Strength-Based Pedagogies in Mathematics Education: “I Like Being Your Little Teacher”
Bibliographic record
Abstract
This article presents a strength-based, cross-age mentorship program where second and sixth-grade students in a multicultural primary school collaborate in mathematics. The sixth-grade students serve as mentors/tutors for the younger students. Drawing on positioning theory and storylines, we have focused on the mentor’s outcome, specifically how the program can help mentors position themselves as mathematics learners. The study presented is a single study based on observations and subsequent interviews with twenty students and their two teachers. The identified storylines suggest that well-structured strength-based cross-age collaboration in mathematics can create learning-focused relationships and learning contexts that enrich mentors (and mentees) both socially and academically. In this strength-based learning environment, mentors are valued for their personal strengths and mathematical proficiency, allowing them to experience a sense of achievement and pride. Keywords: strength-based pedagogies, cross-age collaboration, multicultural mathematics education, positioning theory, mentoring, classroom tensions
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".