Introducing Co-Teaching and Co-Generative Dialogues in a Pre-Service Teaching Practicum: Stepping in and Remaining Contradictions
Bibliographic record
Abstract
This article reports on collaborative teaching practices and co-generative dialogues incorporated into a Master of Arts (MA) in Teaching English as a Second Language (TESL) practicum at a large northeastern U.S. university with a focus on uncovering the processes associated with learning-to-teach. The paper describes the contents of the existing pre-service teaching practicum as well as the supplementary activities that were introduced. The article presents two main findings: (1) Co-teaching opportunities provided critical support to the pre-service teachers as they began teaching, and (2) Although the co-generative dialogues were set up to mediate a reflexive dialogue around the nature of teaching, we found that the pre-service teachers retained some teaching beliefs that were in conflict with those of the mentors even at the end of the practicum. Despite the extended opportunities to discuss their respective teaching beliefs, the mentors were not prepared to deal with the pre-service teachers’ unresolved concerns, which bears important implications for how to prepare mentors to work with novices in the context of an MA TESL practicum.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.008 |
| 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".