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 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 | 0.024 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".