Bibliographic record
Abstract
This book is grounded in the author’s experiences of teaching mathematics for prospective elementary school teachers and conducting research on their understanding of mathematical concepts. It is a reflection on practice and an attempt to cope with a double challenge: that of a teacher, in helping prospective teachers make sense of mathematics, and that of a researcher, in an attempt to understand and describe the challenges faced by students. This work fits within the current community interest on teacher education and provides a novel focus, with both theoretical and practical considerations. The central claim in this book is that encounters with mathematical content by prospective elementary school teachers constitute relearning, rather than learning, of mathematics. The specific focus is on topics related to elementary number theory (e.g. divisibility, prime factorization), which is referred to as a “forgotten queen” (following Gauss’ reference to number theory as a queen of mathematics). This is the content area that has not received significant attention in mathematics education research. The book can be summarized as an attempt to address the following questions: What is relearning of mathematical content and how is it similar to or different from learning? What are the examples of specific mathematical topics or concepts that require relearning? What pedagogical approaches can support relearning? The detailed analysis of research data and pedagogical approaches presented in the book are intertwined with stories of personal experiences of the author, which makes the reading not only intellectually stimulating but also enjoyable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".