Mentoring teachers in the context of student-question-based inquiry: the challenges of the <i>Savanturiers</i> programme
Bibliographic record
Abstract
Student-Question-Based Inquiry (SQBI) represents a form of inquiry where pupils design their research questions, hypotheses, and protocols. The Savanturiers program aims at helping teachers overcome their potential apprehension of SQBI by matching hundreds of elementary and middle school teachers with mentors. These mentors are usually researchers or Ph.D. candidates. This contribution aims at describing various tensions that arose during the mentorship of SQBI in this program through the lens of Engeström’s activity theory. Field observations and semi-structured interviews with a sample of teachers suggested that numerous obstacles hindered mentor-practitioner collaborations. We identified at least three mechanisms that contributed to the paucity of interactions that we interpreted as contradictions of the activity system: mentors sometimes suggested activities that fell outside of the boundaries of curricula or were too time-consuming for a given group of learners. Alternatively, pupils’ agency, notably in the phrasing of research questions, could lead them to explore topics or approaches for which the mentor had little expertise.
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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 | 0.120 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".