The Effect of Pre-Task Planning Time on L2 Learners’ Narrative Writing Performance
Bibliographic record
Abstract
Building on Baddeley’s cognitive psychology (2007) and Skehan’s Limited Attentional Capacity Model (2009), this article reports a study of the effects of pre-task planning time (strategic planning time) on Malaysian English learners’ written narratives elicited by means of a picture composition. 50 first-year undergraduate students studying at Universiti Sains Malaysia (USM) Penang were served as the participants of this study. All the participants achieved band four from Malaysian University English Test (MUET). They were randomly selected and divided into two equal groups of with pre-task planning time and without pre-task planning time. Each group was asked to narrate a story under the two different conditions. Participants in pre-task planning time group was required to plan for their performance for 10 minutes and take notes before they performed the tasks, whilst the participants in without pre-task planning time group began writing immediately. The learners’ writing performance was measured for complexity, accuracy, and fluency (CAF). Independent samples t-test was employed to analyze the collected data. Results indicated that pre-task planning time had no effect on the accuracy of the learners’ writing performances, but led to more fluency and complexity.
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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 | 0.009 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".