The Effect of Dynamic and Non-Dynamic Assessment on Acquisition of Apology Speech Act among Iranian EFL Learners
Bibliographic record
Abstract
The current study has aimed to explore the effect of dynamic and non-dynamic assessment on acquisition of apology speech act among Iranian EFL learners. One-hundred forty-three male and female intermediate Iranian learners, who were selected randomly, were assigned to high and low groups based on their score in OPT. Then, each group was divided into two DA and NDA groups randomly. Both groups participated in pretest and posttest, six treatment tests during fifteen sessions, also an instruction part about different apology strategies based on Olshatain and Cohen (1983). DA groups, the experimental ones, received different types of mediation in tests based on Lantolf and Poehner's (2011) scale and their ZPDs. NDA groups, the control ones, were assessed in traditional method. T-test was used to analyze the data, which revealed that DA group significantly outperformed NDA group. The main result of this study showed that dynamic assessment helps learners to improve their apology speech act acquisition. The findings of this study provide insights to assessment of pragmatics.
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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 | 0.000 |
| 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".