This study investigates the determinants of academic adaptation and retention among international students in Japanese higher education, employing a Mixed Methods Research (MMR) design to provide evidence-based insights for institutional management. In the quantitative phase (n = 177), multivariate regression analysis and structural equation modeling (SEM) were utilized to examine the causal relationships between Japanese language proficiency, social capital (bonding, bridging, and linking), and institutional support systems. The statistical results demonstrate that linguistic barriers significantly inhibit the formation of "linking capital" (relationships with faculty and administrative staff), which serves as a critical predictor of institutional commitment and dropout risk. In the qualitative phase (n = 40), semi-structured interviews were analyzed using text mining techniques to quantify and visualize the psychological and social processes underlying the statistical models. By integrating these findings, the study maps the structural vulnerabilities in student support under crisis conditions (e.g., COVID-19). The findings offer actionable, data-driven recommendations for Teaching and Learning Management (TLM), including the development of predictive models for student attrition and the optimization of integrated language-academic support frameworks. This research demonstrates a robust capacity for utilizing complex datasets to inform strategic institutional decision-making.
Social Capital Theory, Japanese Higher Education, Mixed Methods Research, Shadow Bureaucracy, Sociocultural Adaptation, Linking Capital, Student Retention.
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