Using pretask modelling to encourage collaborative learning opportunities
Bibliographic record
Abstract
The current study examines the impact of pretask modelling on the collaborative learning opportunities that occurred when Korean learners of English as a foreign language (EFL) carried out three tasks: dictogloss, decision-making, and information-gap. Forty-four adolescents who were enrolled in a required English course at a middle school in Korea completed the tasks over a two-week period. Half of the learners viewed videotaped models of collaborative interaction prior to carrying out the tasks, while the other learners did not receive pretask modelling. The interaction between the learners was analysed in terms of the type and resolution of language related episodes (LREs) and the learners’ pair dynamics. Results indicated that learners who received pretask modelling produced more LREs and correctly resolved a greater proportion of those LREs than learners who did not receive any models. They also demonstrated more collaborative pair dynamics than learners who did not receive models. Trends in the data are discussed in terms of the potential benefits of pretask modelling for encouraging collaboration between young learners in EFL settings.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".