Observing the interactive qualities of L2 instructional practices in ESL and FSL classrooms
Bibliographic record
Abstract
Discourse features that promote the generation of interactionally modified input and output, such as negotiation for meaning, have been shown to significantly enhance second language acquisition. Research has also identified several characteristics of instructional practices that render them more or less propitious to the generation of these discourse features. While various classroom observation studies have successfully measured the communicative orientation of classroom environments, most of the indicators of interactivity analyzed in those studies were obtained through micro-level discourse analyses and not through macro-level analyses of task-related factors shown to directly influence the interactivity of instructional practices. Such a macro-level scale has potential practical implications for teachers and administrators seeking an efficient tool for assessing and improving the interactivity afforded by a given curriculum. The objective of the present study was therefore to develop macro-level scale to determine the extent to which teachers of French and English as a second language use interaction-friendly instructional practices. Using an observation scheme designed to code data on factors shown to influence interactivity, 63 hours of FSL and ESL classes from secondary schools in the Montreal area were observed and analyzed. Results indicate clear differences between the two groups. While both ESL and FSL classes were less teacher-centered than those observed in previous studies, they were still rated as not-very-interactive. Target language differences showed that the FSL classes were more teacher-centered and characterized by fewer interaction-friendly tasks and activities than the ESL classes. Task characteristics, reasons for ESL and FSL differences and recommendations for improvement are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".