Richesse des problèmes posés et créativité des solutions soumises dans la Communauté d'apprentissages scientifiques et mathématiques interactifs (CASMI)
Bibliographic record
Abstract
<p><strong>Résumé</strong></p><p>Cette étude s’intéresse à la richesse des problèmes mathématiques posés et à la créativité des solutions soumises par les membres de la Communauté d’apprentissages scientifiques et mathématiques interactifs (CASMI), une ressource virtuelle destinée aux élèves francophones du Nouveau-Brunswick et d’ailleurs. L’exploration des problématiques identifiées par les chercheurs préoccupés par les rares occasions qu’ont les élèves de résoudre des problèmes riches et de développer leur créativité en classe nous amène à construire un cadre conceptuel afin 1) d’analyser la richesse des problèmes proposés dans la CASMI, 2) d’évaluer la créativité des solutions soumises par les membres de cette communauté virtuelle et 3) de déterminer s’il existe une relation entre la richesse des problèmes posés et la créativité des solutions soumises. Les résultats révèlent que les problèmes plus riches semblent susciter plus de solutions originales et des réponses divergentes. Cependant, ces résultats mettent aussi en évidence le besoin d’élargir le cadre conceptuel sous-jacent à la formulation des problèmes mathématiques riches offerts aux élèves et de mener des recherches plus approfondies dans ce domaine.</p><p><strong>Abstract</strong></p><p>This research focuses on the richness of mathematical problems posted and the creativity of the solutions submitted by members of the CASMI (Communauté d’apprentissages scientifiques et mathématiques interactifs), a virtual resource used by Francophone students from New Brunswick and elsewhere. After reviewing issues identified by researchers preoccupied by the few opportunities offered to students to solve rich mathematical problems and develop their creativity in the classroom, we develop a conceptual framework in order to : 1) analyze the richness of the mathematical problems posted on the CASMI website; 2) assess the creativity of the solutions to the problems submitted on this website; and 3) verify the link between the richness of the problems and the mathematical creativity of the solutions. Our results suggest that rich mathematical problems bring more original solutions and multiple answers. These results also reveal the need for a broader conceptual framework in order to enhance the richness of mathematical problems offered to students, as well as for continuing research in this area.</p>
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".