Challenges in teaching math through problem-solving: reflections on potential actions to support primary school teachers
Bibliographic record
Abstract
For decades, problem-solving has played multiple roles in mathematics education. Among these, teaching through problem solving (TTPS) is widely recognized for promoting authentic mathematical exploration and conceptual understanding. Despite its benefits, TTPS remains rarely implemented in primary classrooms. Why is this the case? What challenges do teachers face, and how can they be addressed? This paper draws on two complementary research projects. The first, involving pedagogical advisors, highlights systemic barriers to TTPS. The second, part of a research and development initiative, focuses on primary teachers’ experiences using TTPS to introduce new mathematical concepts. Data include transcripts from four semi-structured interviews (grades 2–6) and 44 teacher questionnaires. The analysis of quantitative and qualitative data highlighted common challenges experienced by teachers in all grades. A key finding was that facilitating the discussion phase proved to be the most difficult aspect for them.Together, the results provide a nuanced understanding of the difficulties teachers encounter and inform practical recommendations to support the integration of TTPS in primary mathematics classrooms.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".