Bridging the Theory/Practice Divide: Experiential Learning for a Critical, People-Centred Economy
Bibliographic record
Abstract
This report provides an overview and analysis of the current understanding of how “experiential learning” is conceptualized, implemented and evaluated in professional service fields of study. Better understanding of this educational approach will benefit educators as well as students. Experiential learning is an integral part of the authors’ institutional culture: 90% of all undergraduate programs include an experiential learning component (Learning and Teaching Office, Ryerson University, 2015). Experiential learning is also rapidly expanding in other Ontario universities (Council of Ontario Universities, 2014). Despite its prevalent use, the field of experiential learning remains under-researched and the research that has been done is fragmented. There is a lack of evidence to support the extent to which this type of learning bridges the gap between theory and practice, broadens career prospects, and contributes to the development of students’ critical thinking skills. This report focuses on the nine professional fields associated with the Faculty of Community Services, Ryerson University: Child and Youth Care, Disability Studies, Early Childhood Studies, Midwifery, Nursing, Nutrition, Public and Occupational Health, Regional and Urban Planning and Social work. (Executive Summary, page 3)
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".