The effects of various combinations of form-focused instruction techniques on the acquisition of English articles by second language learners of English
Bibliographic record
Abstract
The current study investigated the extent to which second language (L2) learners of English benefitted from various combinations of form-focused instruction (FFI) techniques on the acquisition of English articles. Forty-six participants were randomly assigned to four conditions: input enhancement only ( n = 12); input enhancement and metalinguistic explanations ( n = 11); input enhancement, metalinguistic explanations, and practice ( n = 11); and a control condition ( n = 12). They each received six hours of online instruction on English articles according to their condition. The participants’ knowledge of articles was measured by four tasks (i.e. grammaticality judgment task, metalinguistic knowledge task, elicited imitation task, and picture-description task) in a pretest, an immediate posttest, and a delayed posttest. Results showed that the group receiving input enhancement and metalinguistic explanations exhibited clear and durable gains in the metalinguistic knowledge task. Furthermore, a subset of participants who benefitted the most from the instructional treatment revealed two common factors: a first language (L1) without an article system and a high rate of engagement during the online instruction. The findings suggest that metalinguistic explanations clearly contribute to explicit knowledge of articles and that learners’ L1 backgrounds may affect the extent to which FFI on English articles benefits them. The present study contributes meaningfully to the current understanding of the effects of FFI as well as the L2 acquisition of English articles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".