A holistic perspective on the contribution of foreign language peace of mind, enjoyment, anxiety, and boredom to EFL learners’ willingness to communicate: The mediating role of engagement
Bibliographic record
Abstract
Learners’ foreign language engagement (FLEng) plays a crucial role in language acquisition, yet its mediating influence between learner emotions and willingness to communicate (WTC) in a second language (L2) remains underexplored. This study investigates how emotional contexts—including positive emotions such as foreign language peace of mind (FLPoM) and foreign language enjoyment (FLE), as well as negative emotions such as foreign language classroom anxiety (FLCA) and foreign language boredom (FLB)—affect English as a foreign language (EFL) learners’ L2 WTC. Utilizing the 3D pyramid model of L2 WTC, we analyzed data from 301 participants who completed six questionnaires. The findings revealed that FLPoM, FLE, FLCA, and FLB did not directly influence L2 WTC. However, learners’ FLEng was found to fully mediate the relationships between both positive and negative emotions and L2 WTC. These results underscore the vital importance of fostering learners’ FLEng in language education, suggesting that enhancing emotional experiences can significantly impact learners’ willingness to communicate in a foreign language.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".