An Organizational Perspective on Experiential Education in Ontario Higher Education
Bibliographic record
Abstract
Experiential education has long been appreciated for its pedagogical value. There is a considerable body of evidence suggesting that students reap particular benefits when ‘learning by doing’ and engaging in practical learning experiences. Though, change is underway. In Ontario, experiential education has been incorporated into strategic funding metrics by the Ministry of Colleges and Universities, and thus subsumed in the neoliberal tradition of quantification and measurement. Universities are now required to ensure all students have at least one experiential learning opportunity before graduation and are required to measure and report where these opportunities are in their programs. This transformation prompts many questions about how organizational change takes place, and how different constituencies within and across Ontario universities are reacting to these changes. Rather than taking a conventional pedagogical view, in this dissertation I analyze experiential education through an organizational lens. I ask questions about the ongoing organizational change, drawing on a variety of organizational theories to capture institutional, organizational, and actor-level perspectives. The first research chapter (Chapter 3) focuses on the field-level dynamics of experiential education in Ontario via the Strategic Mandate process. Using three cohorts of Strategic Mandate Agreements between 2014 and 2025, I uncover how experiential education has evolved over time at the discursive and policy level. The second and third research chapters draw on 132 survey responses from faculty across the province, and 47 interviews from faculty at six Ontario universities. In the second research chapter (Chapter 4), I examine how faculty have experienced the changes to experiential education. This chapter captures recoupling in action and considers how faculty have experienced a changing organizational conception of experiential education. The final research chapter (Chapter 5) draws on the same qualitative sample but takes a more micro-level view of faculty sensemaking, delineating the various lenses through which faculty have made — and continue to make — sense of experiential education. Together, these chapters contribute a gradual narrowing from meso-level dynamics down to micro-level sensemaking to understand how a particular organizational change occurs, instigates responses, and spurs actor-level sensemaking. By taking an organizational approach, I uncover a much more nuanced understanding of experiential education and its relative complexities. My thesis concludes with policy recommendations, and implications for future research.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.019 | 0.037 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".