Do Secondary School Students’ Strategies in Solving Permutation and Combination Problems Change with Instruction?
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Bibliographic record
Abstract
Abstract This work is part of an investigation conducted in Italy, which aims to explore the effects of instruction on secondary school students’ combinatorial reasoning. We gave a questionnaire adapted from Navarro-Pelayo’s research to two groups of students with and without instruction on combinatorics in order to analyse the students’ performances and the strategies used in their solutions, as well as the effect of instruction on the same. We present the results obtained in two permutation and two combination problems (each in the distribution and selection models). Permutation problems were found easier than combination problems, selection problems were found easier after instruction, and the instruction group obtained better results. We found differences in the main strategies used in both groups: enumeration and dividing a problem in parts was more common in the no-instruction group. The instruction group frequently relied on the use of a formula and the product rule.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it