Making as a Window into the Process of Becoming a Teacher
Bibliographic record
Abstract
The process of becoming a mathematics teacher is one that entails the development of an integrated base of teacher knowledge. A considerable body of research has been undertaken to identify the knowledge that is essential to effective mathematics teaching and to determine how it could be developed. In this chapter, we aim to make a contribution to this body of research by accounting for the essential role of the knower’s situated and agentive interactions with the social, material, and conceptual artifacts that mediate such knowledge development. We do so through a revelatory case study of one prospective teacher named “Moira” as she participates in a constructionist Making experience within a specialized mathematics content course for future elementary teachers. Using both cultural-historical activity theory and figured worlds perspectives, we took a novel approach to the analysis of a range of interactions that mediated Moira’s design activity. This analysis of three “moments of becoming” on Moira’s trajectory demonstrates the methodological value of this analytic approach by revealing the blended nature of knowledge and identity that constitutes learning in and through collective social practice. In addition, these findings establish the theoretical value of framing Making as mediated learning and offer empirical evidence of the formative power of the Making experience not only for the development of Moira’s mathematical, pedagogical, and design knowledge, but also for her identity as a mathematics teacher. We conclude the chapter by considering the implications of these findings for teacher preparation coursework and proposing future directions for research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".