Exploring the Unknown Professional: A Narrative Analysis of the Identity of Ontario College Educational Developers
Bibliographic record
Abstract
Educational Developers have become increasingly important in higher education, providing essential teaching support and driving educational innovation. However, little is known about how they navigate their professional identity, with this knowledge gap being particularly significant in the Ontario College context where there is a scarcity of research on Ontario College Educational Developers (OCEDs). To address this gap, this study explores the professional identity of OCEDs through their narratives, focusing on their experiences and perceptions of the role. The overarching research question guiding this study is: How do Ontario College Educational Developers (OCEDs) experience their professional identity? Specifically, the study examines how OCEDs perceive different aspects of their roles, how they position their professional identity in relation to key stakeholders, as well as the challenges that undermine their sense of professional identity and the supports that affirm it. Using a narrative methodology, in-depth interviews were conducted with twelve participants across Ontario. The transcribed narratives were analyzed through the lens of Identity Theory, revealing core aspects of the OCED professional identity as well as the tensions OCEDs face between their personal goals and institutional expectations. Findings reveal that OCEDs possess a professional identity grounded in faculty-oriented learning support and collaborative leadership. Eight interconnected identity themes were determined: being self-taught, diplomatic, altruistic, faculty-oriented, emergent leaders focused on learning support, along with experiencing a sense of conflict and of being outsiders. Together, these themes illustrate both the coherence and contradictions present in the role. While participants reported emotional strain, marginality, and tension, they also found affirmation through recognition and through engagement with external professional communities and networks. The results can be used to further support those in this role, inform interactions others have with OCEDs, and guide employers and institutions in hiring and using OCEDs more effectively. This research also contributes to the growing body of scholarship on educational development in higher education.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".