The Paradox of Transformative Learning Among Mid-Career Professionals
Bibliographic record
Abstract
Royal Roads University (RRU) is a special purpose university in British Columbia, Canada. Since 1995, this university has focused primarily on multi-sectoral and interdisciplinary graduate education for working professionals. Most programs are offered in a blended online and face-to-face format, which enables adult learners to continue in their professions while they pursue their studies. While one might not expect a primarily distance education degree to be transformative, feedback from learners consistently points to the experience of transformative learning. This article explores the Master's of Arts in Leadership Studies (MA-L) program. It is proposed that there are at least three elements of the design of this program that contribute to experiences of transformation. First, the RRU Learning and Teaching Model creates a framework that can allow many learners to learn how to learn in a new way. Second, the MA-L program itself has its own competency framework that begins by priming learners to look inward before they seek to lead others. Third and finally, the first year two-week residency, completed after one month of online preparation, provides an embodied experience in what, for many, is a new way of being. This embodied experience creates an awareness of what is possible for human relationship and communication, not only in the context of their particular graduate learning cohort, but also with colleagues, family members, and friends. Taken together, these create an often unexpectedly, and somewhat paradoxically, transformative experience for mid-career professionals.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.034 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".