Heutagogy for dynamic learning: lessons learned from an Innovation Fellowship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The educational landscape is shifting toward learner-centered approaches, with heutagogy emerging as a framework. Heutagogy emphasizes self-determined learning, departing from traditional pedagogy. The purpose of this study was to explore how a heutagogical framework, applied within a faculty Innovation Fellowship, influenced participants’ development of innovation competencies, self-directed learning behaviors, and overall wellbeing in an academic healthcare setting. This paper focuses on the application of the heutagogy framework within an Innovation Fellowship, focusing on how it fosters innovation, wellbeing, and lifelong learning in healthcare education. We implemented a heutagogical framework by encouraging self-directed exploration of innovation concepts in a self-selected cohort of innovation fellows over the course of five years. A total of 48 fellows have engaged in flexible, collaborative learning. Thematic analysis revealed insights to ‘structure of fluidity’, where fellows highlighted the importance of balancing structured guidance with freedom for self-directed learning. The flexible approach fostered autonomy and creativity in learning. The integration of heutagogical principles enabled fellows to enhance innovation capacity while promoting personal wellbeing. The heutagogy framework shows transformative potential in fostering innovation, wellbeing, and lifelong learning within healthcare education. The ‘structure of fluidity’ underscores the necessity of integrating flexibility and guidance in heutagogical approaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it