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 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 | 0.000 | 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) | 0.000 | 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".