The Differential Diagnostic Affordances of Interventionist and Interactionist Dynamic Assessment for L2 Argumentative Writing
Bibliographic record
Abstract
Taking a case study approach, this study investigated the differential potentials of interactionist and interventionist Dynamic Assessment (DA) as diagnostic tools for the investigation of the difficulties faced by five Farsi-speaking learners of English argumentative writing. The study was conducted as part of an EFL academic writing course which aimed to improve learners’ ability to present strong arguments based on a revised version of Toulmin’s model (Qin, 2009). The focus of the study was on the process rather than the product of learning, with the aim of gaining insights into the diagnostic nature of DA to address persistent problems these learners had been shown to have, as confirmed by their instructor. Data were collected via individualized sessions between the mediator and the learners, randomly assigned into interactionist (n=3) and interventionist (n=2) DA groups. Qualitative analysis of transcribed interactions evidenced that interactionist DA could provide more nuanced understandings of the learners’ ZPDs in relation to the components of Toulmin’s model. Suggestions for further research have been made.
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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 | 0.035 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".