What went wrong? A critical reflection on educator midwives’ inability to transfer education knowledge
Bibliographic record
Abstract
As part of a competency-based curriculum development exercise, educator midwives were required to apply the design principles of constructivism, constructive alignment, scaffolding and authenticity in the development of teaching and learning material for a newly approved curriculum. Through action research cycles, the facilitators and educator midwives reflected on possible reasons why they struggled to apply the mentioned principles in developing learning activities for students. The unit of analysis comprised the reflections of facilitators and 12 educator midwives. Ten of the 12 educator midwives were older than 40 years and all had qualifications and experience in midwifery and education. The action–reflection cycles contributed to improvement in the quality of the learning activities but application of the scaffolding principle remained a challenge. Failure of the educator midwives to transfer their learning raised concerns about their ability to facilitate deep learning. Considering the age of the group and the ingrained rote memorisation characteristic of education methods during their training made curriculum drift a real threat. Educator midwives struggled to integrate education knowledge into educational practice. Nursing schools that are in the process of changing their teaching paradigm may find this article useful to identify possible challenges and suggested solutions.
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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 | 0.082 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.032 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.019 | 0.047 |
| Insufficient payload (model declined to judge) | 0.002 | 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".