Inner Landscapes: Transformative Learning Experiences of Canadian Education Interns in Greece
Bibliographic record
Abstract
This exploratory study uses transformative learning theory as a lens to interpret and understand the challenges and successes experienced by education students who elect to teach or intern abroad. Transformative learning is a deeper-level learning that challenges learners to understand themselves and their world in new, more nuanced ways. We explore frames of teaching and learning from multiple lenses. Elements of the educational internship experience that emerged from students who participated in this study include initial apprehension, disbelief, and even fear; a disorienting dilemma or incongruent experience within the new school cultural context; a re-evaluation of their frames of reference, and a final emergence of more integrative, inclusive senses of self as “teacher” and “learner.” Transformative learning theory can serve both as a conceptual framework for understanding the experiences of students and as a means of suggesting ways in which educational outcomes can be better designed with a transformative intent in mind. We then present implications for teaching and learning and suggestions for future studies. Keywords: international experience; preservice teachers; internship; transformative learning; frames of reference
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".