with children aged 1-12 were surveyed at four diverse tourist sites in Mueang District, Nakhon Ratchasima Province, Thailand.
The results demonstrated that family tourism significantly boosts children’s development in four areas: responsibility, social skills, environmental awareness, and problem-solving. Different tourism formats yield distinct developmental benefits—collaborative planning fosters responsibility and decision-making, guided eco-tours enhance environmental awareness (p<0.001), and interactive cultural workshops most strongly improve social skills (β=0.78). The GBR model showed high predictive accuracy (MSE=0.004, R²=0.99), confirming the findings’ reliability. Theoretically, this study expands Kolb’s Experiential Learning Theory by illustrating how tourism settings offer unique concrete experiences that engage the full learning cycle through culturally influenced reflection. The findings highlight how tourism’s social and physical contexts support transformative learning for children in a Thai cultural framework. This research offers tourism practitioners and educators evidence-based guidance for designing family tourism programs that prioritize specific developmental outcomes, advocating a shift from entertainment-driven to development-focused tourism.
References
Amalia, E. (2023). Experiential Learning in Kindergarten. In E. Soare, & C. Langa (Eds.), Education Facing Contemporary World Issues - EDU WORLD 2022, vol 5. European Proceedings of Educational Sciences (pp. 684-692).
Amaro, D., Caldeira, A. M., & Seabra, C. (2024). Will I Be the Same? Enhancing Academic Tourism Through Transformative Tourism Experiences. Journal of Travel Research, 1-21.
Arcodia, C., Novais, M. A., Cavlek, N., & Humpe, A. (2020). Educational Tourism and Experiential Learning: Students’ Perceptions of Field Trips. Tourism Review, 76, 241-254.
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Chen, T. Q. & Guestrin, C. (2016). Xgboost: A Scalable Tree Boosting System. In B. Kirshnapuram, M. Shah, A. J. Smola, C. Aggarwal, D. Shen, & R. Rastogi (Eds.), Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, (pp. 785-794).
Chen, H., & Wang, Y. (2024). Enhancing social competence through family tourism: A perspective from Chinese families. Journal of Travel Research, 63(2), 345-360.
Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Science, 7, e623. https://doi.org/10.7717/peerj-cs.623
Collins, M. A., Romero, V. F., Young, A., Dorph, R., Foreman, J., Strang, C., Pande, A., & Laina, V. (2024). The value of outdoor environmental education programs for girls and youth of color: cultivating positive dispositions toward science and the environment. Environmental Education Research, 31(1), 108–133. https://doi.org/10.1080/13504622.2024.2340502
Daimon, T. (2022). Mobility and Learning Through Tourism: Touristic Learning of Children During Family Travels. International Journal of Mobile and Blended Learning, 14, 1-14.
Dolnicar, S. (2018). Tourism research: The importance of quality. Journal of Travel Research, 57(5), 649-659.
Durko, A. M., & Petrick, J. F. (2013). Family and Relationship Benefits of Travel Experiences: A Literature Review. Journal of Travel Research, 52(6), 1–11.
Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29, 1189-1232.
Froeschke, B. F., Roux-Osovitz, M., Baker, M. L., Hampson, E. G., Nau, S. L., & Thomas, A. (2024). The Use of Boosted Regression Trees to Predict the Occurrence and Quantity of Staphylococcus aureus in Recreational Marine Waterways. Water, 16(9), 1-14.
Fu, X., Lehto, X., & Park, O. (2014). What Does Vacation do to our Family? Contrasting the Perspectives of Parents and Children. Journal of Travel & Tourism Marketing, 31, 461-475.
Garau, C., & Annunziata, A. (2020). Supporting Children’s Independent Activities in Smart and Playable Public Places. Sustainability, 12(20), 8352.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Stanford University.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd ed.). OTexts.
Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development, 15(14), 5481–5487.
ISANist (2025, May 6). Where are the 48 million tourists scattered in the Northeast?. Facebook. https://www.facebook.com/isanist/posts/
Khoo-Lattimore, C. (2015). Kids on board: methodological challenges, concerns and clarifications when including young children’s voices in tourism research. Current Issues in Tourism, 18(9), 845–858.
Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall.
