<p>Prospective elementary school teachers’ ways of making sense of mathematical problem posing</p>
Bibliographic record
Abstract
The study tackled prospective teachers’ sense-making of mathematical problem posing and the impact of posing different contextual problems on their learning. Focus was on the generation of new problems and reformulation of given problems. Participants were 40 prospective elementary teachers. The findings provide insights into possible ways these teachers could make sense of problem posing of contextual mathematical problems and the learning afforded by posing diverse problems. Highlighted are five perspectives and nine categories of problem posing tasks to support development of proficiency in problem-posing knowledge for teaching. Modos en que futuros profesores de primaria dan sentido a la invención de problemas matemáticos El estudio indagó sobre los modos en que futuros profesores de primaria dan sentido a la invención de problemas matemáticos y el impacto de plantear diferentes problemas contextualizados en su aprendizaje. El foco fue la invención de nuevos problemas y la reformulación de otros dados. Los participantes fueron 40 futuros maestros de primaria. Los resultados proporcionan elementos sobre posibles modos en que estos maestros dan sentido a la invención de problemas matemáticos y el aprendizaje que ofrece plantear diversos problemas. Se destacan cinco perspectivas y nueve categorías de tareas en la invención de problemas para apoyar el desarrollo de la competencia de plantear problemas en la enseñanza.Handle: http://hdl.handle.net/10481/20053Nº de citas en WOS (2017): 6 (Citas de 2º orden, 4)Nº de citas en SCOPUS (2017): 7 (Citas de 2º orden, 7)
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".