Dynamic Assessment of EFL Reading: Revealing Hidden Aspects at Different Proficiency Levels
Bibliographic record
Abstract
Dynamic assessment as a complementary approach to traditional static assessment emphasizes the learning process and accounts for the amount and nature of examiner investment. The present qualitative study analyzed interactions for 270 reading test items which were recorded and tape scripted. The reading ability of 9 EFL participants at three proficiency levels of high, mid, and low were assessed dynamically during five weeks of this study. The findings revealed five major data type only available through DA including an exact estimation of examinees’ abilities, identifying the source of problem, identifying the stage of the problem, estimating the extent of examinees’ development within their ZPDs, and the extent of transcendence for later independent performance. The results also highlighted the differences in these data for readers at different proficiency levels. These findings have implications for assessors, and teachers in taking effective steps to improve learner development with exact information about learners of different proficiency levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".