The Impact of the Dynamicity and Non-dynamicity of Assessment on EFL Learners' Productive Skills: Attitude in Focus
Bibliographic record
Abstract
The possible effects of dynamic evaluation (DE) and non-dynamic (non-DE) evaluation on the productive skills of Saudi EFL students were examined in this study. This study also looked at how Saudi EFL students felt about utilizing DE in their writing and speaking sessions. To achieve these objectives, sixty-four Saudi intermediate EFL students were split into two groups and selected using the convenience sample approach. Then, a pre-test was given to both groups for two skills: speaking and writing. After that, one group was taught speaking and writing using dynamic evaluation, while the other group was taught using NDE. Following eighteen training sessions, the groups were given posttests in speaking and writing, and the dynamic evaluation group was also given a perception questionnaire. The speaking and writing posttests for the two groups showed a substantial difference that favored the experimental group. The speaking and writing posttests demonstrated that the DE group fared better than the non-DE group. The results also pointed out that the DE group members had favorable opinions of the evaluation process. It was concluded that one of the best ways to help EFL students advance in their English language learning is to use DE in the classroom. Teachers and course designers may be convinced to incorporate dynamic evaluation into their lesson plans and courses by the consequences of this research.
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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| 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".