Implementation and impact of experiential learning in a graduate level teacher education program: An example from a Canadian universit
Bibliographic record
Abstract
Teacher inquiry, in which teachers study their own professional practice, is currently a popular form of experiential learning that is considered a powerful tool to bring about effective change in teaching and learning. Little empirical evidence, however, exists to explain precisely if and how this pedagogical methodology moves teachers toward transformation of practice. Using grounded theory methodology, we examined twelve end of term graduate level learning portfolios and administered a survey to 336 in-service teachers enrolled in a two-year graduate diploma program in the Faculty of Education at Simon Fraser University, Canada. We found powerful evidence that our programs were highly impactful, with 94% of teachers reporting transformative learning within the second year of the program. Using portfolio data we examined the process of the teacher transformations. Our findings revealed that teachers’ abilities to interrogate their subjective-objective stance deepened their experiential learning. Using three case studies we exemplify how transformative pathways were formulated and conclude with a discussion of the implications of learning through experience, including the value of student-generated learning goals, continuous interfacing of theory and practice, seeing your ‘teaching’ through the eyes of your students/colleagues or parents, and the power of living your research question in the context of your own classroom and school setting. We end the paper on a cautionary note pointing out the vulnerability of programs of this nature in an era of accountability, standardization, quality control, and risk management all of which eclipse approaches that focus on authentic practical problems and student generated solutions.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".