Interactional role negotiation among co-facilitators in an online design workshop
Bibliographic record
Abstract
Research has demonstrated the important role of co-teacher communication and planning, but relatively little is understood about co-teacher interactions during the act of teaching itself and how these interactions relate to educators’ positionings and ongoing identity development. This paper presents a case study of interaction between two co-facilitators of a team of Japanese youth during a week-long, synchronous, online workshop on human-centred design. One co-facilitator had several years of experience, and the other was a first-timer. Using positioning theory and discourse analysis, we show that the co-facilitators developed a relatively stable pattern of instructional authority delegation, or the social order that guides who has rights and responsibilities over which forms of instructional decision-making. We describe the delegation between the co-teachers in this study as involving ‘instructional content authority’ and ‘instructional language authority’, established through interactions of positioning early in the workshop. Then, we examine an interview activity later in the workshop that seemed to disrupt the established pattern. This work extends research on co-teacher communication and teacher learning to understand co-teacher interactions during live teaching, with potential implications for co-teacher preparation and the learning of less experienced co-teachers.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".