The Tableau ST Project: Inspiring Francophone Teachers with Effective Science Practices
Bibliographic record
Abstract
The Social Sciences and Humanities Research Council (SSHRC)-funded Tableau ST project involved two years of collaborative fieldwork with 19 francophone teachers and resulted in a repertoire of 53 effective science lessons and projects. To disseminate these resources, we designed an innovative, free-access website (http://www.tableaust.ca) that provides ready-to-use lessons to elementary francophone teachers seeking to improve their teaching. For teachers, the website offers valuable and empowering tools to educate the science-literate citizens of the future. For scholars, the project, inspired by the Heart-Hands-Head model, sheds new light on the definition of what constitutes effective science-teaching practices. The collaboration with the teachers has also led to identifying three new criteria for effective practices. Résumé Le projet Tableau ST, relié au site http://www.tableaust.ca, est né d’une recherche participative qui a réuni chercheures et enseignants francophones pendant 2 ans, dans le but de disséminer plus d’une cinquantaine de pratiques gagnantes en sciences à l’élémentaire. Plusieurs critères contribuent à identifier les pratiques efficaces et parmi eux, l’apprentissage actif, le questionnement et le partage et la confrontation des preuves témoignent de la réflexivité des enseignants sur leurs pratiques en classe. Les critères gagnants agencés selon le modèle conceptuel Cœur-Mains-Tête pourront éventuellement servir à guider la pratique enseignante, mais aussi la formation des enseignants en didactique des sciences. Keywords: Elementary science and technology education; Effective science teaching; Tableau ST; Canada and Francophonie. Mots-clés: Enseignement des sciences et technologies à l’élémentaire; Pratiques gagnantes en sciences; Tableau ST; Canada et Francophonie.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".