Embarking on the Teaching Journey:Pre-service Teachers Reflecting upon Themselves as Future Teachers
Bibliographic record
Abstract
This article reports on a research study that examined 25 reflective papers written by pre-service teachers a few weeks into attending primary teacher education. The overall aim of the study was to gain insight into the students’ thoughts and ways of reflecting as a basis for taking action to improve lecturing, tasks and exercises which promote reflection in the primary teacher education program. The students were asked to write a paper reflecting upon “The teacher I want to become”. Two research questions were posed upon the material: What images of teachers emerge in the texts? How is reflection carried out in constructing these images? The analysis of the reflective papers revealed that the students’ images of themselves might be placed in the main category; “caring teacher”. Furthermore their ways and levels of reflection varied considerably from descriptions and fragmented thoughts to accounts with elements of dialogic reflection.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".