The Tension Between Allowing Student Struggle and Providing Support When Teaching Problem-Solving in Primary School Mathematics
Bibliographic record
Abstract
Abstract This article reports two primary school teachers’ perceptions of the effectiveness of lessons based on a problem-solving intervention. The intervention included enabling and extending prompts, independent student struggle time initially and time to share problem-solving strategies at the end. The intervention had two versions: one included whole class prompts and teachers anticipated students’ responses before teaching; the other without these features. Each teacher implemented two lessons in year 1/2 composite classes, with one lesson common. Teachers identified positive impacts of the intervention including providing support for students, extending students’ thinking and providing positive challenge during problem-solving. Struggle time was believed to negatively impact some students’ resilience and confidence; both teachers deviated from the intervention to reduce struggle time. Students used more problem-solving strategies when struggle time was included compared to when the teacher modelled an approach for solving. There was a tension for teachers between providing time for students to struggle and preserving some students’ confidence. One teacher facilitated student share time in the middle of one lesson, allowing students to experience both struggle and success; this compromise could address the tension. Overall, the intervention was perceived to positively impact teaching practice.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".