Heutagogy for dynamic learning: lessons learned from an Innovation Fellowship
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".