Code-Switching Explorations in Teaching Early Number Sense
Bibliographic record
Abstract
New semiotic perspectives about the role of language in mathematics education indicate that teachers have a fundamental role in communicating and teaching the language that carries mathematical meaning. However, little is known about how educators of young children understand and use the language of mathematics. This study addresses this void. Supported by the understanding that mathematics has its own language (Pimm, 1987), the study focuses on code switching—the mixing of words from two languages—by educators as they shift between the language of instruction and the language of mathematics. A qualitative multiple case study approach utilizing discourse analysis was used to explore three early years teachers’ math talk. Findings indicate that these educators code-switched to the mathematics register when they talked about numbers, number words and counting, to revoice students’ ideas, to explain students’ and teachers’ actions, to provide new math information, and when they chose between two terms that belonged to the math register. Findings also demonstrated that educators preferred to avoid the use of the mathematics’ register and relied instead on what the educators called “familiar language.” Findings further indicated the presence of semantic patterns between perceptual terms and the mathematics register.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".