Teaching Effectiveness and Deep Learning in EMI Business Courses: The Mediating Role of Academic Buoyancy

초록

Background: The rapid global expansion of English-Medium Instruction (EMI) has transformed higher education, particularly in business programs that require students to master complex disciplinary knowledge while overcoming language-related barriers. EMI students must simultaneouslyovercome linguistic, cognitive, and cultural barriers, which can increase academic stress, reduce participation, and negatively impact learning outcomes. Despite these challenges, most EMI research has relied on correlational models emphasizing self-efficacy, motivation, or language proficiency, while overlooking psychological constructs that capture students' abilityto manage daily academic stressors. Academic buoyancy-the capacity to persist through routine academic setbacks-offers a theoretically robust and practically relevant lens for understanding student adaptation in multilingual learning environments. Purpose: This study examines academic buoyancy as a mediating mechanism linking teaching effectiveness to deep learning outcomes in EMI business education. Drawing on academic resilience theory, interaction theory, and self-regulated learning theory, we investigated predictors of academic buoyancy and tested whether it mediates the relationship between teaching effectiveness and deep learning. Method: Data were collected via cross-sectional surveyfrom 215 international students enrolled in EMI business courses at a South Korean university. Multiple regression analysis identified predictors of academic buoyancy, and Hayes' PROCESS macro with 5,000 bootstrap samples tested mediation pathways. Results: Learner-professor interaction (beta = .163, p < .001), teaching effectiveness (beta = .146, p < .001), and cognitive engagement (beta = .102, p = .006) significantly predicted academic buoyancy, collectively explaining 30.6% of the variance. Mediation analysis demonstrated that academic buoyancy fully mediated the relationship between teaching effectiveness and deep learning (indirect effect = .071, p < .05; 95% CI [.012, .130]), with no significant direct effect (beta =-.023, p > .05). Notably, peer interactions, content engagement, and metacognitive self-regulation did not significantly predict academic buoyancy after controlling for other variables. Conclusion: Academic buoyancy served as a key psychological mechanism through which teaching effectiveness influences deep learning in EMI contexts. The findings suggest that effective EMI pedagogy operates primarily by fostering student resilience rather than through direct content transmission, with practical implications for faculty development emphasizing both relational and pedagogical dimensions of instruction.

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