Effectiveness of Proactive Intensive FFI in Cultivating Grammatical Knowledge of Iranian EFL Learners at Different Proficiency Levels
Bibliographic record
Abstract
The present study provides a report on an experiment seeking the effect of proactive intensive form-focused instruction on the grammatical development of intermediate and advanced Iranian EFL learners. More specifically the aim of the study was to discern (a) whether the grammatical knowledge of Iranian EFL learners at different proficiency levels ameliorated in the wake of exposure to proactive intensive FFI (b) whether the grammatical knowledge of the learners at the two proficiency levels improved equally or not, and (c) whether there was an interaction effect between the FFI and the proficiency level, which affected the grammatical development of the learners. To meet these objectives, 119 intermediate and advanced EFL learners at Sadr Institute of Higher Education in Isfahan, Iran were asked to take part in this study. To ascertain the homogeneity of the participants, a QPT was administered to them. The learners in each proficiency level were assigned to a control and an experimental group and they sat for a grammar pretest. Subsequently, the learners in the treatment groups received proactive intensive FFI (metalinguistic explanations followed by repeated form-focused tasks and explicit corrective feedback on form) in their free discussion classes, while those in the control groups participated in classes bereft of FFI. After 9 sessions of instruction, the learners took a posttest. Such statistical tools as paired sample t test and two-way ANOVA were utilized to analyze the collected data, and the following results were obtained: (a) both intermediate and advanced group learners experienced a boost in their grammatical knowledge after being exposed to proactive intensive FFI (b) the treatment in the current study was more beneficial to intermediate EFL learners’ grammatical development than it was to advanced learners’, and (c) proactive intensive FFI, proficiency level, and the interaction between them all were instrumental in molding the grammatical knowledge of Iranian EFL learners. The implications for EFL practitioners are enumerated in the paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".