University of KwaZulu-Natal LEARNING TO CHANGE: A STUDY OF CONTINUING TEACHER DEVELOPMENT IN TWO CONTEXTS OF EDUCATIONAL REFORM
Bibliographic record
Abstract
ii Systemic educational reforms entail major changes at the different levels of the system, of which classroom practice is ultimately crucial to obtaining the desired output. Within this paradigm shift, experienced teachers have to replace what they are likely to consider good teaching and learning approaches with unfamiliar strategies. Continuing professional teacher development (CPTD) plays a key role in successfully changing classroom practices. This in-depth case study research —six teachers in two different countries, Canada and South Africa—looks into the information acquisition process of instructors. Interviews were performed at different levels of the educational system – policy makers, pedagogic/subject advisors as well as teachers for which questionnaires and classroom observation were also used to collect data. A research-based analytical tool developed by Laura Desimone (Desimone, 2009) guided the exploration of the vast data collected and served as the analytical framework for the various data sources, drawing a link between the intended, implemented and attained policies. The thorough discourse analysis situated in the interpretivist framework
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".