Reflective Practice: Tools and Challenges in Difficult Contexts
Bibliographic record
Abstract
Reflective practice (RP) plays a prominent role in both the initial training and continuous professional development of teachers.It helps teachers to improve on their teaching practice by thinking about their experiences, identifying areas for improvement and making changes.This article discusses RP as a vital component of teacher training and development and presents a range of tools and practical strategies for encouraging and supporting teachers on their journey to becoming reflective practitioners, drawing on my personal experience and the experience of a preservice teacher on teaching practice, whom I interviewed.But it also leverages on the Cameroon context, which reflects the realities of most Global South countries, in general, and Sub-Saharan Africa, in particular, to explain how and why many of the tools would not work very well in difficult contexts and suggests ways in which teachers working in such contexts can navigate the challenges.Given that RP is a continuous process that requires ongoing engagement and dedication, this article makes a case for teachers, especially those working in difficult contexts, to be encouraged and supported in the process.
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 | 0.164 | 0.262 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.014 | 0.086 |
| Scholarly communication | 0.054 | 0.057 |
| Open science | 0.013 | 0.037 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".