Learner attention to form in ACCESS task-based interaction
Bibliographic record
Abstract
This study explored the potential effects of communicative tasks developed using a reformulation of a task-based language teaching called Automatization in Communicative Contexts of Essential Speech Sequences (ACCESS) that includes automatization of language elements as one of its goals on learner attention to form in task-based interaction. The interaction data collected from a class for English as a second language (ESL) over a four-week period was analysed for incidence, outcome and characteristics (i.e. focus, initiation, response, and turn length) of language-related episodes (LREs) operationalized as evidence of learner attention to form. The results showed that during ACCESS task-based interactions, learners attended to form as reflected in a large number of LREs. Despite being brief, a majority of these LREs were correctly resolved, self-initiated, self- and other-responded, and focused on the target linguistic item: past-tense verbs. These results are discussed in terms of the potential effects of ACCESS task principles, different task features (i.e. task complexity, pre-task modeling, speaker role and group size), and learners’ approach to tasks on the incidence and characteristics of LREs.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".