Migrating Abroad : Factors and Experiences of Bangladeshi Students in Umeå, Sweden
Bibliographic record
Abstract
The predominance of International Higher Education has switched from the USA, Canada, England and Australia, the pioneers of internationalization of higher education, to Europe and other countries. European countries have become the higher study destination for many students from all over the world. Own strategies and policies for internationalization have developed by European countries, building on and seeing beyond the policies stipulated by the European Commission. As a member of European Union, Swedish Government and universities have developed strategies, measures and tools to attract international students. Scholarships and grants, different projects, extended visa, education quality, social security, ranking of institutions etc. play important role behind third country students choosing Sweden for higher studies. However, other features such as education cost, ideological affinity, language aptitude, job availability, easy visa process etc. can influence the choice of students. These features make Sweden attractive as higher education destination to Bangladeshi Students. This study tries to understand the factors behind decision of Bangladeshi students choosing Swedish University for tertiary education. The study also attempts to discuss the experience of Bangladeshi students during their study period in Sweden as well as after finishing study. The theory of “Push and Pull factors” is used as the main theoretical reference to discuss findings of the study. Social insecurity, political instability, education opportunity and quality of home country, personal choice, job opportunity etc. are recognized as major ‘push factors and education quality, world class institution, social security, standard of life, easy application process etc. are recognized as ‘pull factors’ behind the decision of Bangladeshi student choosing Sweden as higher education destination. The Study not only discusses the challenges and experiences of Bangladeshi students including covid-19 situation but also gives implications for future students. The data was collected by taking interviews from the Bangladeshi students studying at masters’ level in Umea university, Sweden and used qualitative method for analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".