A comparative study of the effects of teacher-initiated planned preemptive and reactive focus on form on L2 learners’ accuracy in narrative writing
Bibliographic record
Abstract
Although researchers in the area of second language acquisition agree on the effectiveness of form-focused instruction, there is little consensus on the appropriate time to direct learners’ attention to linguistic items, that is, whether before or after the error occurrence [Nassaji, H. 2010, “The Occurrence and Effectiveness of Spontaneous Focus on Form in Adult ESL Classrooms.” The Canadian Modern Language Review 66 (6): 907–933; Panahzade, V., and J. Gholami. 2014, “The Relative Impacts of Planned Preemptive vs. Delayed Reactive Focus on Form on Language Learners’ Lexical Resource.” The Journal of Language Teaching and Learning 4 (1): 69–83]. In this regard, adopting a quasi-experimental design, the present study attempted to compare the effects of two techniques of focus on form (FonF), namely teacher-initiated planned preemptive and reactive FonF, on the accurate use of English third person singular -s in L2 learners’ narrative writing. Thirty-two English learners selected out of a total of 70 following a Quick Oxford Placement Test were randomly classified into two groups (one experimental and one comparison) each receiving a different FonF instruction during narrative tasks. Analysis of the groups’ performance on the pretest, immediate posttest, and delayed posttest revealed that both techniques were equally beneficial in bringing the form in focus to the center of the learners’ attention. It can, therefore, be suggested that the teachers need to observe the time when deciding to draw the learners’ attention to the linguistic forms.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".