The Effects of Pre-task, On-line, and both Pre-task and On-line Planning on Fluency, Complexity, and Accuracy – The Case of Iranian EFL Learners’ Written Production
Bibliographic record
Abstract
Previous studies on the effect of planning on language production have revealed that planning does have a positive effect on language performance in terms of fluency, complexity, and accuracy. The present study was an attempt to investigate the effects of pre-task, on-line, and both pre-task and on-line planning on fluency, accuracy, and complexity of Iranian EFL learners’ written production. Forty five Iranian learners of English performed a narrative task based on a series of six pictures. The narratives, then, were coded to measure the fluency, accuracy, and complexity of the participants' production. The results of one-way ANOVA revealed that on-line planning (OLP) and pre-task plus on-line planning (PTP+OLP) had no effect on the fluency, complexity, and accuracy of the Iranian EFL learners’ written narratives. However, pre-task planning (PTP) had a significant effect on one variable of fluency (i.e. syllables per minute) but it had no effect on the complexity and accuracy of the written narratives. Findings have pedagogical implications for language teachers and syllabus designers in EFL context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.031 |
| 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 | 0.000 |
| 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".