Impacts of Resource Dispersing and Resource Directing Task Dimensions on EFL Learners’ Oral Production
Bibliographic record
Abstract
This study was primarily aimed at investigating the effects of simultaneous use of reasoning demand (resource-directing) and prior knowledge (resource-dispersing) on fluency, accuracy, and complexity of L2 oral performance. More, specifically, an attempt was made to investigate how EFL oral production could be affected by ±reasoning demand and ±prior knowledge in the local context of Iran. Thirty male and female Iranian intermediate EFL learners whose mother tongue was Persian and whose age ranged between 23 and 29 were chosen as the participants in this study, and a pretest-posttest quasi-experimental design was utilized. Assigned to two experimental conditions, participants were engaged in a narrative task in which two different wordless picture stories were chosen for data collection. Such statistical operations as t tests and MANOVA were applied to analyze the data. The results obtained from t tests revealed that in ±reasoning demand condition, both complexity and accuracy significantly improved whereas the results for fluency were not statistically significant. In addition, with regard to the ±prior knowledge group, similar results were obtained. In the end, conducting MANOVA revealed that both groups were not different in the pretest; however, utilizing the same procedure for the posttest illustrated a difference between the two groups in terms of their accuracy and complexity, but not their fluency. The results bear some implications for L2 oral production and practice as controlled by teachers and practitioners in EFL contexts.
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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 | 0.007 |
| 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 | 0.001 |
| 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".