Running a Diagnostic on Motivation: Exploring Motivational Dynamics Underlying Non-Anglophone Medical Students’ Efforts to Learn English for Medical Purposes
Article Number: e2026001 | Available Online: January 2026 | DOI: 10.22521/edupij.2026.20.1
Andrea Stötzer , Márton Bagyura , Éva Farkas
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Abstract
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Background/purpose. While the significance of English for Medical Purposes (EMP) in medical education continues to grow, research on the motivational dynamics shaping non-Anglophone students’ efforts to learn EMP remains limited. To address this gap, we developed a contextualized questionnaire based on the L2 Motivational Self System. Materials/methods. The study, conducted across Hungary’s four medical schools, involved 283 medical students in Hungarian-medium instruction. Using linear regression and mediation analyses, we examined the direct and indirect effects of core motivational dimensions (Ideal L2 Self, Ought-to L2 Self, Integrativeness, and Instrumentality) on students’ intended learning effort (ILE). Results. Results identified Integrativeness, Ideal L2 Self, and Instrumentality Prevention as direct predictors of ILE. Further analyses explored supplementary psychosocial dimensions (Self-efficacy, Self-confidence, L2 anxiety, and Attitude toward EMP), revealing significant but varied effects on ILE’s direct predictors. |
Conclusion. These findings not only deepen theoretical insights into EMP motivation but also highlight important pedagogical implications. To enhance motivation and sustain engagement in EMP learning, pedagogical strategies should incorporate authentic materials, real-world simulations, and interdisciplinary collaboration while fostering a supportive classroom environment that strengthens self-confidence, reinforces self-efficacy, and minimizes L2 anxiety. By bridging theoretical and practical perspectives, this study contributes to research on EMP motivation and provides insights for curriculum design and instruction.
Keywords: English for Medical Purposes, medical education, motivation, linear models, mediation analysis
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