Unveiling multilingual English learners’ perceptions about language-specific adversities and sufferings and their associated regulatory strategies: an existential positive psychology (EPP) perspective
Bibliographic record
Abstract
Previous studies have extensively endorsed the complexities, difficulties, and emotional pressures on learners learning English. However, there is limited evidence on language-specific adversities and suffering experienced by multilingual English students. To fill this gap, relying on existential positive psychology (EPP), the current study examined a sample of 58 multilingual English students’ perceptions of language-specific adversities and sufferings and their associated regulatory strategies. A semi-structured interview was used to collect the data. The results of the thematic analysis indicated four common language-specific adversities and sufferings, namely ‘acquiring a new linguistic system’, ‘learning barriers and complexities of L2’, ‘experiencing negative emotions’, and ‘adopting new cultural norms and practices’. To regulate these adversities, the participants employed different ‘antecedent-focused’ and ‘response-focused’ regulation strategies. As for response-focused strategies, ‘situation avoidance’, ‘situation modification’, ‘cognitive reappraisal’, and ‘attention shift’ were the most common strategies to manage adversities and sufferings. On the other hand, ‘deep breathing’, ‘mindfulness practices’, ‘scaffolding strategies’, and ‘tolerance’ were the most frequently used response-focused strategies. The study discusses each finding in detail and presents some theoretical and practical implications for multilingual students, teachers, and policymakers to understand and regulate language-specific adversities and sufferings more effectively.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".