PROMPT-TYPE FREQUENCY, AUDITORY PATTERN DISCRIMINATION, AND EFL LEARNERS’ PRODUCTION OF<i>WH</i>-QUESTIONS
Bibliographic record
Abstract
Recently researchers have suggested that syntactic priming may facilitate the production ofwh-questions with obligatory auxiliary verbs, particularly when learners are prompted to produce those questions with a wide variety of lexical items (McDonough & Kim, 2009; McDonough & Mackey, 2008). However, learners’ ability to benefit from syntactic priming materials with prompt-type frequency may be mediated by their ability to recognize patterns in aural input. The purpose of this replication study is to confirm the positive impact of prompt-type frequency on learners’ production ofwh-questions reported by McDonough and Kim (2009), and to investigate whether its impact is mediated by learners’ auditory pattern-discrimination abilities. Thai learners (n= 43) of English as a foreign language (EFL) carried out three oral tests, two sets of syntactic priming activities, and an auditory pattern-discrimination test over a 4-week period. Half of the learners carried out the syntactic priming activities with low-type-frequency prompts, whereas the other learners received high-type-frequency prompts. The results revealed a significant interaction between Type Frequency × Auditory Pattern Discrimination on the immediate and delayed posttests. The findings are discussed in terms of the potential role of individual cognitive factors in mediating the relationship between syntactic priming and second language (L2) 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".