The impact of task type and pre-task planning condition on the accuracy of intermediate EFL learners’ oral performance
Bibliographic record
Abstract
Task-based language teaching comprises both a novel language teaching approach and a burgeoning area of study in the field of second-language acquisition. This study investigated the effects of task type and planning conditions on the accuracy of learners’ oral performance during pre-task planning. Eighty intermediate EFL learners were assigned to four task conditions: individual-planning personal task, individual-planning decision-making task, group-planning personal task, and group-planning decision-making task (n= 20). Individual task performances were scored for accuracy prior to the treatment sessions. During the treatment sessions, the participants completed the tasks under different planning conditions. Results of statistical analyses revealed that pre-task planning conditions and the task type are effective in enhancing the accuracy of learners’ oral production. The findings lend support to the view that there are advantages in selecting and implementing appropriate task-based conditions to develop the accuracy of language learners’ oral performance. The implications for task-based language teaching are explained and some suggestions for further research are offered.
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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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".