If You Choose Not to Decide: A Survey of Online Field Experiences for Canadian Teacher Preparation Programs
Bibliographic record
Abstract
Despite the rapid growth in online and distance learning in Canada, there does not appear to be much interest on the part of teacher education programs to evolve to meet the needs of future generations of teacher candidates. While understanding the notion that systemic change in tertiary education takes time, the steady growth of online and blended learning in Canada–and globally–combined with raised awareness of distance learning stoked by the COVID-19 pandemic should make educators and policymakers worry about failing to respond to a rapidly changing educational landscape. This paper highlights the status of distance and online field experiences provided by Canadian teacher education programs. In addition, we review program offerings to support in-service teachers, such as graduate certificate, degree, and diploma programs, as well as MOOCs offering free professional development. This study, a replication of a mixed-method study originally conducted in the United States and published as a technical report by Archibald et al. (2020)[1], found that a minority of teacher education programs offered online or blended field experiences. Further, we found that programs were slow to change these deficiencies due to institutional lack of resources, limited knowledge base, perceived lack of usefulness for their teachers’ future careers, and regulatory bodies discouraging online field experiences. This study highlights the dramatic need for programming in distance and online education. [1] This article is original, with some exceptions in the “Results” section.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".