Making research on instructed SLA relevant for teachers through professional development
Bibliographic record
Abstract
This article recounts three studies that portray an evolution from research examining the effects of researcher-designed instructional interventions to research examining the impact of helping teachers to design their own instructional interventions. Study 1 investigated the effects of an instructional treatment designed by the research team on students’ ability to accurately assign grammatical gender in French. The treatment yielded positive outcomes and evolved into an instructional model employed in two subsequent researcher-led professional development (PD) initiatives (Studies 2 and 3). The PD in Study 2 aimed to engage teachers with instructional practices considered effective for integrating language and content across their classes in French L2 and social studies classes taught in French. Study 3 had biliteracy instruction as its primary goal, aiming to make connections between French and English classes, specifically with respect to derivational morphology. Together the three studies point to the benefits of developing a synergy between teachers and researchers as a means (1) to support teachers in their implementation of pedagogical insights yielded by instructed second language acquisition (ISLA) research and (2) to strengthen ISLA itself in its endeavor to improve language teaching and learning.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".