<i>‘We can help when you lose your way.’</i> High-school students’ reflections on pre-service teachers and the teaching practicum
Bibliographic record
Abstract
Classroom students are stakeholders in initial teacher education given that many preparation programs include field-based practice in classroom settings as a certification requirement. Owing to their frequent contact with pre-service teachers (PSTs) during practicum periods, students are well positioned to contribute to PSTs’ learn-to-teach experiences. Practicum scholarship, however, commonly positions classroom students as passive participants, or bystanders, tending to overlook their learning relationships with PSTs. Utilizing group interviews with students from three high schools in Québec (Canada), this qualitative study asked the following: How do high-school students experience practice teaching? How might students’ experiences with practice teaching inform PSTs’ professional learning? The findings highlight ways PSTs and practice teaching have the potential to both enhance and impede students’ learning and classroom experiences. Students widely acknowledged that they have an important role to play in supporting the professional learning of PSTs. Drawing on the theory of practice architectures, the student testimonies shared in this study incite initial teacher education programs to develop contemporary models of practicum mentorship that support reciprocal learning relationships between PSTs and their students.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".