Collaborative and individual output tasks and their effects on learning English phrasal verbs
Bibliographic record
Abstract
Current second language (L2) instruction research has encouraged the use of collaborative output tasks in L2 classrooms. This study examined and compared the effectiveness of two types of output tasks (reconstruction cloze tasks and reconstruction editing tasks) for learning English phrasal verbs. Of interest was whether doing the tasks collaboratively led to greater gains of knowledge of the target verbs than doing the tasks individually, and also whether the type of task made a difference. The study was conducted in two intact low-intermediate adult English-as-a-second-language (ESL) classrooms. The effectiveness of the tasks was determined by how successfully learners completed the tasks and also by means of a vocabulary knowledge test administered before and after the treatment. The results showed that completing the tasks collaboratively (in pairs) led to a greater accuracy of task completion than completing them individually. However, collaborative tasks did not lead to significantly greater gains of vocabulary knowledge than individual tasks. The results, however, showed an effect of task type, with the editing tasks being more effective than the cloze tasks in promoting negotiation and learning. The findings contribute to the research that has examined the effectiveness of pedagogical tasks in L2 classrooms.
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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 | 0.019 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".