Can I Teach Abroad? Motivations and Decision-Making Processes of Teachers to the International Locations
Bibliographic record
Abstract
Over the past few decades, South Korea has become a popular education and teaching destination for native English teachers, international school teachers, and university lecturers. However, as the COVID-19 pandemic has changed the requirements, offshore teachers need to complete the self-funded quarantine before they can join the workforce in South Korea. This study aims to understand the motivations, career decisions, and decision-making processes of a group of native English teachers who decided to come to South Korea to develop their English language teaching career, particularly those who came during the COVID-19 pandemic. The phenomenological approach with interview session, focus group activity, and member checking interview were employed. Based on the social cognitive career and motivation theory and qualitative data from 38 participants, three themes were categorized: special life pathways, easy employment, and attractive cultural environment. The results of this study may provide some recommendations to school leaders, employers, and policymakers for native English teachers who would like to provide teaching services in their countries. As the COVID-19 pandemic and traveling restrictions will eventually eliminate, the human resources management and school leaders should continue to reform and improve the management to meet the needs of the long-term human resources shortage.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".