Educational Migration from Kerala: An Empirical Study of Student Mobility for Higher Education
Notice bibliographique
Résumé
Migration has become a defining feature of the globalized world, with educational migration emerging as a significant trend, particularly among students seeking higher education opportunities abroad. This study explores the phenomenon of educational migration from Kerala, India, to countries such as the United States, Canada, the United Kingdom, and Australia. It examines the underlying push and pull factors influencing students’ decisions, including limited opportunities in domestic institutions, the desire for global exposure, advanced academic infrastructure, and better career prospects abroad. The choice of destination countries is justified by their globally recognized education systems and favorable immigration policies. The UK is preferred due to abundant scholarships, affordable living costs, and flexible part-time job options. Australia and Canada are chosen for their easier immigration pathways and employment opportunities. While the US offers high-quality education and well-paid jobs post-graduation, its higher living costs and work restrictions make it a slightly less favorable option. The study also investigates the academic and economic impacts of such migration and to investigate the reasons behind students migrating from Kerala to the United States, Canada, the United Kingdom, and Australia. It is based on primary data collected from 130 students who migrated for higher education, using structured questionnaire and surveys. Statistical tools such as Chi-square test and trend analysis were employed to examine patterns and influencing factors. The findings indicate that despite Kerala’s strong educational foundation, students often migrate due to outdated curricula, intense competition, and a lack of job-oriented courses, while attractive immigration policies, scholarships, and global career opportunities serve as major pull factors. The study further identifies key challenges faced by students, including cultural adaptation, financial strain, and legal complexities. It highlights the benefits of international education, such as academic recognition, skill development, and personal growth, while also acknowledging concerns such as brain drain and economic dependency on remittances. The research underscores the need for policy reforms in Kerala’s education system to retain talent and better prepare students for global academic and professional environments.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».