The STAR Framework: Towards a more Communicative EFL Class
Bibliographic record
Abstract
English instruction needs to be transformed to provide learners with a suitable environment that allows them to interact in real life situations. For this reason, Task-based Instruction (TBI) has been included into a communicative framework to offer opportunities for learners to use the language without penalizing them for inevitable failures in accuracy, motivating them to engage in the learning process (Willis & Willis, 2007). Inspired by the TBI methodology, a framework called STAR was created to intensify the use of communication in each stage of the lesson (starter, tackle, automatization and recycling stages). Bearing this in mind, the purpose of this qualitative study is to demonstrate the extent to which STAR fosters a more communicative environment for English teaching. This research was conducted in a language program in a public university in Costa Rica, where twenty-five English teachers were consulted about the effectiveness of the framework; added to this, classroom observations and researchers’ logs provided a deeper understanding about the role of STAR. The results evinced the effectiveness of the use of communicative activities within the framework proposed to foster spontaneous speech, heighten engagement, increase risk taking skills, boost learners’ autonomy and build up collaboration in the EFL classroom.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".