Discrete Modeling of Teacher Satisfaction and Well-Being Using TALIS Data: Latent Profiles, Gender Disparities, and Global Network Dynamics

  • Authors

    https://doi.org/10.14419/kzyp8b13

    Received date: September 24, 2025

    Accepted date: October 15, 2025

    Published date: October 19, 2025

  • Latent Profile Analysis; Network Analysis; Discrete Modeling; Gaussian Graphical Models; Centrality Measures; Teacher Well-Being; Educational Data Mining.

  • Abstract

    This study applies discrete mathematical modeling to examine patterns of teacher satisfaction and well-being using data from the 2018 Teaching and Learning International Survey (TALIS), which included 261,426 teachers across 48 education systems. Latent profile analysis based on nine indicators: self-efficacy, job satisfaction (overall, profession, and environment), emotional well-being, workload stress, disciplinary climate, professional engagement, and collaboration; identified three distinct profiles: High (34.2%), Moderate (45.6%), and Low (20.2%). Welch’s t-tests revealed significant gender disparities across most indicators, with female teachers reporting higher workload stress (M = 9.30 vs. 8.99, p < .001) and lower emotional well-being (M = 9.43 vs. 9.19, p < .001). Country-level ANOVA results indicated statistically significant differences across all nine measures (p < .001). Teachers in Finland, Japan, and South Korea reported higher levels of emotional well-being and professional satisfaction, while Brazil, Mexico, and the Czech Republic reported lower levels. A Gaussian graphical model was then constructed to explore interrelations among indicators. The network revealed strong partial correlations between job satisfaction and emotional well-being (r = 0.24), and between workload and emotional exhaustion (r = –0.21). Centrality analysis highlighted overall job satisfaction and emotional well-being as the most influential constructs, serving as key bridges and hubs of connectivity. These findings demonstrate the utility of combining latent profile classification with graph-theoretic modeling, providing a novel framework for analyzing large-scale teacher data through discrete mathematical procedures.

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    Edara, I. R., del Castillo, F., Chao, P.-C., del Castillo, C. D., & Ching, G. S. (2025). Discrete Modeling of Teacher Satisfaction and Well-Being Using TALIS Data: Latent Profiles, Gender Disparities, and Global Network Dynamics. International Journal of Basic and Applied Sciences, 14(6), 413-424. https://doi.org/10.14419/kzyp8b13