Transformative Learning in Field Education: Students Bridging the Theory/Practice Gap
Bibliographic record
Abstract
Abstract In a four-year, four cohort study utilising a series of six focus groups, forty Masters of Social Work students preparing to graduate defined their personal and professional experiences of transformation in their respective social work field education settings. Using an inductive thematic analysis, students highlighted four key themes in their transformative learning (TL) process: (i) defining the nature of disorienting dilemmas in field education; (ii) critical self-reflection, coping and moving through disorienting dilemmas; (iii) identifying the transformative outcomes in a field context; and (iv) facilitative factors to TL in field education. The findings illuminate the essential role of the field supervisor in creating ‘relationship’. The field supervisor/student relationship is the conduit to students’ deep learning, critical reflection, identity shifts and empathy supporting the student’s navigation through their disorientating moments towards transformative and meaningful outcomes. This study extends our understanding of the role of TL theory within experiential learning contexts and the feasibility of its use in the social work field education experience.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".