Learning from Faculty Mentors Who Had to Mentor and Evaluate Teacher Candidates Completing a Remote Practicum in the Early Stages of the COVID-19 Pandemic in Canada
Bibliographic record
Abstract
In the Spring of 2020, the COVID-19 global pandemic impacted all aspects of life throughout the world, including education. Teachers who had never taught online before, all of a sudden had one week to get ready to engage with their students in a virtual setting. On top of these changes, our small post-degree Canadian teacher education program had teacher candidates on practicum in K-12 schools. That meant our faculty mentors, responsible for recommending teacher candidates for certification, had to figure out how to mentor, support, and evaluate teacher candidates who were teaching remotely. This research aimed to address the following two questions: a) What were these faculty mentors’ experiences when having to move mentoring of teacher candidates on a remote practicum? and b) What recommendations do these faculty mentors have for teacher education programs trying to support faculty mentors having to mentor teacher candidates who are teaching remotely? Results illustrate challenges with workload, anxiety, screen time, teacher mentors limiting teacher candidate opportunities, and figuring out how to evaluate certification readiness.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".