Understanding the Well-Being Literacy of EFL Learners: Towards a Framework of Learners’ Knowledge and Skills
Bibliographic record
Abstract
Well-being has been recognized as a basic human right, a core determinant of success in education, and a skill that can be developed. In language education, the literature suggests that higher well-being is likely to lead to more classroom engagement and ultimately greater success for learners. For English language teachers, there is a need to understand what learners know about well-being, what kinds of support they feel they need, and how best to integrate such support into regular language teaching practice. This paper reports on a qualitative study using focus group data that set out to understand the well-being literacy of a group of 42 Austrian learners of English as a foreign language (EFL) in their final year of school. The findings reveal five categories in which learners demonstrated knowledge of well-being: conceptual understanding of well-being, factors impacting well-being, coping strategies, the role of systemic factors, and issues in the English language teaching context specifically. Based on analysis of these data, we present an initial practical framework for evaluating and guiding EFL student well-being literacy development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".