A Model of Willingness to Communicate in English in Iranian EFL Classroom Context
Bibliographic record
Abstract
The present study aimed to propose a model of willingness to communicate in English (WTC) in English as a foreign language (EFL) classroom context in Iran considering four trait-like variables: confidence, motivation, anxiety, and grit. An online questionnaire measuring the five variables was sent to eight classes of non-English majored university students in two public and two private universities in Iran. 488 questionnaires were returned and analyzed using the structural equation modeling (SEM) using Amos. The key findings were as follows. First, the finalized model showed motivation, confidence, and anxiety to be the predicting variables of WTC in Iranian EFL classroom context whereas grit served as a mediator. Second, among the four variables, motivation was the best predicting variable, having both direct and indirect effects on WTC. Based on the key findings, to promote Iranian university students’ English communication behaviors, English teachers are recommended to design their lessons to enhance students’ motivation in learning English, to build their confidence in using English while keeping their anxiety optimal, and to promote grit. The new path that shows grit as a mediating variable in this model should be further explored. Qualitative data should be considered for future research to gain insights into the path from motivation to WTC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".