Bridging the Gap Between Instruction and Assessment: Examining the Role of Dynamic Assessment in the Oral Proficiency Skills of English-as-an-Additional-Language Learners
Bibliographic record
Abstract
This exploratory study investigated the role of dynamic assessment (DA) in improving the oral proficiency skills of English-as-an-additional-language learners. It focused specifically on speaking test scores and the use of language learner strategies, with the goal of providing empirical evidence as well as pedagogical recommendations. Seven participants were administered a section of the IELTSTM Speaking test in both dynamic and standardized formats. Each test was followed by a think-aloud protocol in order to ascertain participants’ thoughts and strategic behaviours during the testing process. In terms of test scores, results showed no holistic differences, but did show differences in fluency, grammatical range, and lexical resource scores. Scores for grammatical range and lexical resource were higher in DA, while scores for fluency were higher in standardized assessment. An analysis of the participants’ strategic behaviours also showed a greater use of cognitive and metacognitive strategy use in DA. These results point to DA’s potential to facilitate the development of grammatical and lexical abilities as well as to foster the use of language learner strategies within the sample.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".