What do Early Childhood Educators Learn from their Experience of Mentoring/Supervising Students?
Bibliographic record
Abstract
Supervising-mentoring early childhood education students in their field placement is considered a highly beneficial process for students. However, not much is known about how this process benefits supervisors. Therefore, supervising students is not considered an opportunity for the professional growth of supervisors. This qualitative research examined supervisor’ perceptions of their own learning while supervising students. Ten early childhood educators who supervise students contributed their views in semi structured, individual interviews. Six themes of professional growth areas were highlighted: supervisory skills, leadership skills, pedagogical knowledge and skills, reflective practice, professional resposibility, and organisational and systematic interdependence. These themes, branching out into further subthemes, also provided insights into supervisors’ thinking and its dependence on influences across all levels, from micro to exosystem. The study found that supervisors saw supervising students as a rich opportunity for their learning. It calls for reconceptulizing supervision of students as professional development, and systematically organizing it, including training for supervisors. Keywords: supervising students, mentoring, field placements, professional development, professional growth, leadership, early childhood education,
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".