Bibliographic record
Abstract
Teaching reflective practice to beginning teachers requires significant changes in a teacher educator’s implicit assumptions about how individuals learn to teach. Teaching reflective practice also requires significant changes in a teacher educator’s teaching practices. The familiar teach-them-theory-and-then-let-them-practice approach assumes that learning is complete before practice begins. In contrast, reflective practice in professions involves learning from firsthand experience and recognizes that the process of learning theory and research is incomplete before personal practice begins. Teaching reflective practice also requires recognizing that terms such asreflectandreflectionare everyday words with multiple meanings and uses and little direct connection toreflective practice. The following argument, grounded in self-study methodology, describes indirect strategies for encouraging reflective practice. These strategies include an extended writing assignment focused on professional learning, teaching how to learn from personal experience, the unrecognized power of listening, and tickets out of class as listening and fostering metacognition. The argument closes with a summary of suggested strategies for encouraging reflective practice by those learning how to teach.
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 | 0.046 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".