Participation Structure and Incidental Focus on Form in Adult ESL Classrooms
Bibliographic record
Abstract
This study examined the role of incidental focus on form (FonF) in adult English‐as‐a‐second‐language classrooms. Specifically, it explored the extent to which the amount, type, and effectiveness of FonF were related to differences in classroom participation structure, that is, the organization of classroom talk within which FonF may occur. The data consisted of 54 hours of audio‐ and video‐recorded classroom interaction collected over two 12‐week semesters from 35 lessons at three levels of language proficiency: beginner, intermediate, and advanced. The data were transcribed and coded in terms of types of FonF (reactive vs. preemptive, and student vs. teacher initiated) and types of participation structure (whole class, small group, and individual one on one). Individualized posttests were developed and administered to each student 1 week after each classroom observation to assess the effectiveness of FonF. The results revealed that incidental FonF occurred rather frequently in all classrooms but its occurrence varied depending on the type of participation structure. The results also demonstrated a relationship between participation structure and the effectiveness of FonF as well as an interaction between participation structure and class levels. These findings highlight the role of classroom participation structure as an important contextual factor that may impact the provision and success of incidental FonF.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".