Bibliographic record
Abstract
Why do we emphasize reflective practice so extensively in pre-service teacher education? What evidence do we have that frequent references to reflection are improving the quality of the teachers we prepare for certification and careers in teaching? Whatever reflection and reflective practice are, they are not ends in themselves; hopefully, they are means to the end of better teaching practices and better learning by students in schools. In this article I explore reflection and reflective practice from several perspectives, including my personal experiences as a teacher educator working with individuals preparing to become teachers of physics. The question asked in the title captures my fear that the ways teacher educators have responded to and made use of the concepts of reflection and reflective practice may be doing more harm than good in pre-service teacher education. To begin, I consider teacher education practices before and after the arrival of the term reflective practice. I then consider elements of Schön’s (1983) work and review five articles about reflective practice in teacher education; this is not a formal literature review, but rather an effort to show how virtually every article about reflective practice seems to be driven by its author’s personal perspective. The article continues with personal interpretations and illustrations and concludes with five generalizations about teacher education practices that indicate that much more work needs to be done if references to reflection are to do more good than harm in preservice teacher education programs.
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 | 0.105 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.076 |
| Scholarly communication | 0.024 | 0.042 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.012 |
| 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".