Kolb, D. A., Boyatzis, R. E., & Mainemelis, C. (2014). Experiential Learning Theory: Previous Research and New Directions. Routledge.
Kyriazos, T., & Poga, M. (2024). Application of Machine Learning Models in Social Sciences: Managing Nonlinear Relationships. Encyclopedia, 4(4), 1790-1805.
Li, Z., Arcodia, C., Yang, E. C. L., & Le, T. H. (2024). Children's learning processes in family travel: A narrative review through a social cognitive lens. Tourism Management Perspectives, 54, 1-11.
Lindqvist, G. (2003). Vygotsky's theory of creativity. Journal of Creativity Research, 15(2–3), 245–251.
Malkewitz, C. P., Schwall, P., Meesters, C., & Hardt, J. (2023). Estimating reliability: A comparison of Cronbach's α, McDonald's ωt and the greatest lower bound. Social Sciences & Humanities Open, 7(1), 1-10.
Martín-Antón, L. J., Carbonero, M. A., Valdivieso, J. A., & Monsalvo, E. (2020). Influence of Some Personal and Family Variables on Social Responsibility Among Primary Education Students. Front. Psychol, 11(1124), 1-10.
Moorhouse, N., Dieck, T. M. C., & Jung, T. (2017). Augmented Reality to enhance the Learning Experience in Cultural Heritage Tourism: An Experiential Learning Cycle Perspective. e-Review of Tourism Research, 8, 1-6.
Nakhon Ratchasima Province. (2023). Nakhon Ratchasima Development Plan, 5 Years (2023 - 2527), Revised on 2023.
Omar, I., Khan, M., & Starr, A. (2023). Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data. Sensors, 23(3), 1074.
Park, S. Y., Pan, B., & Ahn, J. (2020). Family trip and academic achievement in early childhood. Annals of Tourism Research, 80, 1-45.
Pfadt, J. M., Bergh, D. V. D., Sijtsma, K., & Wagenmakers, E. J. (2023). A tutorial on Bayesian single-test reliability analysis with JASP. Behav Res, 55, 1069–1078.
Qiao, G., Cao, Y., Chen, Q., & Jia, Q. (2022). Understanding Family Tourism: A Perspective of Bibliometric Review. Front. Psychol, 13, 1-11.
Rahman, M. S., Haider, Z., & Shah, S. (2021). Evaluating the model performance metrics for regression analysis in tourism research. Tourism Management Perspectives, 41, 55-62.
Rizkallah, L. W. (2025). Enhancing the performance of gradient boosting trees on regression problems. J Big Data, 12(35), 1-34.
Schänzel, H. A., & Yeoman, I. (2024). A review of family tourism research. Tourism Management, 48, 38-47.
Soper, D. S. (2021). A-priori sample size calculator for structural equation models [software]. https://www.danielsoper.com/statcalc.
Tseng, T. A., Chien, H. W., & Shen, C. C. (2023). Exploring family travel motivation, preference, and decision-making with children of different ages. Tourism & Management Studies, 19(4), 35-45.
Whitburn, J., Abrahamse, W., & Linklater, W. (2023). Do environmental education fieldtrips strengthen children’s connection to nature and promote environmental behaviour or wellbeing?. Current Research in Ecological and Social Psychology, 5, 1-10.
Wu, M. Y., Wall, G., & Pearce, P. L. (2014). Shopping experiences: International tourists in Beijing's Silk Market. Tourism Management, 41, 96-106.
Wu, W., Kirillova, K., & Lehto, X. (2021). Learning in family travel: what, how, and from whom?. Journal of Travel & Tourism Marketing, 38(1), 44-57.
Yamazaki, Y. & Toyama, M. (2022). A Preliminary Study to Develop a Cross-Cultural Learning Style Measurement. Journal of Public and Private Management, 8(4), 1-13.
Zhang, Y., Ricci, P., & Bilgihan, A. (2016). Agent-based Modeling of Destination-Tourist Interactive Evolution. Travel and Tourism Research Association: Advancing Tourism Research Globally, 30, 1-14.
Zhang, X., Kuchinke, L., Woud, M. L., Velten, J., & Margraf, J. (2017). Survey method matters: Online/offline questionnaires and face-to-face or telephone interviews differ. Computers in Human Behavior, 71, 172–180.
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