The effects of proactive form-focused instruction and individual differences on second language acquisition
Bibliographic record
Abstract
This study investigated the extent to which second language (L2) learners benefited from proactive form-focused instruction (FFI) targeting French grammatical gender attribution and the degree to which L2 learners’ attention control and working memory predicted their learning gains. A total of 102 L2 learners received either proactive FFI targeting French grammatical gender attribution or their regular instruction (i.e. control condition) for six 80-minute instructional sessions. A pretest, an immediate posttest, and a delayed posttest were administered, each entailing binary-choice, text-completion, picture-description, and listening tasks. The L2 learners also completed the Simon Test and the Corsi Block-Tapping Test, which measured their attention control and working memory. Results showed that L2 learners receiving the proactive FFI condition significantly outperformed those receiving the control condition in all tasks after the instructional sessions. More importantly, L2 learners’ attention control and working memory were significant predictors of their learning gains in the binary-choice and listening tasks, but not in the text-completion and picture-description tasks. The current study highlighted the roles of L2 learners’ attention control and working memory in proactive FFI.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".