Student teacher collaborative reflection: perspectives on learning together
Bibliographic record
Abstract
This paper reflects on the collaborative learning experiences of two ‘student teachers’ who have recently completed a Masters in Higher Education. Its purpose is to enhance our general understanding, and to encourage debate on how the professional expertise and confidence of new academics may be supported. Specifically, the article examines the use of classroom observation and associated reflective activities between student teachers as important cornerstones for professional growth and in developing a sense of collegiality. The paper sets the context by drawing together the theoretical literature relating to classroom observation and reflective practice. Then it draws on our experiences of using a particular classroom observation tool (FIAC) and how our learning was enriched through the exchange of written reflections. The paper illustrates how shared reflective activities between peers, such as story-telling, deepens understanding. In particular, it highlights the importance of understanding the emotional dynamics at play in learning. Travelling, while taking you to new places, emphasizes the value of having an emotional and physical base. T. S. Eliot said that ‘the end of all our exploring/Will be to arrive where we started/And know the place for the first time’. This applies equally to an internal as well as an external environment and I feel that this trip has helped me see myself more clearly. (Keenan & McCarthy, , p. 386)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".