Levels of Engagement in Task-based Synchronous Computer Mediated Interaction
Bibliographic record
Abstract
Investigating task-based synchronous computer-mediated communication (SCMC) interaction has increasingly received scholarly attention. However, studies have focused on negotiation of meaning and the quantity, focus and resolution of language related episodes (LREs). This study aims to broaden our understanding of the role of audio, video, and text SCMC conditions by additionally examining second language (L2) learners’ levels of engagement during the production of LREs as a result of interactive real-world tasks. We tested 52 dyads of L2 Spanish intermediate learners who completed a decision- making/writing task. Our main analysis revealed that dyads in the audio SCMC condition engaged in more limited LREs vis-à-vis the text SCMC group, and audio SCMC dyads also showed a trend of engaging more in elaborate LREs. The findings imply that interactive SCMC conditions can place differential demands on L2 learners, which has an effect on the ways in which L2 learners address LREs during task-based interaction.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".