The Study of the Impact of Reflection Journal on Canadian Pre-service Teachers’ Professional Development
Bibliographic record
Abstract
Teachers’ professional development relates to the quality of teaching and education. Previous research has found that teaching experience and reflection on teaching could lead to teachers’ development (Golombek & Johnson, 2017). Reciprocal Learning Program requires Canadian pre-service teachers to write weekly reflection during their three-month learning trip in China where they get the chance to teach Chinese students and observe classes. Although there are acumulating studies on teachers’ reflexive behavior as a way of developing profession, not much attention is paid to the role of reflection that plays in pre-service teachers’ professional development. The qualitative study will collect and analyze Canadian pre-service teachers’ weekly reflection journals, and interview the seven teachers who have been to China in 2019 from March to June. The interview will be audio-recorded and transcribed. The research questions will be about how Canadian pre-service teachers from Ontario re-construct their teaching and learning experience in China and develop their teaching knowledge and teacher identity through writing weekly reflection. This study will provide implications on teacher education programs for pre-service teachers and offer suggestions on how to use weekly reflection as an important tool to develop teachers’ profession.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".