Effects of Dynamic Assessment on the Acquisition of the Rhythm of English: The Case of EFL Learners’ Attitudes
Bibliographic record
Abstract
Dynamic assessment has been widely used in educational literature over the past two decades. The present study aimed at investigating the effect of using dynamic assessment on teaching the rhythm of English to Iranian EFL learners and scrutinizing their attitudes towards it. The participants of the study were 30 Iranian EFL leaners at the intermediate level of proficiency, who were conveniently selected from a foreign language institute in Isfahan, Iran. In order to achieve the objectives of the study, the participants were divided into two homogenous groups, including the experimental group and control group. In this quasi-experimental, pretest-posttest-control-group-design research study, the control group followed traditional method of learning pronunciation and rhythm, while dynamic assessment approach was used to teach the same materials to the experimental group. In contrast to the control group, the experimental group took an active role in the classroom by having more interaction and using the ongoing hints and prompts provided by the teacher. The result of the posttest unfolded that there was a significant difference between the performances of the two groups, and that the experimental group participants managed to outperform the control group members on the pronunciation posttest. Moreover, based on the attitude questionnaire, EFL learners had grown a positive attitude towards the use of dynamic assessment to learn rhythm. The results of this study demonstrated that through the implementation of DA, the proper form of mediation could be provided to the learners regarding their ZPD.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".