Profiling mediating factors in CSL learners’ willingness to communicate and talk realisation: an observation-enhanced repeated Q methodology study
Bibliographic record
Abstract
Translating willingness to communicate (WTC) into learner talk is a complex adaptive process which involves the coactions of multiple learner-internal and -external factors. However, the dynamic interplay among these factors and the relative strength of such factors carry into the process remain underexplored. Furthermore, WTC in languages other than English has been underrepresented in existing literature. Addressing these gaps, this study investigates: (1) the key factors, interactions, and their selective salience underlying multilingual multicultural learners’ WTC-talk realisation in a Chinese-as-a-second-language classroom, and (2) whether these interplays and prioritisations shift across a half-semester period. Two rounds of Q-sorting and semi-structured interviews were triangulated with classroom observational data collected during the intervening period. Findings revealed two factor profiles that remained largely consistent across both time points. The two profiles exhibited disparities in factor interactions and the factors they prioritised, with these differences persisting throughout the study. These findings provide evidence for the understanding that not all factors are equally impactful in WTC-turn-talk processes and underscore the importance of examining contextualised confluences of factors rather than isolating factors. This study concludes with context-contingent agent-centred pedagogical suggestions that address the selective salience of factors and their interactions in mediating WTC and its behavioural manifestations.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".