Promoting Mathematical Knowledge and Skills in a Mathematical Classroom Using a Gallery Walk
Bibliographic record
Abstract
The aim of this paper is to share part of an ongoing study in which we are interested in introducing a Gallery Walk (GW) as an instructional strategy to contemplate in the classroom, in the context of preservice teacher training for elementary education (6-12 years old), to promote students' mathematical knowledge and skills, through problem solving abilities. In this study we intend, in particular, to identify the strategies used by students when solving challenging tasks with multiple approaches, using a GW, as well as characterize their reaction during their engagement in the GW as a teaching and learning strategy. A qualitative and interpretive study, with an exploratory approach, was adopted and the collected data included classroom observations and written productions. The results allowed to identify the strategies used by the participants and to verify the potential of the GW in the quality of the written productions and discussions, which proved to be more effective than in more traditional discussions, allowing to increase the repertoire of solving strategies of each student and communication and collaborative skills; it had a positive effect on the participants’ achievements and it was an enjoyable and rewarding experience for all of them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".