Investigating What Second Language Learners Do and Monitor under Careful Online Planning Conditions
Bibliographic record
Abstract
Abstract: This study used quantitative analyses complemented by the retrospective data obtained through a stimulated recall procedure to address three interrelated issues: (a) whether second language learners use online planning opportunities to carefully plan their speech to enhance the quality of the language they produce, (b) what kinds of self-repair behaviour the pressured and careful online planning conditions are likely to induce speakers to make, and (c) the way careful online planning affects EFL learners’ oral L2 performance as measured in terms of complexity, accuracy, and fluency. Thirty intermediate EFL learners were asked to perform an oral narrative task under careful and pressured online planning conditions. Results of the qualitative and quantitative analyses revealed that L2 learners use the planning time to monitor their speech for grammatical accuracy, to retrieve and monitor the appropriate lexical items, and to plan the message they will communicate. In addition, it was found that careful online planning conditions induce learners to execute more error repairs and fewer appropriacy and different-information repairs compared to the pressured online planning condition. An analysis in terms of complexity, accuracy, and fluency measures testified to the positive effects of careful online planning on L2 oral performance.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".